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Liang L, Pang JS, Gao RZ, Que Q, Wu YQ, Peng JB, Bai XM, Qin Q, Tang QQ, Li LP, He Y, Yang H. Development and validation of a combined radiomic and clinical model based on contrast-enhanced ultrasound for preoperative prediction of CK19-positive hepatocellular carcinoma. Abdom Radiol (NY) 2025:10.1007/s00261-025-04799-x. [PMID: 39907719 DOI: 10.1007/s00261-025-04799-x] [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: 11/23/2024] [Revised: 12/31/2024] [Accepted: 01/03/2025] [Indexed: 02/06/2025]
Abstract
PURPOSE We aimed to develop and validate a combined model integrating radiomic features derived from Contrast-Enhanced Ultrasound (CEUS) images and clinical parameters for preoperative prediction of CK19-positive status in hepatocellular carcinoma (HCC). METHODS A total of 434 patients who underwent CEUS and surgical resection from January 2020 to December 2023 were included. Patients were randomly divided into a training cohort (n = 304) and a validation cohort (n = 130). Radiomic features were extracted from multiphase CEUS images, including two-dimensional ultrasound (US), arterial, portal venous, and delayed phases, and combined to derive a Radscore model. Subsequently, a Combined Model was constructed using the Radscore and clinical parameters. Model performance was assessed using calibration, discrimination, and clinical utility. RESULTS Multivariate logistic regression analysis identified Radscore (OR = 10.054, 95% CI: 5.931-19.120, p < 0.001) and AFP levels > 200 ng/mL (OR = 5.027, 95% CI: 2.089-12.784, p < 0.001) as significant predictors in the combined model. The AUC (Area Under the Curve) for the Combined Model was 0.954 in the training cohort and 0.927 in the validation cohort, compared to 0.939 and 0.917 for the Radscore Model alone. Calibration curves demonstrated strong concordance between predicted and actual outcomes. Decision curve analysis (DCA) showed that both the Radscore Model and the Combined Model exhibited good net benefits across a wide range of threshold values in both the training and validation cohorts. CONCLUSION The Radscore based on CEUS, combined with the serum markers AFP > 200 ng/L to construct a Combined Model, shows good predictive performance for CK19 + hepatocellular carcinoma (HCC).
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Affiliation(s)
- Li Liang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Jin-Shu Pang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Qiao Que
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yu-Quan Wu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Jin-Bo Peng
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Xiu-Mei Bai
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Qiong Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Quan-Quan Tang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Li-Peng Li
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China.
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China.
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, China.
- Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor/Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Nanning, China.
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Ren L, Chen DB, Yan X, She S, Yang Y, Zhang X, Liao W, Chen H. Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma. J Hepatocell Carcinoma 2024; 11:2359-2372. [PMID: 39619602 PMCID: PMC11608547 DOI: 10.2147/jhc.s423549] [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: 08/02/2024] [Accepted: 11/19/2024] [Indexed: 01/04/2025] Open
Abstract
Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the third leading cause of cancer-related deaths. Imaging plays a crucial role in the screening, diagnosis, and monitoring of HCC; however, the potential mechanism regarding phenotypes or molecular subtyping remains underexplored. Radiomics significantly expands the selection of features available by extracting quantitative features from imaging data. Radiogenomics bridges the gap between imaging and genetic/transcriptomic information by associating imaging features with critical genes and pathways, thereby providing biological annotations to these features. Despite challenges in interpreting these connections, assessing their universality, and considering the diversity in HCC etiology and genetic information across different populations, radiomics and radiogenomics offer new perspectives for precision treatment in HCC. This article provides an up-to-date summary of the advancements in radiomics and radiogenomics throughout the HCC care continuum, focusing on the clinical applications, advantages, and limitations of current techniques and offering prospects. Future research should aim to overcome these challenges to improve the prognosis of HCC patients and leverage imaging information for patient benefit.
