1
|
Guo C, Luo J, Liang M, Xiao J. Correlation of 18F-FDG PET/CT metabolic parameters with Ki-67 expression and tumor staging in nasopharyngeal carcinoma. Nucl Med Commun 2025:00006231-990000000-00400. [PMID: 39967464 DOI: 10.1097/mnm.0000000000001966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2025]
Abstract
PURPOSE The study aimed to investigate the imaging parameters of 18F-fluorodeoxyglucose (18F-FDG) PET/computed tomography (PET/CT) in nasopharyngeal carcinoma (NPC), specifically examining the relationship between mean standardized uptake value (SUVmean), maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) with Ki-67 expression, T-stage, and tumor node metastasis (TNM) stage. METHODS A retrospective analysis was conducted on 143 consecutive NPC patients from January 2015 to December 2023 who underwent 18F-FDG PET/CT for initial disease assessment. SUVmax, SUVmean, MTV, and TLG were quantified from PET/CT images. Immunohistochemical staining was used to assess Ki-67 protein expression. Correlations between 18F-FDG PET/CT metabolic parameters, Ki-67 expression, T-stage, and TNM-stage were evaluated using statistical methods, with significance set at P < 0.05. RESULTS All primary NPC lesions demonstrated elevated 18F-FDG uptake. Significant positive correlations were observed between SUVmax (r = 0.234, P = 0.005), SUVmean (r = 0.223, P = 0.007), MTV (r = 0.218, P = 0.009), and TLG (r = 0.232, P = 0.005) with Ki-67 labeling index. The univariate analysis indicated that all the parameters (SUVmax, SUVmean, MTV, and TLG) in the group with Ki-67 ≥ 50% were significantly higher than those in the group with Ki-67 < 50% (P = 0.001). Additionally, binary logistic regression analysis revealed that SUVmax was an independent risk factor for the group with Ki-67 ≥ 50% (P = 0.003). The univariate analysis revealed that all parameters (SUVmax, SUVmean, MTV, and TLG) in the T3-4 group and clinical stage IV group were significantly higher than those in the T1-2 group and stages I-III group (P both <0.05), respectively. Furthermore, binary logistic regression analysis demonstrated that MTV was an independent risk factor for both comparisons (P both <0.05). CONCLUSION The metabolic parameters derived from 18F-FDG PET/CT in NPC indirectly reflect tumor biological behavior, suggesting their potential utility in guiding individualized comprehensive treatment strategies.
Collapse
Affiliation(s)
| | - JunJia Luo
- Radiology Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | | | | |
Collapse
|
2
|
Xie H, Tan T, Li Q, Li T. Revolutionizing HER-2 assessment: multidimensional radiomics in breast cancer diagnosis. BMC Cancer 2025; 25:265. [PMID: 39953417 PMCID: PMC11829378 DOI: 10.1186/s12885-025-13549-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2024] [Accepted: 01/17/2025] [Indexed: 02/17/2025] Open
Abstract
OBJECTIVE To explore the application value of multidimensional radiomics based on ultrasound imaging in assessing the HER-2 status of breast cancer. METHODS We retrospectively analyzed the ultrasound imaging, clinical, and laboratory data of 850 breast cancer patients from two centers. During the study, we first utilized automation technology to accurately delineate the tumor region of interest (ROI) in breast ultrasound imaging. Subsequently, the intra-tumoral ROI was automatically expanded by 1 cm and 2 cm to obtain larger areas including the peritumoral tissues, and further generated three-dimensional volumes of interest (VOI) within and around the tumor. Through the K-means clustering method, we identified the sub-regions of interest within the ROI and extracted corresponding radiomic features using the pyradiomics toolkit. Additionally, we employed an advanced Vision Transformer (VIT) model to perform deep radiomic feature extraction on the ROI. Based on feature selection, we utilized various machine learning algorithms for modeling and analysis to assess the HER-2 status of breast cancer. RESULTS After comprehensive comparison and evaluation of multiple models, we found that the diagnostic model based on multidimensional feature fusion exhibited excellent diagnostic performance in assessing the HER-2 status of breast cancer. In the training set, the model achieved an accuracy of 0.949 and an AUC value of 0.990 (95% CI: 0.986-0.995), with outstanding key performance indicators such as sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. The model showed good generalization in the test set, with accuracy 0.747, AUC 0.848 (95% CI: 0.791-0.904), and sensitivity 0.911. Specificity was slightly lower, but other indicators remained high, and the F1 score was 0.703. Calibration and clinical decision curves further confirmed the model's effectiveness and reliability. CONCLUSION This study fully demonstrates that multidimensional breast ultrasonography-based radiomic features can effectively assess the HER-2 status of breast cancer. This finding not only provides new evidence for early diagnosis of breast cancer but also offers new ideas and methods for personalized treatment planning and prognosis assessment.
Collapse
Affiliation(s)
- Hui Xie
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, P. R. China
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, P. R. China
| | - Tao Tan
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, P. R. China
| | - Qing Li
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, P. R. China
- Key Experimental Project of Higher Education Institutes in Hunan Province (Key Laboratory of Tumor Precision Medicine), Chenzhou, 423000, P. R. China
- College of Medical Imaging, Laboratory Diagnostics, and Rehabilitation, Xiangnan University, Chenzhou, 423000, P. R. China
| | - Tao Li
- College of Medical Imaging, Laboratory Diagnostics, and Rehabilitation, Xiangnan University, Chenzhou, 423000, P. R. China.
- Department of Medical, Xiangnan University, Chenzhou, 423000, P. R. China.
| |
Collapse
|
3
|
Wang X, Xie Z, Wang X, Song Y, Suo S, Ren Y, Hu W, Zhu Y, Cao M, Zhou Y. Preoperative prediction of IDH genotypes and prognosis in adult-type diffuse gliomas: intratumor heterogeneity habitat analysis using dynamic contrast-enhanced MRI and diffusion-weighted imaging. Cancer Imaging 2025; 25:11. [PMID: 39923105 PMCID: PMC11807326 DOI: 10.1186/s40644-025-00829-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Accepted: 01/27/2025] [Indexed: 02/10/2025] Open
Abstract
BACKGROUND Intratumor heterogeneity (ITH) is a key biological characteristic of gliomas. This study aimed to characterize ITH in adult-type diffuse gliomas and assess the feasibility of using habitat imaging based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) to preoperatively predict isocitrate dehydrogenase (IDH) genotypes and prognosis. METHODS Sixty-three adult-type diffuse gliomas with known IDH genotypes were enrolled. Volume transfer constant (Ktrans) and apparent diffusion coefficient (ADC) maps were acquired from DCE-MRI and DWI, respectively. After tumor segmentation, the k-means algorithm clustered Ktrans and ADC image voxels to generate spatial habitats and extract quantitative image features. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to evaluate IDH predictive performance. Multivariable logistic regression models were constructed and validated using leave-one-out cross-validation, and the contrast-enhanced subgroup was analyzed independently. Kaplan-Meier and Cox proportional hazards regression analyses were used to investigate the relationship between tumor habitats and progression-free survival (PFS) in the two IDH groups. RESULTS Three habitats were identified: Habitat 1 (hypo-vasopermeability and hyper-cellularity), Habitat 2 (hypo-vasopermeability and hypo-cellularity), and Habitat 3 (hyper-vasopermeability). Compared to the IDH wild-type group, the IDH mutant group exhibited lower mean Ktrans values in Habitats 1 and 2 (both P < 0.001), higher volume (P < 0.05) and volume percentage (pVol, P < 0.01) of Habitat 2, and lower volume and pVol of Habitat 3 (both P < 0.001). The optimal logistic regression model for IDH prediction yielded an AUC of 0.940 (95% confidence interval [CI]: 0.880-1.000), which improved to 0.948 (95% CI: 0.890-1.000) after cross-validation. Habitat 2 contributed the most to the model, consistent with the findings in the contrast-enhanced subgroup. In IDH wild-type group, pVol of Habitat 2 was identified as a significant risk factor for PFS (high- vs. low-pVol subgroup, hazard ratio = 2.204, 95% CI: 1.061-4.580, P = 0.034), with a value below 0.26 indicating a 5-month median survival benefit. CONCLUSIONS Habitat imaging employing DCE-MRI and DWI may facilitate the characterization of ITH in adult-type diffuse gliomas and serve as a valuable adjunct in the preoperative prediction of IDH genotypes and prognosis. CLINICAL TRIAL NUMBER Not applicable.
Collapse
Affiliation(s)
- Xingrui Wang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Zhenhui Xie
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Xiaoqing Wang
- Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yang Song
- MR Research Collaboration Team, Siemens Healthineers Ltd, Shanghai, 200126, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Wentao Hu
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yi Zhu
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Mengqiu Cao
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| |
Collapse
|
4
|
Wang Z, Wang L, Wang Y. Radiomics in glioma: emerging trends and challenges. Ann Clin Transl Neurol 2025. [PMID: 39901654 DOI: 10.1002/acn3.52306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Revised: 12/11/2024] [Accepted: 12/31/2024] [Indexed: 02/05/2025] Open
Abstract
Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion-weighted imaging, perfusion-weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune-related biomarkers and characterizing immune responses within tumors. Integrating multi-omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi-omics data integration, is crucial for the clinical translation of radiomics in glioma.