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Affiliation(s)
- Liying Ren
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Dong Bo Chen
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Xuanzhi Yan
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, 541001, People’s Republic of China
| | - Shaoping She
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Yao Yang
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Xue Zhang
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Weijia Liao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, 541001, People’s Republic of China
| | - Hongsong Chen
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
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方 威, 肖 慧, 王 爽, 林 晓, 陈 超. [A deep learning model based on magnetic resonance imaging and clinical feature fusion for predicting preoperative cytokeratin 19 status in hepatocellular carcinoma]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:1738-1751. [PMID: 39505342 PMCID: PMC11744095 DOI: 10.12122/j.issn.1673-4254.2024.09.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Indexed: 11/08/2024]
Abstract
OBJECTIVE To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging (MRI) deep learning features with clinical features for preoperative prediction of cytokeratin 19 (CK19) status of hepatocellular carcinoma (HCC). METHODS A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status. A single sequence multi-scale feature fusion deep learning model (MSFF-IResnet) and a multi-scale and multimodality feature fusion model (MMFF-IResnet) were established based on the hepatobiliary phase (HBP), diffusion weighted imaging (DWI) sequences of enhanced MRI images, and the clinical features significantly correlated with CK19 status. The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery. RESULTS Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio (P=0.029) and incomplete tumor capsule (P=0.028) were independent predictors of CK19 expression in HCC. The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models, and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%, an accuracy of 80.6%, a sensitivity of 80.1% and a specificity of 81.2%. CONCLUSION The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC, demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.
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Affiliation(s)
- 威扬 方
- 南方医科大学生物医学工程学院，广东 广州 510500School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- 广东顺德创新设计研究院，广东 佛山 528300Guangdong Shunde Innovative Design Institute, Foshan 528300, China
| | - 慧 肖
- 南方医科大学生物医学工程学院，广东 广州 510500School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 爽 王
- 广东顺德创新设计研究院，广东 佛山 528300Guangdong Shunde Innovative Design Institute, Foshan 528300, China
| | - 晓明 林
- 广东顺德创新设计研究院，广东 佛山 528300Guangdong Shunde Innovative Design Institute, Foshan 528300, China
| | - 超敏 陈
- 南方医科大学生物医学工程学院，广东 广州 510500School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Liang Y, Xu F, Mou Q, Wang Z, Xiao C, Zhou T, Zhang N, Yang J, Wu H. A gadoxetic acid-enhanced MRI-based model using LI-RADS v2018 features for preoperatively predicting Ki-67 expression in hepatocellular carcinoma. BMC Med Imaging 2024; 24:27. [PMID: 38273242 PMCID: PMC10811868 DOI: 10.1186/s12880-024-01204-9] [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: 09/27/2023] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
PURPOSE To construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features. METHODS A total of 121 patients with HCC who underwent EOB-MRI were enrolled in this study. The patients were divided into three groups according to Ki-67 cut-offs: Ki-67 ≥ 20% (n = 86) vs. Ki-67 < 20% (n = 35); Ki-67 ≥ 30% (n = 73) vs. Ki-67 < 30% (n = 48); Ki-67 ≥ 50% (n = 45) vs. Ki-67 < 50% (n = 76). MRI features were analyzed to be associated with high Ki-67 expression using logistic regression to construct multivariable models. The performance characteristic of the models for the prediction of high Ki-67 expression was assessed using receiver operating characteristic curves. RESULTS The presence of mosaic architecture (p = 0.045), the presence of infiltrative appearance (p = 0.039), and the absence of targetoid hepatobiliary phase (HBP, p = 0.035) were independent differential factors for the prediction of high Ki-67 status (≥ 50% vs. < 50%) in HCC patients, while no features could predict high Ki-67 status with thresholds of 20% (≥ 20% vs. < 20%) and 30% (≥ 30% vs. < 30%) (p > 0.05). Four models were constructed including model A (mosaic architecture and infiltrated appearance), model B (mosaic architecture and targetoid HBP), model C (infiltrated appearance and targetoid HBP), and model D (mosaic architecture, infiltrated appearance and targetoid HBP). The model D yielded better diagnostic performance than the model C (0.776 vs. 0.669, p = 0.002), but a comparable AUC than model A (0.776 vs. 0.781, p = 0.855) and model B (0.776 vs. 0.746, p = 0.076). CONCLUSIONS Mosaic architecture, infiltrated appearance and targetoid HBP were sensitive imaging features for predicting Ki-67 index ≥ 50% and EOB-MRI model based on LI-RADS v2018 features may be an effective imaging approach for the risk stratification of patients with HCC before surgery.