Collapse
Affiliation(s)
- Zihan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Guiqian International General Hospital, Guiyang, Guizhou, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
5
|
Tian R, Duan X, Xing F, Zhao Y, Liu C, Li H, Kong N, Cao R, Guan H, Li Y, Li X, Zhang J, Wang K, Yang P, Wang C. Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty. Int J Comput Assist Radiol Surg 2025; 20:237-248. [PMID: 38836956 DOI: 10.1007/s11548-024-03192-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/16/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE After robotic-assisted total knee arthroplasty (RA-TKA) surgery, some patients still experience joint discomfort. We aimed to establish an effective machine learning model that integrates radiomic features extracted from computed tomography (CT) scans and relevant clinical information to predict patient satisfaction three months postoperatively following RA-TKA. MATERIALS AND METHODS After careful selection, data from 142 patients were randomly divided into a training set (n = 99) and a test set (n = 43), approximately in a 7:3 ratio. A total of 1329 radiomic features were extracted from the regions of interest delineated in CT scans. The features were standardized using normalization algorithms, and the least absolute shrinkage and selection operator regression model was employed to select radiomic features with ICC > 0.75 and P < 0.05, generating the Rad-score as feature markers. Univariate and multivariate logistic regression was then used to screen clinical information (age, body mass index, operation time, gender, surgical side, comorbidities, preoperative KSS score, preoperative range of motion (ROM), preoperative and postoperative HKA angle, preoperative and postoperative VAS score) as potential predictive factors. The satisfaction scale ≥ 20 indicates patient satisfaction. Finally, three prediction models were established, focusing on radiomic features, clinical features, and their fusion. Model performance was evaluated using Receiver Operating Characteristic curves and decision curve analysis. RESULTS In the training set, the area under the curve (AUC) of the clinical model was 0.793 (95% CI 0.681-0.906), the radiomic model was 0.854 (95% CI 0.743-0.964), and the combined radiomic-clinical model was 0.899 (95% CI 0.804-0.995). In the test set, the AUC of the clinical model was 0.908 (95% CI 0.814-1.000), the radiomic model was 0.709 (95% CI 0.541-0.878), and the combined radiomic-clinical model was 0.928 (95% CI 0.842-1.000). The AUC of the radiomic-clinical model was significantly higher than the other two models. The decision curve analysis indicated its clinical application value. CONCLUSION We developed a radiomic-based nomogram model using CT imaging to predict the satisfaction of RA-TKA patients at 3 months postoperatively. This model integrated clinical and radiomic features and demonstrated good predictive performance and excellent clinical application potential.
Collapse
Affiliation(s)
- Run Tian
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xudong Duan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Fangze Xing
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiwei Zhao
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - ChengYan Liu
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Heng Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ning Kong
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ruomu Cao
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Huanshuai Guan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiyang Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinghua Li
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jiewen Zhang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Kunzheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Pei Yang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Chunsheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| |
Collapse
|
6
|
Zhang Y, Ma H, Lei P, Li Z, Yan Z, Wang X. Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT. Front Oncol 2025; 14:1522501. [PMID: 39830646 PMCID: PMC11739309 DOI: 10.3389/fonc.2024.1522501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Accepted: 12/10/2024] [Indexed: 01/22/2025] Open
Abstract
Aim To develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma. Methods A retrospective cohort study was conducted to collect data on 344 patients who underwent liver resection for HCC. The internal subregion of the tumor was objectively delineated and the clinical features were also analyzed to construct clinical models. Radiomics feature extraction was performed on tumor subregions of arterial and portal venous phase images. Machine learning classification models were constructed as a fusion model combining the three different models, and the models were assessed. Results A comprehensive retrospective analysis was conducted on a cohort of 344 patients who underwent hepatic cancer resection at one of the two centers. it was found that the combined SVM model yielded superior results after comparing various metrics, such as the AUC, accuracy, sensitivity, specificity, and DCA. Conclusions Habitat analysis of sequential CT images can delineate distinct subregions within a tumor, offering valuable insights for early prediction of postoperative HCC recurrence.
Collapse
Affiliation(s)
- Yubo Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, China
- School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Hongyan Ma
- School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Peng Lei
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Zhiyuan Li
- School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Zhao Yan
- School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xinqing Wang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, China
| |
Collapse
|
7
|
Guo Y, Li T, Gong B, Hu Y, Wang S, Yang L, Zheng C. From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2408069. [PMID: 39535476 PMCID: PMC11727298 DOI: 10.1002/advs.202408069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 10/19/2024] [Indexed: 11/16/2024]
Abstract
With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.
Collapse
Affiliation(s)
- Yusheng Guo
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| | - Tianxiang Li
- Department of UltrasoundState Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical. SciencesPeking Union Medical CollegeBeijing100730China
| | - Bingxin Gong
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| | - Yan Hu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen518055China
| | - Sichen Wang
- School of Life Science and TechnologyComputational Biology Research CenterHarbin Institute of TechnologyHarbin150001China
| | - Lian Yang
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| | - Chuansheng Zheng
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
- Hubei Key Laboratory of Molecular ImagingWuhan430022China
| |
Collapse
|
8
|
Yang YC, Wu JJ, Shi F, Ren QG, Jiang QJ, Guan S, Tang XQ, Meng XS. Sub-regional Radiomics Analysis for Predicting Metastasis Risk in Clear Cell Renal Cell Carcinoma: A Multicenter Retrospective Study. Acad Radiol 2025; 32:237-249. [PMID: 39147643 DOI: 10.1016/j.acra.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 08/01/2024] [Accepted: 08/03/2024] [Indexed: 08/17/2024]
Abstract
RATIONALE AND OBJECTIVES Clear cell renal cell carcinoma (ccRCC) is the most common malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic instances. Elucidating the composition of ccRCC holds promise for the discovery of highly sensitive biomarkers. Our objective was to utilize habitat imaging techniques and integrate multimodal data to precisely predict the risk of metastasis, ultimately enabling early intervention and enhancing patient survival rates. MATERIAL AND METHODS A retrospective analysis was performed on a cohort of 263 patients with ccRCC from three hospitals between April 2013 and March 2021. Preoperative CT images, ultrasound images, and clinical data were comprehensively analyzed. Patients from two campuses of Qilu Hospital of Shandong University were assigned to the training dataset, while the third hospital served as the independent testing dataset. A robust consensus clustering method was used to classify the primary tumor space into distinct sub-regions (i.e., habitats) using contrast-enhanced CT images. Radiomic features were extracted from these tumor sub-regions and subsequently reduced to identify meaningful features for constructing a predictive model for ccRCC metastasis risk assessment. In addition, the potential value of radiomics in predicting ccRCC metastasis risk was explored by integrating ultrasound image features and clinical data to construct and compare alternative models. RESULTS In this study, we performed k-means clustering within the tumor region to generate three distinct tumor subregions. We quantified the Hounsfiled Unit (HU) value, volume fraction, and distribution of high- and low-risk groups in each subregion. Our investigation focused on 252 patients with Habitat1 + Habitat3 to assess the discriminative power of these two subregions. We then developed a risk prediction model for ccRCC metastasis risk classification based on radiomic features extracted from CT and ultrasound images, and clinical data. The Combined model and the CT_Habitat3 model showed AUC values of 0.935 [95%CI: 0.902-0.968] and 0.934 [95%CI: 0.902-0.966], respectively, in the training dataset, while in the independent testing dataset, they achieved AUC values of 0.891 [95%CI: 0.794-0.988] and 0.903 [95%CI: 0.819-0.987], respectively. CONCLUSION We have identified a non-invasive imaging predictor and the proposed sub-regional radiomics model can accurately predict the risk of metastasis in ccRCC. This predictive tool has potential for clinical application to refine individualized treatment strategies for patients with ccRCC.
Collapse
Affiliation(s)
- You Chang Yang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Jiao Jiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Qing Guo Ren
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Qing Jun Jiang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Shuai Guan
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Xiao Qiang Tang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Xiang Shui Meng
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| |
Collapse
|
9
|
Miniere HJM, Lima EABF, Lorenzo G, Hormuth II DA, Ty S, Brock A, Yankeelov TE. A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin. Cancer Biol Ther 2024; 25:2321769. [PMID: 38411436 PMCID: PMC11057790 DOI: 10.1080/15384047.2024.2321769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 02/18/2024] [Indexed: 02/28/2024] Open
Abstract
Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.
Collapse
Affiliation(s)
- Hugo J. M. Miniere
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USA
- Department of Civil Engineering and Architecture, University of Pavia, Lombardy, Italy
| | - David A. Hormuth II
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA
| | - Sophia Ty
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, USA
- Department of Oncology, The University of Texas at Austin, Austin, USA
- Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, USA
| |
Collapse
|
10
|
Song D, Fan G, Chang M. Research Progress on Glioma Microenvironment and Invasiveness Utilizing Advanced Multi-Parametric Quantitative MRI. Cancers (Basel) 2024; 17:74. [PMID: 39796702 PMCID: PMC11719598 DOI: 10.3390/cancers17010074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Revised: 11/28/2024] [Accepted: 12/23/2024] [Indexed: 01/13/2025] Open
Abstract
Magnetic resonance imaging (MRI) currently serves as the primary diagnostic method for glioma detection and monitoring. The integration of neurosurgery, radiation therapy, pathology, and radiology in a multi-disciplinary approach has significantly advanced its diagnosis and treatment. However, the prognosis remains unfavorable due to treatment resistance, inconsistent response rates, and high recurrence rates after surgery. These factors are closely associated with the complex molecular characteristics of the tumors, the internal heterogeneity, and the relevant external microenvironment. The complete removal of gliomas presents challenges due to their infiltrative growth pattern along the white matter fibers and perivascular space. Therefore, it is crucial to comprehensively understand the molecular features of gliomas and analyze the internal tumor heterogeneity in order to accurately characterize and quantify the tumor invasion range. The multi-parameter quantitative MRI technique provides an opportunity to investigate the microenvironment and aggressiveness of glioma tumors at the cellular, blood perfusion, and cerebrovascular response levels. Therefore, this review examines the current applications of advanced multi-parameter quantitative MRI in glioma research and explores the prospects for future development.
Collapse
Affiliation(s)
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang 110001, China;
| | - Miao Chang
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang 110001, China;
| |
Collapse
|
11
|
Wang B, Guo H, Zhang M, Huang Y, Duan L, Huang C, Xu J, Wang H. Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study. Front Oncol 2024; 14:1433196. [PMID: 39723369 PMCID: PMC11668965 DOI: 10.3389/fonc.2024.1433196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 11/22/2024] [Indexed: 12/28/2024] Open
Abstract
Background Accurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS). Purpose To construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral habitats (subregions of clusters of voxels containing similar features) and peritumoral features for the preoperative prediction of the pathological grade of STS. Methods The MRI data of 145 patients with STS (74 low-grade and 71 high-grade) from 4 hospitals were retrospectively collected, including enhanced T1-weighted and fat-suppressed-T2-weighted sequences. The patients were divided into training cohort (n = 102) and validation cohort (n = 43). K-means clustering was used to divide intratumoral voxels into three habitats according to signal intensity. A number of radiomics features were extracted from tumor-related regions to construct radiomics prediction signatures for seven subgroups. Logistic regression analysis identified peritumoral edema as an independent risk factor. A nomogram was created by merging the best radiomics signature with the peritumoral edema. We evaluated the performance and clinical value of the model using area under the curve (AUC), calibration curves, and decision curve analysis. Results A multi-layer perceptron classifier model based on intratumoral habitats and peritumoral features combined gave the best radiomics signature, with an AUC of 0.856 for the validation cohort. The AUC of the nomogram in the validation cohort was 0.868, which was superior to the radiomics signature and the clinical model established by peritumoral edema. The calibration curves and decision curve analyses revealed good calibration and a high clinical application value for this nomogram. Conclusion The MRI-based nomogram is accurate and effective for predicting preoperative grading in patients with STS.
Collapse
Affiliation(s)
- Bo Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Hongwei Guo
- Department of Operation Center, Women and Children’s Hospital, Qingdao University, Qingdao, Shandong, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, Henan, China
| | - Lisha Duan
- Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Chencui Huang
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise and League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Jun Xu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
12
|
Tavakkoli MB, Abedi I, Abdollahi H, Amouheidari A, Azmoonfar R, Saber K, Hassaninejad H. Comparison prediction models of bladder toxicity based on radiomic features of CT and MRI in patients with prostate cancer undergoing radiotherapy. J Med Imaging Radiat Sci 2024; 55:101765. [PMID: 39306942 DOI: 10.1016/j.jmir.2024.101765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/19/2024] [Accepted: 09/04/2024] [Indexed: 12/02/2024]
Abstract
PURPOSE This study aimed to assess the radiomic features of computed tomography (CT) and magnetic resonance imaging (MRI) of the bladder wall before radiotherapy using machine learning (ML) methods to predict bladder radiotoxicity in patients with prostate cancer. METHODS This study enrolled 70 patients with pathologically confirmed prostate cancer who were candidates for radiation therapy (RT). CT and MRI of the bladder wall before radiotherapy were used to extract radiomic features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Algorithms such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) have been used to develop models based on radiomic, dosimetry, and clinical parameters. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and accuracy were used to analyze the predictive power of all models. RESULTS The RF and LR models based on the radiomic features of MRI and clinical/dosimetry parameters with an AUC of 0.95 and 0.93, and an accuracy of 86% and 86%, respectively, had the highest performance in the prediction of bladder radiation toxicity. CONCLUSIONS This study showed that, firstly, CT and MRI radiomic features of the bladder wall before treatment could be used to predict bladder radiotoxicity. Second, MRI is better than CT in predicting bladder toxicity caused by radiation. And thirdly, the performance of the predictive models based on the combination of radiomic, clinical, and dosimetry characteristics was improved.
Collapse
Affiliation(s)
- Mohammad Bagher Tavakkoli
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Iraj Abedi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hamid Abdollahi
- Department of Radiology Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran; Department of Radiology, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | | | - Rasool Azmoonfar
- Department of Radiology, School of Allied Medical Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Korosh Saber
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Hassaninejad
- Department of Radiology, Faculty of Paramedical, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
| |
Collapse
|
13
|
Jin W, Wang S, Wang T, Zhang D, Wang Y, Zhang G. Multi-machine Learning Model Based on Habitat Subregions for Outcome Prediction in Adenomyosis Treated by Uterine Artery Embolization. Acad Radiol 2024; 31:4985-4995. [PMID: 38845295 DOI: 10.1016/j.acra.2024.05.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 11/30/2024]
Abstract
RATIONALE AND OBJECTIVES To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions. MATERIALS AND METHODS Patients who underwent UAE for adenomyosis at institution A between November 2015 and June 2018 were included in the training cohort and those at institution B between June 2017 and June 2019 were included in the test cohort. The regions of interest (ROI) were manually segmented on the T2-weighted images (T2WI). The ROIs were subsequently partitioned into habitat subregions using k-means clustering. Radiomic features were extracted from each subregion on T1WI, T2WI, apparent diffusion coefficient, and contrast-enhanced images. The least absolute shrinkage and selection operator (LASSO) was used to select the subregion radiomics features. With the improvement in patients' symptoms at 36 months post-UAE, the habitat subregion features were trained using six machine-learning classifiers. The most suitable classifier was chosen based on model performance to establish the habitat radiomics model (HRM). The efficacy of the model was validated using both the training and test cohorts. Finally, a whole-region radiomics model (WRM) and clinical model (CM) were established. The Delong test was used to compare the predictive performance of the habitat subregion model and the two other models. RESULTS The study included 258 patients, 191 in the training cohort and 67 in the test cohort. The ROIs were divided into four habitat subregions. Radiomics features were extracted from different sequence images of the subregions. After LASSO regression, 24 habitat subregion features were included in the model. Based on the receiver operating characteristic curve analysis, the area under the curve (AUC) of the HRM was 0.921 (95% CI, 0.857-0.985, training) and 0.890 (95% CI, 0.736-1.000, test). The AUCs for the WRM were 0.805 (95% CI, 0.737-0.872, training) and 0.693 (95% CI, 0.497-0.889, test). Compared to the HRM, the difference in predictive performance was statistically significant (p = 0.008, training; p = 0.007, test). The AUCs for the CM were 0.788 (95% CI, 0.711-0.866, training) and 0.735 (95% CI, 0.566-0.903, test). Compared to the HRM, there was a statistically significant difference in the training cohort (p = 0.014) but not in the test cohort (p = 0.186). CONCLUSION The HRM can predict the long-term efficacy of UAE in the treatment of adenomyosis. The predictive performance was superior to that of both the WRM and CM, serving as an effective tool to assist interventional physicians in clinical decision-making.
Collapse
Affiliation(s)
- Wentao Jin
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, P R China
| | - Shijia Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, P R China
| | - Tianpin Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, P R China
| | - Di Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, P R China
| | - Yitang Wang
- Department of Interventional Radiology, DaLian Women and Children's Medical Group, P R China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, P R China.
| |
Collapse
|
14
|
Xu C, Wang Z, Wang A, Zheng Y, Song Y, Wang C, Yang G, Ma M, He M. Breast Cancer: Multi-b-Value Diffusion Weighted Habitat Imaging in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy. Acad Radiol 2024; 31:4733-4742. [PMID: 38890032 DOI: 10.1016/j.acra.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/20/2024]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to ascertain whether the utilization of multiple b-value diffusion-weighted habitat imaging, a technique that depicts tumor heterogeneity, could aid in identifying breast cancer patients who would derive substantial benefit from neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS This prospective study enrolled 143 women (II-III breast cancer), who underwent multi-b-value diffusion-weighted imaging (DWI) in 3-T magnetic resonance (MR) before NAC. The patient cohort was partitioned into a training set (consisting of 100 patients, of which 36 demonstrated a pathologic complete response [pCR]) and a test set (featuring 43 patients, 16 of whom exhibited pCR). Utilizing the training set, predictive models for pCR, were constructed using different parameters: whole-tumor radiomics (ModelWH), diffusion-weighted habitat-imaging (ModelHabitats), conventional MRI features (ModelCF), along with combined models ModelHabitats+CF. The performance of these models was assessed based on the area under the receiver operating characteristic curve (AUC) and calibration slope. RESULTS In the prediction of pCR, ModelWH, ModelHabitats, ModelCF, and ModelHabitats+CF achieved AUCs of 0.733, 0.722, 0.705, and 0.756 respectively, within the training set. These scores corresponded to AUCs of 0.625, 0.801, 0.700, and 0.824 respectively in the test set. The DeLong test revealed no significant difference between ModelWH and ModelHabitats (P = 0.182), between ModelHabitats and ModelHabitats+CF (P = 0.113). CONCLUSION The habitat model we developed, incorporating first-order features along with conventional MRI features, has demonstrated accurate predication of pCR prior to NAC. This model holds the potential to augment decision-making processes in personalized treatment strategies for breast cancer.