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Affiliation(s)
- Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China
| | - Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, 396 Tongfu Road, Guangzhou, Guangdong Province, 510220, China
| | - Qiuju Mou
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China
| | - Zihua Wang
- Department of Radiology, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong Province, 528000, China
| | - Chuyin Xiao
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China
| | - Tingwen Zhou
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China
| | - Nianru Zhang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China
| | - Jing Yang
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
| | - Hongzhen Wu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
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Zhou L, Chen Y, Li Y, Wu C, Xue C, Wang X. Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis. Front Oncol 2024; 13:1323534. [PMID: 38234405 PMCID: PMC10792117 DOI: 10.3389/fonc.2023.1323534] [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: 10/18/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Background Radiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC. Methods Original studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression. Results We identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86-0.92) in the training set, and 0.87 (95% CI: 0.82-0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81-0.90) in the training set, and 0.79 (95% CI: 0.73-0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I 2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased. Conclusion Radiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.
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Affiliation(s)
- Lu Zhou
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Yiheng Chen
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Yan Li
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Chaoyong Wu
- Shenzhen Hospital of Beijing University of Chinese Medicine, Shenzhen, China
| | - Chongxiang Xue
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Xihong Wang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China
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Marcu LG, Dell’Oro M, Bezak E. Opportunities in Cancer Therapies: Deciphering the Role of Cancer Stem Cells in Tumour Repopulation. Int J Mol Sci 2023; 24:17258. [PMID: 38139085 PMCID: PMC10744048 DOI: 10.3390/ijms242417258] [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: 11/18/2023] [Revised: 12/06/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Tumour repopulation during treatment is a well acknowledged yet still challenging aspect of cancer management. The latest research results show clear evidence towards the existence of cancer stem cells (CSCs) that are responsible for tumour repopulation, dissemination, and distant metastases in most solid cancers. Cancer stem cell quiescence and the loss of asymmetrical division are two powerful mechanisms behind repopulation. Another important aspect in the context of cancer stem cells is cell plasticity, which was shown to be triggered during fractionated radiotherapy, leading to cell dedifferentiation and thus reactivation of stem-like properties. Repopulation during treatment is not limited to radiotherapy, as there is clinical proof for repopulation mechanisms to be activated through other conventional treatment techniques, such as chemotherapy. The dynamic nature of stem-like cancer cells often elicits resistance to treatment by escaping drug-induced cell death. The aims of this scoping review are (1) to describe the main mechanisms used by cancer stem cells to initiate tumour repopulation during therapy; (2) to present clinical evidence for tumour repopulation during radio- and chemotherapy; (3) to illustrate current trends in the identification of CSCs using specific imaging techniques; and (4) to highlight novel technologies that show potential in the eradication of CSCs.
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Affiliation(s)
- Loredana G. Marcu
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA 5001, Australia;
- Faculty of Informatics and Science, University of Oradea, 410087 Oradea, Romania
| | - Mikaela Dell’Oro
- Australian Centre for Quantitative Imaging, School of Medicine, The University of Western Australia, Perth, WA 6009, Australia;
| | - Eva Bezak
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA 5001, Australia;
- Faculty of Chemistry & Physics, University of Adelaide, Adelaide, SA 5000, Australia
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Wu J, Liu W, Qiu X, Li J, Song K, Shen S, Huo L, Chen L, Xu M, Wang H, Jia N, Chen L. A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:549-564. [PMID: 38223688 PMCID: PMC10781918 DOI: 10.1007/s43657-023-00136-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/21/2023] [Accepted: 10/13/2023] [Indexed: 01/16/2024]
Abstract
It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00136-8.
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Affiliation(s)
- Jianmin Wu
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438 China
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Wanmin Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200333 China
| | - Xinyao Qiu
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 China
| | - Jing Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Kairong Song
- Department of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438 China
| | - Siyun Shen
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Lei Huo
- Department of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438 China
| | - Lu Chen
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Mingshuang Xu
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
| | - Hongyang Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438 China
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 China
| | - Ningyang Jia
- Department of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438 China
| | - Lei Chen
- The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, 200438 China
- National Center for Liver Cancer, Shanghai, 201805 China
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Chen Y, Chen J, Yang C, Wu Y, Wei H, Duan T, Zhang Z, Long L, Jiang H, Song B. Preoperative prediction of cholangiocyte phenotype hepatocellular carcinoma on contrast-enhanced MRI and the prognostic implication after hepatectomy. Insights Imaging 2023; 14:190. [PMID: 37962669 PMCID: PMC10645671 DOI: 10.1186/s13244-023-01539-x] [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: 06/12/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) expressing cytokeratin (CK) 7 or CK19 has a cholangiocyte phenotype that stimulates HCC proliferation, metastasis, and sorafenib therapy resistance This study aims to noninvasively predict cholangiocyte phenotype-positive HCC and assess its prognosis after hepatectomy. METHODS Between January 2010 and May 2022, preoperative contrast-enhanced MRI was performed on consecutive patients who underwent hepatectomy and had pathologically confirmed solitary HCC. Two abdominal radiologists separately assessed the MRI features. A predictive model for cholangiocyte phenotype HCC was created using logistic regression analysis and five-fold cross-validation. A receiver operating characteristic curve was used to calculate the model performance. Kaplan-Meier and log-rank methods were used to evaluate survival outcomes. RESULTS In total, 334 patients were included in this retrospective study. Four contrast-enhanced MRI features, including "rim arterial phase hyperenhancement" (OR = 5.9, 95% confidence interval [CI]: 2.9-12.0, 10 points), "nodule in nodule architecture" (OR = 3.5, 95% CI: 2.1-5.9, 7 points), "non-smooth tumor margin" (OR = 1.6, 95% CI: 0.8-2.9, 3 points), and "non-peripheral washout" (OR = 0.6, 95% CI: 0.3-1.0, - 3 points), were assigned to the cholangiocyte phenotype HCC prediction model. The area under the curves for the training and independent validation set were 0.76 and 0.73, respectively. Patients with model-predicted cholangiocyte phenotype HCC demonstrated lower rates of recurrence-free survival (RFS) and overall survival (OS) after hepatectomy, with an estimated median RFS and OS of 926 vs. 1565 days (p < 0.001) and 1504 vs. 2960 days (p < 0.001), respectively. CONCLUSIONS Contrast-enhanced MRI features can be used to predict cholangiocyte phenotype-positive HCC. Patients with pathologically confirmed or MRI model-predicted cholangiocyte phenotype HCC have a worse prognosis after hepatectomy. CRITICAL RELEVANCE STATEMENT Four contrast-enhanced MRI features were significantly associated with cholangiocyte phenotype HCC and a worse prognosis following hepatectomy; these features may assist in predicting prognosis after surgery and improve personalized treatment decision-making. KEY POINTS • Four contrast-enhanced MRI features were significantly associated with cholangiocyte phenotype HCC. • A noninvasive cholangiocyte phenotype HCC predictive model was established based on MRI features. • Patients with cholangiocyte phenotype HCC demonstrated a worse prognosis following hepatic resection.