Collapse
Affiliation(s)
- Chao Xu
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (C.X.)
| | - Zhihong Wang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Hematology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Z.W.)
| | - Ailing Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China (A.W., C.W., G.Y.)
| | - Yunyan Zheng
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Y.Z., M.M., M.H.)
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China (Y.S.)
| | - Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China (A.W., C.W., G.Y.)
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China (A.W., C.W., G.Y.)
| | - Mingping Ma
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Y.Z., M.M., M.H.)
| | - Muzhen He
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Y.Z., M.M., M.H.).
| |
Collapse
|
15
|
Zhu Y, Zheng D, Xu S, Chen J, Wen L, Zhang Z, Ruan H. Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma. Jpn J Radiol 2024; 42:1413-1424. [PMID: 39162780 DOI: 10.1007/s11604-024-01639-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 07/27/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm3 (group_1mm) and 3 × 3 × 3 mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. RESULTS Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. CONCLUSIONS A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes.
Collapse
Affiliation(s)
- Yuemin Zhu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China.
| | - Shugui Xu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Zhichao Zhang
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Huiping Ruan
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| |
Collapse
|
16
|
Karagkounis G, Horvat N, Danilova S, Chhabra S, Narayan RR, Barekzai AB, Kleshchelski A, Joanne C, Gonen M, Balachandran V, Soares KC, Wei AC, Kingham TP, Jarnagin WR, Shia J, Chakraborty J, D'Angelica MI. Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy. Ann Surg Oncol 2024; 31:9196-9204. [PMID: 39369120 DOI: 10.1245/s10434-024-15373-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/14/2024] [Indexed: 10/07/2024]
Abstract
OBJECTIVES This study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreatment scans in estimating pathologic treatment response to neoadjuvant therapy, and to compare treatment response estimates between CT-based radiomics and radiological response assessment by using RECIST 1.1 and CT morphologic criteria. METHODS Patients who underwent resection for CRLM from January 2003-December 2012 at a single institution were included. Patients who did not receive preoperative systemic chemotherapy, or without adequate imaging, were excluded. Imaging characteristics were evaluated based on RECIST 1.1 and CT morphologic criteria. A machine-learning model was designed with radiomic features extracted from manually segmented posttreatment CT tumoral and peritumoral regions to identify pathologic responders (≥ 50% response) versus nonresponders. Statistical analysis was performed at the tumor level. RESULTS Eighty-five patients (median age, 62 years; 55 women) with 95 tumors were included. None of the subjectively evaluated imaging characteristics were associated with pathologic response (p > 0.05). Inter-reader agreement was substantial for RECIST categorical response assessment (K = 0.70) and moderate for CT morphological group response (K = 0.50). In the validation cohort, the machine learning model built with radiomic features obtained an area under the curve (AUC) of 0.87 and outperformed subjective RECIST assessment (AUC = 0.53, p = 0.01) and morphologic assessment (AUC = 0.56, p = 0.02). CONCLUSIONS Radiologist assessment of oligometastatic CRLM after neoadjuvant therapy using RECIST 1.1 and CT morphologic criteria was not associated with pathologic response. In contrast, a machine-learning model based on radiomic features extracted from tumoral and peritumoral regions had high diagnostic performance in assessing responders versus nonresponders.
Collapse
Affiliation(s)
- Georgios Karagkounis
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Sofia Danilova
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Salini Chhabra
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Raja R Narayan
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Ahmad B Barekzai
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Adam Kleshchelski
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Chou Joanne
- Department of Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Vinod Balachandran
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Kevin C Soares
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Alice C Wei
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - T Peter Kingham
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - William R Jarnagin
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jinru Shia
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jayasree Chakraborty
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Michael I D'Angelica
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
| |
Collapse
|
17
|
Zhu Y, Liu T, Chen J, Wen L, Zhang J, Zheng D. Prediction of therapeutic response to transarterial chemoembolization plus systemic therapy regimen in hepatocellular carcinoma using pretreatment contrast-enhanced MRI based habitat analysis and Crossformer model. Abdom Radiol (NY) 2024:10.1007/s00261-024-04709-7. [PMID: 39586897 DOI: 10.1007/s00261-024-04709-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 11/14/2024] [Accepted: 11/15/2024] [Indexed: 11/27/2024]
Abstract
PURPOSE To develop habitat and deep learning (DL) models from multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) habitat images categorized using the K-means clustering algorithm. Additionally, we aim to assess the predictive value of identified regions for early evaluation of the responsiveness of hepatocellular carcinoma (HCC) patients to treatment with transarterial chemoembolization (TACE) plus molecular targeted therapies (MTT) and anti-PD-(L)1. METHODS A total of 102 patients with HCC from two institutions (A, n = 63 and B, n = 39) who received TACE plus systemic therapy were enrolled from September 2020 to January 2024. Multiple CE-MRI sequences were used to outline 3D volumes of interest (VOI) of the lesion. Subsequently, K-means clustering was applied to categorize intratumoral voxels into three distinct subgroups, based on signal intensity values of images. Using data from institution A, the habitat model was built with the ExtraTrees classifier after extracting radiomics features from intratumoral habitats. Similarly, the Crossformer model and ResNet50 model were trained on multi-channel data in institution A, and a DL model with Transformer-based aggregation was constructed to predict the response. Finally, all models underwent validation at institution B. RESULTS The Crossformer model and the habitat model both showed high area under the receiver operating characteristic curves (AUCs) of 0.869 and 0.877 (training cohort). In validation, AUC was 0.762 for the Crossformer model and 0.721 for the habitat model. CONCLUSION The habitat model and DL model based on CE-MRI possesses the capability to non-invasively predict the efficacy of TACE plus systemic therapy in HCC patients, which is critical for precision treatment and patient outcomes.
Collapse
Affiliation(s)
- Yuemin Zhu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Tao Liu
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Cancer Institute, and Chongqing Cancer Hospital, Chongqing, China
| | - Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Cancer Institute, and Chongqing Cancer Hospital, Chongqing, China.
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
| |
Collapse
|
18
|
Yuan J, Wu M, Qiu L, Xu W, Fei Y, Zhu Y, Shi K, Li Y, Luo J, Ding Z, Sun X, Zhou S. Tumor habitat-based MRI features assessing early response in locally advanced nasopharyngeal carcinoma. Oral Oncol 2024; 158:106980. [PMID: 39151333 DOI: 10.1016/j.oraloncology.2024.106980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/08/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024]
Abstract
OBJECTIVE The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors. METHODS A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833). CONCLUSION The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC.
Collapse
Affiliation(s)
- Jinling Yuan
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Mengxing Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Lei Qiu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Weilin Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yinjiao Fei
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yuchen Zhu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Kexin Shi
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yurong Li
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Jinyan Luo
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Zhou Ding
- Department of Radiation Oncology, Lianshui County People's Hospital, Huai'an 223400, Jiangsu, China.
| | - Xinchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
| | - Shu Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
| |
Collapse
|
19
|
Wang X, Deng C, Kong R, Gong Z, Dai H, Song Y, Wu Y, Bi G, Ai C, Bi Q. Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma. Acad Radiol 2024:S1076-6332(24)00689-5. [PMID: 39368914 DOI: 10.1016/j.acra.2024.09.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 09/13/2024] [Accepted: 09/16/2024] [Indexed: 10/07/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats. MATERIALS AND METHODS The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC). RESULTS Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor. CONCLUSION Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.
Collapse
Affiliation(s)
- Xianhong Wang
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.); Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B)
| | - Cheng Deng
- Department of Radiology, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650101, China (C.D.)
| | - Ruize Kong
- Department of Vascular Surgery, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (R.K.); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Zhimei Gong
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Hongying Dai
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Yang Song
- MR Research Collaboration, Siemens Healthineers, Shanghai 201318, China (Y.S.)
| | - Yunzhu Wu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China (Y.W.)
| | - Guoli Bi
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Conghui Ai
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China (C.A.)
| | - Qiu Bi
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.).
| |
Collapse
|
20
|
Li T, Gan T, Wang J, Long Y, Zhang K, Liao M. "Application of CT radiomics in brain metastasis of lung cancer: A systematic review and meta-analysis". Clin Imaging 2024; 114:110275. [PMID: 39243496 DOI: 10.1016/j.clinimag.2024.110275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 08/16/2024] [Accepted: 08/25/2024] [Indexed: 09/09/2024]
Abstract
PURPOSE This study aimed to systematically assess the quality and performance of computed tomography (CT) radiomics studies in predicting brain metastasis (BM) among patients with lung cancer. METHODS The PubMed, Embase and Web of Science were searched for studies predicting BM in patients with lung cancer using CT-based radiomics features. Information regarding patients, imaging, and radiomics analysis was extracted from eligible studies. We assessed the quality of included studies using the Radiomics Quality Scoring (RQS) tool and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A meta-analysis of studies regarding the prediction of BM in patients with lung cancer was performed. RESULTS Thirteen studies were identified, with sample sizes ranging from 75 to 602. The mean RQS of the studies was 12 (range 9-16), and the corresponding percentage of the score was 33.55 % (range 25.00-44.44 %). Four studies (30.8 %) were considered as low risk of bias, while the remaining nine studies (69.2 %) were considered to have unclear risks. The meta-analysis included twelve studies. The pooled sensitivity, specificity and Area Under the Curve (AUC) value with 95 % confidence intervals were 0.75 [0.69, 0.80], 0.76 [0.68, 0.82], and 0.81 [0.77-0.84], respectively. CONCLUSION CT radiomics-based models show promising results as a non-invasive method to predict BM in lung cancer patients. However, multicenter and prospective studies are warranted to enhance the stability and acceptance of radiomics.