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Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Zhen Zhang
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Liling Long
- Department of Radiology, the First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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Qi YM, Xiao EH. Advances in application of novel magnetic resonance imaging technologies in liver disease diagnosis. World J Gastroenterol 2023; 29:4384-4396. [PMID: 37576700 PMCID: PMC10415971 DOI: 10.3748/wjg.v29.i28.4384] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/03/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023] Open
Abstract
Liver disease is a major health concern globally, with high morbidity and mor-tality rates. Precise diagnosis and assessment are vital for guiding treatment approaches, predicting outcomes, and improving patient prognosis. Magnetic resonance imaging (MRI) is a non-invasive diagnostic technique that has been widely used for detecting liver disease. Recent advancements in MRI technology, such as diffusion weighted imaging, intravoxel incoherent motion, magnetic resonance elastography, chemical exchange saturation transfer, magnetic resonance spectroscopy, hyperpolarized MR, contrast-enhanced MRI, and ra-diomics, have significantly improved the accuracy and effectiveness of liver disease diagnosis. This review aims to discuss the progress in new MRI technologies for liver diagnosis. By summarizing current research findings, we aim to provide a comprehensive reference for researchers and clinicians to optimize the use of MRI in liver disease diagnosis and improve patient prognosis.
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Affiliation(s)
- Yi-Ming Qi
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410000, Hunan Province, China
| | - En-Hua Xiao
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410000, Hunan Province, China
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Xie Y, Liu Q, Ji C, Sun Y, Zhang S, Hua M, Liu X, Pan S, Hu W, Ma Y, Wang Y, Zhang X. An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study. Sci Rep 2023; 13:8673. [PMID: 37248363 PMCID: PMC10226996 DOI: 10.1038/s41598-023-35556-z] [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: 11/25/2022] [Accepted: 05/20/2023] [Indexed: 05/31/2023] Open
Abstract
Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) in terms of symptom relief and long-term survival. In contrast, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pretreatment prediction of the radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computed tomography. A total of 248 patients with advanced ESCC who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated superior performance, with AUCs of 0.876, 0.802 and 0.732 in the training, internal validation, and external validation cohorts, respectively. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and decision curve analysis. Herein, a novel pretreatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.
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Affiliation(s)
- Yuchen Xie
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qiang Liu
- Department of Computer Science and Communications Engineering, Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan
| | - Chao Ji
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shuliang Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Mingyu Hua
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xueting Liu
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shupei Pan
- Department of Radiation Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Weibin Hu
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yanfang Ma
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ying Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaozhi Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Pomohaci MD, Grasu MC, Dumitru RL, Toma M, Lupescu IG. Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13091663. [PMID: 37175054 PMCID: PMC10178485 DOI: 10.3390/diagnostics13091663] [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: 03/08/2023] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. However, due to organ shortages, this treatment can only be applied to carefully selected patients according to clinical guidelines. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging, like radiology. With the help of these technologies, laborious tasks can be automated, and new lesion imaging criteria can be developed based on pixel-level analysis. Our objectives are to review the developing AI applications that could be implemented to better stratify liver transplant candidates. The papers analysed applied AI for liver segmentation, evaluation of steatosis, sarcopenia assessment, lesion detection, segmentation, and characterization. A liver transplant is an optimal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease. Furthermore, AI could provide solutions for improving the management of liver transplant candidates to improve survival.
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Affiliation(s)
- Mihai Dan Pomohaci
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mugur Cristian Grasu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Radu Lucian Dumitru
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mihai Toma
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Ioana Gabriela Lupescu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
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Panic J, Defeudis A, Balestra G, Giannini V, Rosati S. Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:67-76. [PMID: 37283773 PMCID: PMC10241248 DOI: 10.1109/ojemb.2023.3271455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.