Collapse
Affiliation(s)
- Ting Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
| | - Tian Gan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
| | - Jingting Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
| | - Yun Long
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
| | - Kemeng Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
| |
Collapse
|
21
|
Zhu N, Meng X, Wang Z, Hu Y, Zhao T, Fan H, Niu F, Han J. Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:3982-3992. [PMID: 38772802 DOI: 10.1016/j.acra.2024.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 05/23/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance. METHODS In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test. RESULTS Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment. CONCLUSION Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials. CLINICAL RELEVANCE STATEMENT Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.
Collapse
Affiliation(s)
- Nana Zhu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Zhi Wang
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Tingting Zhao
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| |
Collapse
|
22
|
Wang SX, Yang Y, Xie H, Yang X, Liu ZQ, Li HJ, Huang WJ, Luo WJ, Lei YM, Sun Y, Ma J, Chen YF, Liu LZ, Mao YP. Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. Eur Radiol 2024; 34:6831-6842. [PMID: 38514481 DOI: 10.1007/s00330-024-10678-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/13/2024] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC). MATERIALS AND METHODS A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods. RESULTS The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients. CONCLUSION The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT. CLINICAL RELEVANCE STATEMENT The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment. KEY POINTS • The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.
Collapse
Affiliation(s)
- Shun-Xin Wang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Yi Yang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Hui Xie
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Xin Yang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Zhi-Qiao Liu
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Hao-Jiang Li
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Wen-Jie Huang
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Wei-Jie Luo
- Department of Medical Oncology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Yi-Ming Lei
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Jun Ma
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Yan-Feng Chen
- Department of Head and Neck Surgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
| | - Li-Zhi Liu
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
| | - Yan-Ping Mao
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
| |
Collapse
|
23
|
Huang F, Huang Q, Liao X, Gao Y. Prediction of high-risk prostate cancer based on the habitat features of biparametric magnetic resonance and the omics features of contrast-enhanced ultrasound. Heliyon 2024; 10:e37955. [PMID: 39323806 PMCID: PMC11423289 DOI: 10.1016/j.heliyon.2024.e37955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 08/22/2024] [Accepted: 09/13/2024] [Indexed: 09/27/2024] Open
Abstract
Rationale and objectives To predict high-risk prostate cancer (PCa) by combining the habitat features of biparametric magnetic resonance imaging (bp-MRI) with the omics features of contrast-enhanced ultrasound (CEUS). Materials and methods This study retrospectively collected patients with PCa confirmed by histopathology from January 2020 to June 2023. All patients underwent bp-MRI and CEUS of the prostate, followed by a targeted and transrectal systematic prostate biopsy. The cases were divided into the intermediate-low-risk group (Gleason score ≤7, n = 59) and high-risk group (Gleason score ≥8, n = 33). Radiomics prediction models, namely, MRI_habitat, CEUS_intra, and MRI-CEUS models, were developed based on the habitat features of bp-MRI, the omics features of CEUS, and a merge of features of the two, respectively. Predicted probabilities, called radscores, were then obtained. Clinical-radiological indicators were screened to construct clinic models, which generated clinic scores. The omics-clinic model was constructed by combining the radscore of MRI-CEUS and the clinic score. The predictive performance of all the models was evaluated using the receiver operating characteristic curve. Results The area under the curve (AUC) values of the MRI-CEUS model were 0.875 and 0.842 in the training set and test set, respectively, which were higher than those of the MR_habitat (training set: 0.846, test set: 0.813), CEUS_intra (training set: 0.801, test set: 0.743), and clinic models (training set: 0.722, test set: 0.611). The omics-clinic model achieved a higher AUC (train set: 0.986, test set: 0.898). Conclusions The combination of the habitat features of bp-MRI and the omics features of CEUS can help predict high-risk PCa.
Collapse
Affiliation(s)
- Fangyi Huang
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| | - Qun Huang
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| | - Xinhong Liao
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| | - Yong Gao
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Rd, Nanning, 530021, Guangxi, China
| |
Collapse
|
24
|
Liu W, Wang W, Guo M, Zhang H. Tumor habitat and peritumoral region evolution-based imaging features to assess risk categorization of thymomas. Clin Radiol 2024; 79:e1117-e1125. [PMID: 38862335 DOI: 10.1016/j.crad.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
AIM To develop an aggregate model that integrated clinical data, habitat characteristics, and intratumoral and peritumoral features to assess the risk categorization of thymomas. MATERIALS AND METHODS We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models. RESULTS The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769. CONCLUSION Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.
Collapse
Affiliation(s)
- W Liu
- School of Health Management, China Medical University, Shenyang City, Liaoning Province, PR China.
| | - W Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang City, Liaoning Province, PR China.
| | - M Guo
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, PR China.
| | - H Zhang
- Department of Radiology, Liaoning Cancer Hospital and Institute, Shenyang City, Liaoning Province, PR China.
| |
Collapse
|
25
|
Cao Y, Zhu H, Li Z, Liu C, Ye J. CT Image-Based Radiomic Analysis for Detecting PD-L1 Expression Status in Bladder Cancer Patients. Acad Radiol 2024; 31:3678-3687. [PMID: 38556431 DOI: 10.1016/j.acra.2024.02.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024]
Abstract
RATIONALE AND OBJECTIVES The role of Programmed death-ligand 1 (PD-L1) expression is crucial in guiding immunotherapy selection. This study aims to develop and evaluate a radiomic model, leveraging Computed Tomography (CT) imaging, with the objective of predicting PD-L1 expression status in patients afflicted with bladder cancer. MATERIALS AND METHODS The study encompassed 183 subjects diagnosed with histologically confirmed bladder cancer, among which the PD-L1(+) cohort constituted 60.1% of the total population. Stratified random sampling was utilized at a 7:3 ratio. We employed five diverse machine learning algorithms-Decision Tree, Random Forest, Linear Support Vector Classification, Support Vector Machine, and Logistic Regression-to establish radiomic models on the training dataset. These models endeavored to predict PD-L1 expression status premised on radiomic features derived from region-of-interest segmentation. Subsequent to this, the predictive performance of these models was examined on a validation set employing the receiver operating characteristic (ROC) curve. The DeLong test was utilized to contrast ROC curves, thereby pinpointing the model with superior predictive accuracy. RESULTS 16 features were chosen for the model construction. All five models revealed strong performance in the training set (AUC, 0.920-1) and commendable predictive ability in the validation set (AUC, 0.753-0.766). As per the DeLong test, no statistically significant disparities were observed among any of the models (P > 0.05) in the validation set. Additional verification through the calibration curve and decision curve analysis indicated that the Logistic Regression model exhibited extraordinary precision and practicality. CONCLUSION Our machine learning model, grounded on radiomic features, demonstrated its proficiency in accurately distinguishing bladder cancer patients with high PD-L1 expression. Future research, incorporating more exhaustive datasets, could potentially augment the predictive efficiency of radiomic algorithms, thereby advancing their clinical utility.
Collapse
Affiliation(s)
- Ying Cao
- Department of Radiotherapy, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou 215028, China
| | - Hongyu Zhu
- Department of Radiotherapy, The Affiliated Suzhou Hospital of Nanjing University Medical School, Suzhou 215153, China
| | - Zhenkai Li
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou 215028, China
| | - Canyu Liu
- Department of Radiotherapy, Dushu Lake Hospital Affiliated to Soochow University, Suzhou 215127, China
| | - Juan Ye
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou 215028, China.
| |
Collapse
|
26
|
Ocaña-Tienda B, Pérez-Beteta J, Ortiz de Mendivil A, Asenjo B, Albillo D, Pérez-Romasanta LA, LLorente M, Carballo N, Arana E, Pérez-García VM. Morphological MRI features as prognostic indicators in brain metastases. Cancer Imaging 2024; 24:111. [PMID: 39164779 PMCID: PMC11334491 DOI: 10.1186/s40644-024-00753-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
BACKGROUND Stereotactic radiotherapy is the preferred treatment for managing patients with fewer than five brain metastases (BMs). However, some lesions recur after irradiation. The purpose of this study was to identify patients who are at a higher risk of failure, which can help in adjusting treatments and preventing recurrence. METHODS In this retrospective multicenter study, we analyzed the predictive significance of a set of interpretable morphological features derived from contrast-enhanced (CE) T1-weighted MR images as imaging biomarkers using Kaplan-Meier analysis. The feature sets studied included the total and necrotic volumes, the surface regularity and the CE rim width. Additionally, we evaluated other nonmorphological variables and performed multivariate Cox analysis. RESULTS A total of 183 lesions in 128 patients were included (median age 61 [31-95], 64 men and 64 women) treated with stereotactic radiotherapy (57% single fraction, 43% fractionated radiotherapy). None of the studied variables measured at diagnosis were found to have prognostic value. However, the total and necrotic volumes and the CE rim width measured at the first follow-up after treatment and the change in volume due to irradiation can be used as imaging biomarkers for recurrence. The optimal classification was achieved by combining the changes in tumor volume before and after treatment with the presence or absence of necrosis (p < < 0.001). CONCLUSION This study demonstrated the prognostic significance of interpretable morphological features extracted from routine clinical MR images following irradiation in brain metastases, offering valuable insights for personalized treatment strategies.