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Affiliation(s)
- Jovana Panic
- Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin10129TurinItaly
| | - Arianna Defeudis
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Gabriella Balestra
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
| | - Valentina Giannini
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Samanta Rosati
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
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Gadoxetic Acid-Enhanced MRI-Based Radiomics Signature: A Potential Imaging Biomarker for Identifying Cytokeratin 19-Positive Hepatocellular Carcinoma. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:5424204. [PMID: 36814805 PMCID: PMC9940957 DOI: 10.1155/2023/5424204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/19/2023] [Accepted: 01/27/2023] [Indexed: 02/16/2023]
Abstract
Purpose One subtype of hepatocellular carcinoma (HCC), with cytokeratin 19 expression (CK19+), has shown to be more aggressive and has a poor prognosis. However, CK19+ is determined by immunohistochemical examination using a surgically resected specimen. This study is aimed at establishing a radiomics signature based on preoperative gadoxetic acid-enhanced MRI for predicting CK19 status in HCC. Patients and Methods. Clinicopathological and imaging data were retrospectively collected from patients who underwent hepatectomy between February 2015 and December 2020. Patients who underwent gadoxetic acid-enhanced MRI and had CK19 results of histopathological examination were included. Radiomics features of the manually segmented lesion during the arterial, portal venous, and hepatobiliary phases were extracted. The 10 most reproducible and robust features at each phase were selected for construction of radiomics signatures, and their performance was evaluated by analyzing the area under the curve (AUC). The goodness of fit of the model was assessed by the Hosmer-Lemeshow test. Results A total of 110 patients were included. The incidence of CK19(+) HCC was 17% (19/110). Alpha fetoprotein was the only significant clinicopathological variable different between CK19(-) and CK19(+) groups. A majority of the selected radiomics features were wavelet filter-derived features. The AUCs of the three radiomics signatures based on arterial, portal venous, and hepatobiliary phases were 0.70 (95% CI: 0.56-0.83), 0.83 (95% CI: 0.73-0.92), and 0.89 (95% CI: 0.82-0.96), respectively. The three radiomics signatures were integrated, and the fusion signature yielded an AUC of 0.92 (95% CI: 0.86-0.98) and was used as the final model for CK19(+) prediction. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the fusion signature was 0.84, 0.89, 0.88, 0.62, and 0.96, respectively. The Hosmer-Lemeshow test showed a good fit of the fusion signature (p > 0.05). Conclusion The established radiomics signature based on preoperative gadoxetic acid-enhanced MRI could be an accurate and potential imaging biomarker for HCC CK19(+) prediction.
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Zhao Y, Tan X, Chen J, Tan H, Huang H, Luo P, Liang Y, Jiang X. Preoperative prediction of cytokeratin-19 expression for hepatocellular carcinoma using T1 mapping on gadoxetic acid-enhanced MRI combined with diffusion-weighted imaging and clinical indicators. Front Oncol 2023; 12:1068231. [PMID: 36741705 PMCID: PMC9893005 DOI: 10.3389/fonc.2022.1068231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/19/2022] [Indexed: 01/20/2023] Open
Abstract
Objectives To explore the value of T1 mapping on gadoxetic acid-enhanced magnetic resonance imaging (MRI) in preoperative predicting cytokeratin 19 (CK19) expression for hepatocellular carcinoma (HCC). Methods This retrospective study included 158 patients from two institutions with surgically resected treatment-native solitary HCC who underwent preoperative T1 mapping on gadoxetic acid-enhanced MRI. Patients from institution I (n = 102) and institution II (n = 56) were assigned to training and test sets, respectively. univariable and multivariable logistic regression analyses were performed to investigate the association of clinicoradiological variables with CK19. The receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the performance for CK19 prediction. Then, a prediction nomogram was developed for CK19 expression. The performance of the prediction nomogram was evaluated by its discrimination, calibration, and clinical utility. Results Multivariable logistic regression analysis showed that AFP>400ng/ml (OR=4.607, 95%CI: 1.098-19.326; p=0.037), relative apparent diffusion coefficient (rADC)≤0.71 (OR=3.450, 95%CI: 1.126-10.567; p=0.030), T1 relaxation time in the 20-minute hepatobiliary phase (T1rt-HBP)>797msec (OR=4.509, 95%CI: 1.301-15.626; p=0.018) were significant independent predictors of CK19 expression. The clinical-quantitative model (CQ-Model) constructed based on these significant variables had the best predictive performance with an area under the ROC curve of 0.844, an area under the PR curve of 0.785 and an F1 score of 0.778. The nomogram constructed based on CQ-Model demonstrated satisfactory performance with C index of 0.844 (95%CI: 0.759-0.908) and 0.818 (95%CI: 0.693-0.902) in the training and test sets, respectively. Conclusions T1 mapping on gadoxetic acid-enhanced MRI has good predictive efficacy for preoperative prediction of CK19 expression in HCC, which can promote the individualized risk stratification and further treatment decision of HCC patients.