Collapse
Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | | | - Beatriz Asenjo
- Hospital Regional Universitario de Málaga, Málaga, Spain
| | | | | | | | | | | | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| |
Collapse
|
27
|
Casotti MC, Meira DD, Zetum ASS, Campanharo CV, da Silva DRC, Giacinti GM, da Silva IM, Moura JAD, Barbosa KRM, Altoé LSC, Mauricio LSR, Góes LSBDB, Alves LNR, Linhares SSG, Ventorim VDP, Guaitolini YM, dos Santos EDVW, Errera FIV, Groisman S, de Carvalho EF, de Paula F, de Sousa MVP, Fechine PBA, Louro ID. Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy. Front Oncol 2024; 14:1419599. [PMID: 39224803 PMCID: PMC11367711 DOI: 10.3389/fonc.2024.1419599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/31/2024] [Indexed: 09/04/2024] Open
Abstract
Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.
Collapse
Affiliation(s)
- Matheus Correia Casotti
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | - Débora Dummer Meira
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | | | | | | | - Giulia Maria Giacinti
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | - Iris Moreira da Silva
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | - João Augusto Diniz Moura
- Laboratório de Oncologia Clínica e Experimental, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | - Karen Ruth Michio Barbosa
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | - Lorena Souza Castro Altoé
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | | | | | - Lyvia Neves Rebello Alves
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | | | - Vinícius do Prado Ventorim
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | - Yasmin Moreto Guaitolini
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | | | | | - Sonia Groisman
- Instituto de Biologia Roberto Alcântara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcântara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil
| | - Flavia de Paula
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| | | | - Pierre Basílio Almeida Fechine
- Group of Chemistry of Advanced Materials (GQMat), Department of Analytical Chemistry and Physical-Chemistry, Federal University of Ceará (UFC), Fortaleza, CE, Brazil
| | - Iuri Drumond Louro
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Vitória, ES, Brazil
| |
Collapse
|
28
|
Yang X, Niu W, Wu K, Li X, Hou H, Tan Y, Wang X, Yang G, Wang L, Zhang H. Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas. Ann Clin Transl Neurol 2024; 11:2073-2087. [PMID: 38887966 PMCID: PMC11330218 DOI: 10.1002/acn3.52128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/14/2024] [Accepted: 06/02/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVE High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment. METHODS We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score,ITH score) and heterogeneity, as well as high-risk molecular subtypes. RESULTS The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes. INTERPRETATION The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.
Collapse
Affiliation(s)
- Xiangli Yang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi HospitalTaiyuan030032China
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
| | - Wenju Niu
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
| | - Kai Wu
- Department of Information ManagementFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Xiang Li
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
| | - Heng Hou
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Yan Tan
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Xiaochun Wang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Guoqiang Yang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
- Shanxi Key Laboratory of Intelligent Imaging and NanomedicineFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| | - Lei Wang
- Beijing Tiantan HospitalCapital Medical UniversityBeijing100050China
| | - Hui Zhang
- Department of RadiologyFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- College of Medical Imaging, Shanxi Medical UniversityTaiyuan030001China
- Shanxi Key Laboratory of Intelligent Imaging and NanomedicineFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
- Intelligent Imaging Big Data and Functional Nano‐imaging Engineering Research Center of Shanxi ProvinceFirst Hospital of Shanxi Medical UniversityTaiyuan030001China
| |
Collapse
|
29
|
Jia PF, Li YR, Wang LY, Lu XR, Guo X. Radiomics in esophagogastric junction cancer: A scoping review of current status and advances. Eur J Radiol 2024; 177:111577. [PMID: 38905802 DOI: 10.1016/j.ejrad.2024.111577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 06/03/2024] [Accepted: 06/14/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer. METHODS We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively. RESULTS A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%). CONCLUSION Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.
Collapse
Affiliation(s)
- Ping-Fan Jia
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yu-Ru Li
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Lu-Yao Wang
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Xiao-Rui Lu
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Xing Guo
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
| |
Collapse
|
30
|
He R, Song G, Fu J, Dou W, Li A, Chen J. Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer. Quant Imaging Med Surg 2024; 14:5358-5372. [PMID: 39144004 PMCID: PMC11320521 DOI: 10.21037/qims-23-1614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 07/05/2024] [Indexed: 08/16/2024]
Abstract
Background Unfortunately, the morphologic magnetic resonance imaging (MRI) is unable to determine perineural invasion (PNI) status. This study applied histogram analysis of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the assessment of PNI status of rectal cancer (RC). Methods The retrospective analysis enrolled 175 patients with RC confirmed by postoperative pathology in The First Affiliated Hospital of Shandong First Medical University from January 2019 to December 2021. All patients underwent preoperative rectal MRI. Whole-tumor volume histogram features from IVIM-DWI were extracted using open-source software. Univariate analysis and multivariate logistic regression analysis were used to compare the differences in histogram parameters and clinical features between the PNI-positive group and PNI-negative group. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance, while the Delong test was used to compare the area under the curve of the models. Results The interobserver agreement of the histogram features derived from DWI, including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC) were good to excellent. A total of eight histogram features including DWI_maximum, DWI_skewness, D_kurtosis, D_minimum, D_skewness, D*_energy, D*_skewness, and f_minimum were significantly different between the PNI-positive and PNI-negative groups in the univariate analysis (P<0.05); among the clinicoradiologic factors, percentage of rectal wall circumference invasion (PCI) was significantly different between the two groups (P<0.05). Multivariate analysis demonstrated that the values of D*_energy, D*_skewness, and f_minimum differed significantly between the PNI-positive patients and PNI-negative patients (P<0.05), with the independent risk factors being D*_skewness [odds ratio (OR) =1.157; 95% confidence interval (CI): 1.050-1.276; P=0.003] and PCI (OR =11.108, 95% CI: 1.767-69.838; P=0.0002). The area under the curve of the model combining the three histogram features and PCI to assess PNI status in RC was 0.807 (95% CI: 0.741-0.863). The results of the Delong test showed that the combined model was significantly different from each single-parameter model (P<0.05). Conclusions The combined model constructed on the basis of IVIM-DWI histogram features may help to assess the status of RC PNI.
Collapse
Affiliation(s)
- Rong He
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Gesheng Song
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Junyi Fu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | | | - Aiyin Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Jingbo Chen
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| |
Collapse
|
31
|
Paverd H, Zormpas-Petridis K, Clayton H, Burge S, Crispin-Ortuzar M. Radiology and multi-scale data integration for precision oncology. NPJ Precis Oncol 2024; 8:158. [PMID: 39060351 PMCID: PMC11282284 DOI: 10.1038/s41698-024-00656-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.
Collapse
Affiliation(s)
- Hania Paverd
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | | | - Hannah Clayton
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Sarah Burge
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Mireia Crispin-Ortuzar
- Department of Oncology, University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
| |
Collapse
|
32
|
Duan C, Liu Q, Wang J, Tong Q, Bai F, Han J, Wang S, Hippe DS, Zeng J, Bowen SR. GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer. Phys Med Biol 2024; 69:10.1088/1361-6560/ad6118. [PMID: 38981590 PMCID: PMC11338282 DOI: 10.1088/1361-6560/ad6118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.
Collapse
Affiliation(s)
- Chunyan Duan
- Department of Mechanical Engineering, School of Mechanical Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, P. R. China
| | - Qiantuo Liu
- Department of Mechanical Engineering, School of Mechanical Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, P. R. China
| | - Jiajie Wang
- Department of Mechanical Engineering, School of Mechanical Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, P. R. China
| | - Qianqian Tong
- Maseeh Department of Civil, Architectural and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, 301 East Dean Keeton Street, Austin, TX 78712, USA
| | - Fangyun Bai
- Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, 2209 Guangxing Road, Shanghai 201613, P. R. China
| | - Jie Han
- Department of Industrial, Manufacturing, and Systems Engineering, College of Engineering, The University of Texas at Arlington, 500 West First Street, Arlington, TX 76019, USA
| | - Shouyi Wang
- Department of Industrial, Manufacturing, and Systems Engineering, College of Engineering, The University of Texas at Arlington, 500 West First Street, Arlington, TX 76019, USA
| | - Daniel S. Hippe
- Clinical Research Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
| | - Jing Zeng
- Department of Radiation Oncology, School of Medicine, University of Washington, 1959 North East Pacific Street, Seattle, WA 98195, USA
| | - Stephen R. Bowen
- Clinical Research Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
- Department of Radiation Oncology, School of Medicine, University of Washington, 1959 North East Pacific Street, Seattle, WA 98195, USA
- Department of Radiology, School of Medicine, University of Washington, 1959 North East Pacific Street, Seattle, WA 98195, USA
| |
Collapse
|
33
|
Wu J, Meng H, Zhou L, Wang M, Jin S, Ji H, Liu B, Jin P, Du C. Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study. Sci Rep 2024; 14:15877. [PMID: 38982267 PMCID: PMC11233600 DOI: 10.1038/s41598-024-66751-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 07/03/2024] [Indexed: 07/11/2024] Open
Abstract
Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.
Collapse
Affiliation(s)
- Jingran Wu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Hao Meng
- Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Lin Zhou
- Department of Thoracic Surgery, Yuebei People's Hospital Affiliated to Shantou University Medical College, Shaoguan, 512025, China
| | - Meiling Wang
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Shanxiu Jin
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Hongjuan Ji
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Bona Liu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China.
| | - Peng Jin
- Department of Oncology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China.
| | - Cheng Du
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China.
| |
Collapse
|
34
|
Wang Q, Zhou Y, Yang H, Zhang J, Zeng X, Tan Y. MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma. Med Phys 2024; 51:4673-4686. [PMID: 38642400 DOI: 10.1002/mp.17087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/12/2024] [Accepted: 04/02/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. PURPOSE Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHOD The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). RESULT Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. CONCLUSION This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.
Collapse
Affiliation(s)
- Qinghua Wang
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Hongan Yang
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Jingrun Zhang
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Xianjun Zeng
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yongming Tan
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| |
Collapse
|
35
|
Ye G, Wu G, Zhang C, Wang M, Liu H, Song E, Zhuang Y, Li K, Qi Y, Liao Y. CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer. Front Immunol 2024; 15:1414954. [PMID: 38933281 PMCID: PMC11199789 DOI: 10.3389/fimmu.2024.1414954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Objectives To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.
Collapse
Affiliation(s)
- Guanchao Ye
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Guangyao Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunyang Zhang
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Mingliang Wang
- Department of Thoracic Surgery, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, China
| | - Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Kuo Li
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Qi
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
36
|
Zhang M, Li X, Zhou P, Zhang P, Wang G, Lin X. Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics. Front Oncol 2024; 14:1411261. [PMID: 38903726 PMCID: PMC11187250 DOI: 10.3389/fonc.2024.1411261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024] Open
Abstract
Objective Construct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer. Materials and methods This study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models. Results Univariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model's predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model. Conclusion The nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer.
Collapse
Affiliation(s)
- Min Zhang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xuanyu Li
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Pin Zhou
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| | - Panpan Zhang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| | - Gang Wang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| | - Xianfang Lin
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| |
Collapse
|
37
|
Caii W, Wu X, Guo K, Chen Y, Shi Y, Chen J. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. Cancer Immunol Immunother 2024; 73:153. [PMID: 38833187 PMCID: PMC11150226 DOI: 10.1007/s00262-024-03724-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). METHODS Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. RESULTS The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. CONCLUSION The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.
Collapse
Affiliation(s)
- Weimin Caii
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China
| | - Xiao Wu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yongxian Chen
- Department of Chest Cancer, Xiamen Second People's Hospital, Xiamen, 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou, 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China.
| |
Collapse
|
38
|
Qiao J, Kang H, Ran Q, Tong H, Ma Q, Wang S, Zhang W, Wu H. Metabolic habitat imaging with hemodynamic heterogeneity predicts individual progression-free survival in high-grade glioma. Clin Radiol 2024; 79:e842-e853. [PMID: 38582632 DOI: 10.1016/j.crad.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 12/07/2023] [Accepted: 02/10/2024] [Indexed: 04/08/2024]
Abstract
AIM We design a feasibility study to obtain a set of metabolic-hemodynamic habitats for tackling tumor spatial metabolic patterns with hemodynamic information. MATERIALS AND METHODS Preoperative data from 69 high-grade gliomas (HGG) patients with subsequent histologic confirmation of HGG were prospectively collected (January 2016 to March 2020) after concurrent chemoradiotherapy (CCRT). Four vascular habitats were automatically segmented by multiparametric magnetic resonance imaging (MRI). The metabolic information, either at enhancing or edema tumor regions, was obtained by two neuroradiologists. The relative habitat volumes were used for weight estimation procedures for computing the coefficients of a linear regression model using weighted least squares (WLS) for metabolite semiquantifications (i.e. the Cho/NAA ratio and the Cho/Cr ratio) at vascular habitats. Multivariate Cox proportional hazard regression analyses are used to obtain the odds ratio (OR) and develop a nomogram using weighted estimators corresponding to each covariate derived from Cox regression coefficients. RESULTS There was a strongly correlation between perfusion indexes and the Cho/Cr ratio (rCBV, r=0.71) or Cho/NAA ratio (rCBV, r=0.66) at high-angiogenic enhancing tumor habitats (HAT) habitat. Compared isocitrate dehydrogenase (IDH) mutation to their wild type, the IDH wild type had significantly decreased Cho/Cr ratio (IDH mutation: Cho/Cr ratio = 2.44 ± 0.33, IDH wildtype: Cho/Cr ratio = 2.66 ± 0.36, p=0.02) and Cho/NAA ratio (IDH mutation: Cho/Cr ratio = 4.59 ± 0.61, IDH wildtype: Cho/Cr ratio = 4.99 ± 0.66, p=0.022) at the HAT. The C-index for the median progression-free survival (PFS) prediction was 0.769 for the Cho/NAA nomogram and 0.747 for the Cho/Cr nomogram through 1000 bootstrapping validation. CONCLUSIONS Our findings suggest that spatial metabolism combined with hemodynamic heterogeneity is associated with individual PFS to HGG patients post-CCRT.
Collapse
Affiliation(s)
- J Qiao
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Kang
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - Q Ran
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Tong
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - Q Ma
- Department of Pathology, Army Medical Center, PLA, Chongqing, 400042, China
| | - S Wang
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China.
| | - W Zhang
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China.
| | - H Wu
- Department of Radiology, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 400024, China; Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Chongqing, 400042, China.
| |
Collapse
|
39
|
Yang Y, Guo Y. Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network. Front Neurol 2024; 15:1394879. [PMID: 38765270 PMCID: PMC11099238 DOI: 10.3389/fneur.2024.1394879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 04/12/2024] [Indexed: 05/21/2024] Open
Abstract
Objectives This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions. Design In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features Ffuse from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of Ffuse in predicting stroke outcomes. Results The experimental results show that the feature Ffuse generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models. Conclusion The method used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment.
Collapse
Affiliation(s)
- Yingjian Yang
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China
- Shenzhen Lanmage Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Yingwei Guo
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China
| |
Collapse
|
40
|
Xiao Y, Huang P, Zhang Y, Lu X, Zhou C, Wu F, Wang Y, Zeng M, Yang C. Component prediction in combined hepatocellular carcinoma-cholangiocarcinoma: habitat imaging and its biologic underpinnings. Abdom Radiol (NY) 2024; 49:1063-1073. [PMID: 38315194 DOI: 10.1007/s00261-023-04174-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 02/07/2024]
Abstract
PURPOSE To construct an MRI-based habitat imaging model to help predict component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) preoperatively, and investigate the biologic underpinnings of habitat imaging in cHCC-CCA. METHODS The study consisted of one retrospective model-building dataset and one prospective validation dataset from two hospitals. All voxels were assigned into different clusters according to the similarity of enhancement pattern by using K-means clustering method, and each habitat's volume fraction in each lesion was calculated. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select optimal predictors, and then to establish an MRI-based habitat imaging model. R-squared was calculated to evaluate performance of the prediction models. Model performance was also verified in the prospective dataset with RNA sequencing data, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was then applied to investigate the biologic underpinnings of habitat imaging. RESULTS A total of 129 patients were enrolled (mean age, 56.1 ± 10.4, 102 man), among which 104 patients were in the retrospective model-building set, while 25 patients in the prospective validation set. Three habitats, habitat1 (HCC-alike habitat), habitat2 (iCCA-alike habitat), and habitat3 (in-between habitat), were identified. Habitat 1's volume fraction, habitat 3's volume fraction, nonrim APHE, nonperipheral washout, and LI-RADS categorization were selected to develop an HCC percentage prediction model with R-squared of 0.611 in the model-building set and 0.541 in the validation set. Habitat 1's volume fraction was correlated with genes involved in regulation of actin cytoskeleton and Rap1 signaling pathway, which regulate cell migration and tumor metastasis. CONCLUSION Preoperative prediction of HCC percentage in patients with cHCC-CCA was achieved using an MRI-based habitat imaging model, which may correlate with signaling pathways regulating cell migration and tumor metastasis.
Collapse
Affiliation(s)
- Yuyao Xiao
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Peng Huang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yunfei Zhang
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Xin Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Changwu Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Fei Wu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yi Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
| |
Collapse
|
41
|
Yu M, Ge Y, Wang Z, Zhang Y, Hou X, Chen H, Chen X, Ji N, Li X, Shen H. The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study. J Neurooncol 2024; 167:305-313. [PMID: 38424338 DOI: 10.1007/s11060-024-04609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024]
Abstract
PURPOSE Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. METHODS Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. RESULTS 64 patients (female, 21; male, 43; age, 47.0 ± 13.7 years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). CONCLUSIONS The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.
Collapse
Affiliation(s)
- Meimei Yu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Radiology, The First People's Hospital of Longquanyi District, Chengdu, Sichuan Province, China
| | - Ying Ge
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Radiology, Beijing Huimin Hospital, Beijing, China
| | - Zixuan Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xinyi Hou
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Hongyan Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huicong Shen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
| |
Collapse
|
42
|
Zhang W, Liang F, Zhao Y, Li J, He C, Zhao Y, Lai S, Xu Y, Ding W, Wei X, Jiang X, Yang R, Zhen X. Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC. Phys Med Biol 2024; 69:055032. [PMID: 38306970 DOI: 10.1088/1361-6560/ad25c0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
Objective.To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model.Approach.466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFFmodel (fused features from all MRI sequences), RADCmodel (ADC radiomics feature), StratifiedADCmodel (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADCmodel were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n= 337) and test set (n= 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy.Main results.Both the RFFand StratifiedADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADCmodel revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p <0.05). The integrated RFF-StratifiedADCmodel demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p <0.05).Significance.The RFF-StratifiedADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADCmodel and radiomics information from mpMRI-based RFFmodel, exhibits tremendous promise for identifying TNBC.