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Affiliation(s)
- Yue Zhao
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China,Department of Radiology, Guangzhou First People’s Hospital, Guangzhou, China
| | - Xiaoliang Tan
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jingmu Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongweng Tan
- Department of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, China
| | - Huasheng Huang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Peng Luo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yongsheng Liang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China,Department of Radiology, Guangzhou First People’s Hospital, Guangzhou, China,*Correspondence: Xinqing Jiang,
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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Zhang L, Zhou H, Zhang X, Ding Z, Xu J. A radiomics nomogram for predicting cytokeratin 19-positive hepatocellular carcinoma: a two-center study. Front Oncol 2023; 13:1174069. [PMID: 37182122 PMCID: PMC10174303 DOI: 10.3389/fonc.2023.1174069] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/13/2023] [Indexed: 05/16/2023] Open
Abstract
Objectives We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid-enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19-positive (+) hepatocellular carcinoma (HCC) and patients' prognosis. Methods A two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan-Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort. Results By combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (-) group were 81% and 77%, and 80% and 74%, respectively. Kaplan-Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients. Conclusion The combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment.
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Affiliation(s)
- Liqing Zhang
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Heshan Zhou
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoqian Zhang
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University, Shulan International Medical College, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhongxiang Ding, ; Jianfeng Xu,
| | - Jianfeng Xu
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University, Shulan International Medical College, Hangzhou, China
- *Correspondence: Zhongxiang Ding, ; Jianfeng Xu,
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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Liu Z, Yang S, Chen X, Luo C, Feng J, Chen H, Ouyang F, Zhang R, Li X, Liu W, Guo B, Hu Q. Nomogram development and validation to predict Ki-67 expression of hepatocellular carcinoma derived from Gd-EOB-DTPA-enhanced MRI combined with T1 mapping. Front Oncol 2022; 12:954445. [PMID: 36313692 PMCID: PMC9613965 DOI: 10.3389/fonc.2022.954445] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Objective As an important biomarker to reflect tumor cell proliferation and tumor aggressiveness, Ki-67 is closely related to the high early recurrence rate and poor prognosis, and pretreatment evaluation of Ki-67 expression possibly provides a more accurate prognosis assessment and more better treatment plan. We aimed to develop a nomogram based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) combined with T1 mapping to predict Ki-67 expression in hepatocellular carcinoma (HCC). Methods This two-center study retrospectively enrolled 148 consecutive patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI T1 mapping and surgically confirmed HCC from July 2019 to December 2020. The correlation between quantitative parameters from T1 mapping, ADC, and Ki-67 was explored. Three cohorts were constructed: a training cohort (n = 73) and an internal validation cohort (n = 31) from Shunde Hospital of Southern Medical University, and an external validation cohort (n = 44) from the Sixth Affiliated Hospital, South China University of Technology. The clinical variables and MRI qualitative and quantitative parameters associational with Ki-67 expression were analyzed by univariate and multivariate logistic regression analyses. A nomogram was developed based on these associated with Ki-67 expression in the training cohort and validated in the internal and external validation cohorts. Results T1rt-Pre and T1rt-20min were strongly positively correlated with Ki-67 (r = 0.627, r = 0.607, P < 0.001); the apparent diffusion coefficient value was moderately negatively correlated with Ki-67 (r = -0.401, P < 0.001). Predictors of Ki-67 expression included in the nomogram were peritumoral enhancement, peritumoral hypointensity, T1rt-20min, and tumor margin, while arterial phase hyperenhancement (APHE) was not a significant predictor even included in the regression model. The nomograms achieved good concordance indices in predicting Ki-67 expression in the training and two validation cohorts (0.919, 0.925, 0.850), respectively. Conclusions T1rt-Pre and T1rt-20min had a strong positive correlation with the Ki-67 expression in HCC, and Gd-EOB-DTPA enhanced MRI combined with T1 mapping-based nomogram effectively predicts high Ki-67 expression in HCC.
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Affiliation(s)
- Ziwei Liu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Shaomin Yang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
- Department of Radiology, The Affiliated Shunde Hospital of Guangzhou Medical University, Foshan, China
| | - Xinjie Chen
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Chun Luo
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Jieying Feng
- Department of Radiology, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China
| | - Haixiong Chen
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Fusheng Ouyang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Rong Zhang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Xiaohong Li
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Wei Liu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Baoliang Guo
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
- *Correspondence: Baoliang Guo, ; Qiugen Hu,
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
- *Correspondence: Baoliang Guo, ; Qiugen Hu,
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Zhang L, Qi Q, Li Q, Ren S, Liu S, Mao B, Li X, Wu Y, Yang L, Liu L, Li Y, Duan S, Zhang L. Ultrasomics prediction for cytokeratin 19 expression in hepatocellular carcinoma: A multicenter study. Front Oncol 2022; 12:994456. [PMID: 36119507 PMCID: PMC9478580 DOI: 10.3389/fonc.2022.994456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The purpose of this study was to investigate the preoperative prediction of Cytokeratin (CK) 19 expression in patients with hepatocellular carcinoma (HCC) by machine learning-based ultrasomics. Methods We retrospectively analyzed 214 patients with pathologically confirmed HCC who received CK19 immunohistochemical staining. Through random stratified sampling (ratio, 8:2), patients from institutions I and II were divided into training dataset (n = 143) and test dataset (n = 36), and patients from institution III served as external validation dataset (n = 35). All gray-scale ultrasound images were preprocessed, and then the regions of interest were then manually segmented by two sonographers. A total of 1409 ultrasomics features were extracted from the original and derived images. Next, the intraclass correlation coefficient, variance threshold, mutual information, and embedded method were applied to feature dimension reduction. Finally, the clinical model, ultrasonics model, and combined model were constructed by eXtreme Gradient Boosting algorithm. Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results A total of 12 ultrasomics signatures were used to construct the ultrasomics models. In addition, 21 clinical features were used to construct the clinical model, including gender, age, Child-Pugh classification, hepatitis B surface antigen/hepatitis C virus antibody (positive/negative), cirrhosis (yes/no), splenomegaly (yes/no), tumor location, tumor maximum diameter, tumor number, alpha-fetoprotein, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, glutamyl-transpeptidase, albumin, total bilirubin, conjugated bilirubin, creatinine, prothrombin time, fibrinogen, and international normalized ratio. The AUC of the ultrasomics model was 0.789 (0.621 – 0.907) and 0.787 (0.616 – 0.907) in the test and validation datasets, respectively. However, the performance of the combined model covering clinical features and ultrasomics signatures improved significantly. Additionally, the AUC (95% CI), sensitivity, specificity, and accuracy were 0.867 (0.712 – 0.957), 0.750, 0.875, 0.861, and 0.862 (0.703 – 0.955), 0.833, 0.862, and 0.857 in the test dataset and external validation dataset, respectively. Conclusion Ultrasomics signatures could be used to predict the expression of CK19 in HCC patients. The combination of clinical features and ultrasomics signatures showed excellent effects, which significantly improved prediction accuracy and robustness.
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Affiliation(s)
- Linlin Zhang
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Qinghua Qi
- Department of Ultrasound, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qian Li
- Department of Ultrasound, Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Shanshan Ren
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Shunhua Liu
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Bing Mao
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xin Li
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yuejin Wu
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Lanling Yang
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Luwen Liu
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yaqiong Li
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Shaobo Duan
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
- Department of Health Management, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- *Correspondence: Lianzhong Zhang, ; Shaobo Duan,
| | - Lianzhong Zhang
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
- *Correspondence: Lianzhong Zhang, ; Shaobo Duan,
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