Collapse
Affiliation(s)
- Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Fangrong Liang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Yue Zhao
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Jiamin Li
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Chutong He
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Yandong Zhao
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, People's Republic of China
| | - Yongzhou Xu
- Philips Healthcare, Guangzhou, Guangdong, 510220, People's Republic of China
| | - Wenshuang Ding
- Department of Pathology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Xinhua Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| |
Collapse
|
43
|
Zhao H, Su Y, Wang Y, Lyu Z, Xu P, Gu W, Tian L, Fu P. Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer. Cancer Imaging 2024; 24:26. [PMID: 38342905 PMCID: PMC10860234 DOI: 10.1186/s40644-024-00670-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/29/2024] [Indexed: 02/13/2024] Open
Abstract
BACKGROUND To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC). METHODS We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model. RESULTS The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection. CONCLUSION The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.
Collapse
Affiliation(s)
- Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yexin Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yan Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhehao Lyu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Xu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan
| | - Lin Tian
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
| |
Collapse
|
44
|
Wei R, Lu S, Lai S, Liang F, Zhang W, Jiang X, Zhen X, Yang R. A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. J Cancer Res Clin Oncol 2024; 150:73. [PMID: 38305926 PMCID: PMC10837235 DOI: 10.1007/s00432-023-05603-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/26/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.
Collapse
Affiliation(s)
- Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Songlin Lu
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Fangrong Liang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China.
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China.
| |
Collapse
|
45
|
Liu Y, Jha AK. How accurately can quantitative imaging methods be ranked without ground truth: An upper bound on no-gold-standard evaluation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12929:129290W. [PMID: 39610808 PMCID: PMC11601990 DOI: 10.1117/12.3006888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2024]
Abstract
Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards. The development of NGSE methods has raised an important question: how accurately can QI methods be ranked without ground truth. To answer this question, we propose a Cramér-Rao bound (CRB)-based framework that quantifies the upper bound in ranking QI methods without any ground truth. We present the application of this framework in guiding the use of a well-known NGSE technique, namely the regression-without-truth (RWT) technique. Our results show the utility of this framework in quantifying the performance of this NGSE technique for different patient numbers. These results provide motivation towards studying other applications of this upper bound.
Collapse
Affiliation(s)
- Yan Liu
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Abhinav K. Jha
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
46
|
Yin J, Dong F, An J, Guo T, Cheng H, Zhang J, Zhang J. Pattern recognition of microcirculation with super-resolution ultrasound imaging provides markers for early tumor response to anti-angiogenic therapy. Theranostics 2024; 14:1312-1324. [PMID: 38323316 PMCID: PMC10845201 DOI: 10.7150/thno.89306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/28/2023] [Indexed: 02/08/2024] Open
Abstract
Rationale: Cancer treatment outcome is traditionally evaluated by tumor volume change in clinics, while tumor microvascular heterogeneity reflecting tumor response has not been fully explored due to technical limitations. Methods: We introduce a new paradigm in super-resolution ultrasound imaging, termed pattern recognition of microcirculation (PARM), which identifies both hemodynamic and morphological patterns of tumor microcirculation hidden in spatio-temporal space trajectories of microbubbles. Results: PARM demonstrates the ability to distinguish different local blood flow velocities separated by a distance of 24 μm. Compared with traditional vascular parameters, PARM-derived heterogeneity parameters prove to be more sensitive to microvascular changes following anti-angiogenic therapy. Particularly, PARM-identified "sentinel" microvasculature, exhibiting evident structural changes as early as 24 hours after treatment initiation, correlates significantly with subsequent tumor volume changes (|r| > 0.9, P < 0.05). This provides prognostic insight into tumor response much earlier than clinical criteria. Conclusions: The ability of PARM to noninvasively quantify tumor vascular heterogeneity at the microvascular level may shed new light on early-stage assessment of cancer therapy.
Collapse
Affiliation(s)
- Jingyi Yin
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Feihong Dong
- College of Future Technology, Peking University, Beijing, China
- State Key Laboratory of Membrane Biology, Peking-Tsinghua Center for Life Sciences, and Institute of Molecular Medicine, Peking University, Beijing, China
- National Biomedical Imaging Center, Peking University, Beijing, China
| | - Jian An
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Tianyu Guo
- College of Future Technology, Peking University, Beijing, China
| | - Heping Cheng
- College of Future Technology, Peking University, Beijing, China
- State Key Laboratory of Membrane Biology, Peking-Tsinghua Center for Life Sciences, and Institute of Molecular Medicine, Peking University, Beijing, China
- National Biomedical Imaging Center, Peking University, Beijing, China
- Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, China
| | - Jiabin Zhang
- College of Future Technology, Peking University, Beijing, China
- State Key Laboratory of Membrane Biology, Peking-Tsinghua Center for Life Sciences, and Institute of Molecular Medicine, Peking University, Beijing, China
- National Biomedical Imaging Center, Peking University, Beijing, China
| | - Jue Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- National Biomedical Imaging Center, Peking University, Beijing, China
- Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, China
- College of Engineering, Peking University, Beijing, China
| |
Collapse
|
47
|
Browning AP, Lewin TD, Baker RE, Maini PK, Moros EG, Caudell J, Byrne HM, Enderling H. Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling. Bull Math Biol 2024; 86:19. [PMID: 38238433 PMCID: PMC10796515 DOI: 10.1007/s11538-023-01246-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 12/14/2023] [Indexed: 01/22/2024]
Abstract
Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations.
Collapse
Affiliation(s)
| | - Thomas D Lewin
- Mathematical Institute, University of Oxford, Oxford, UK
- Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - Ruth E Baker
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Philip K Maini
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA
| | - Jimmy Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA
| | - Helen M Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Heiko Enderling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA.
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA.
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
| |
Collapse
|
48
|
Alshamrani K, Alshamrani HA. A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images1. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:123-139. [PMID: 37458060 DOI: 10.3233/xst-230095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images.
Collapse
Affiliation(s)
- Khalaf Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | - Hassan A Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| |
Collapse
|
49
|
Liu J, Cong C, Zhang J, Qiao J, Guo H, Wu H, Sang Z, Kang H, Fang J, Zhang W. Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas. Clin Radiol 2024; 79:e127-e136. [PMID: 37923627 DOI: 10.1016/j.crad.2023.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 08/15/2023] [Accepted: 09/22/2023] [Indexed: 11/07/2023]
Abstract
AIM To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI). MATERIALS AND METHODS Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test. RESULTS Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05). CONCLUSIONS The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.
Collapse
Affiliation(s)
- J Liu
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - C Cong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, China; School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, 400054, China
| | - J Zhang
- Department of Radiology, General Hospital of Western Theater Command of PLA, Chengdu, 600083, China
| | - J Qiao
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Guo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Z Sang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Kang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - J Fang
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China; Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - W Zhang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China.
| |
Collapse
|
50
|
Lv T, Hong X, Liu Y, Miao K, Sun H, Li L, Deng C, Jiang C, Pan X. AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107857. [PMID: 37865058 DOI: 10.1016/j.cmpb.2023.107857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 08/23/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
BACKGROUND AND OBJECTIVES Tumor microenvironment (TME) is a determining factor in decision-making and personalized treatment for breast cancer, which is highly intra-tumor heterogeneous (ITH). However, the noninvasive imaging phenotypes of TME are poorly understood, even invasive genotypes have been largely known in breast cancer. METHODS Here, we develop an artificial intelligence (AI)-driven approach for noninvasively characterizing TME by integrating the predictive power of deep learning with the explainability of human-interpretable imaging phenotypes (IMPs) derived from 4D dynamic imaging (DCE-MRI) of 342 breast tumors linked to genomic and clinical data, which connect cancer phenotypes to genotypes. An unsupervised dual-attention deep graph clustering model (DGCLM) is developed to divide bulk tumor into multiple spatially segregated and phenotypically consistent subclusters. The IMPs ranging from spatial heterogeneity to kinetic heterogeneity are leveraged to capture architecture, interaction, and proximity between intratumoral subclusters. RESULTS We demonstrate that our IMPs correlate with well-known markers of TME and also can predict distinct molecular signatures, including expression of hormone receptor, epithelial growth factor receptor and immune checkpoint proteins, with the performance of accuracy, reliability and transparency superior to recent state-of-the-art radiomics and 'black-box' deep learning methods. Moreover, prognostic value is confirmed by survival analysis accounting for IMPs. CONCLUSIONS Our approach provides an interpretable, quantitative, and comprehensive perspective to characterize TME in a noninvasive and clinically relevant manner.
Collapse
Affiliation(s)
- Tianxu Lv
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
| | - Xiaoyan Hong
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
| | - Yuan Liu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
| | - Kai Miao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Heng Sun
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China.
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Chuxia Deng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China.
| | - Chunjuan Jiang
- Department of Nuclear Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
| | - Xiang Pan
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China.
| |
Collapse
|