|
1
|
Wang D, Liu S, Fu J, Zhang P, Zheng S, Qiu B, Liu H, Ye Y, Guo J, Zhou Y, Jiang H, Yin S, He H, Xie C, Liu H. Correlation of K trans derived from dynamic contrast-enhanced MRI with treatment response and survival in locally advanced NSCLC patients undergoing induction immunochemotherapy and concurrent chemoradiotherapy. J Immunother Cancer 2024; 12:e008574. [PMID: 38910009 PMCID: PMC11328668 DOI: 10.1136/jitc-2023-008574] [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] [Accepted: 05/30/2024] [Indexed: 06/25/2024] Open
Abstract
PURPOSE This study aimed to investigate the prognostic significance of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters concerning tumor response following induction immunochemotherapy and survival outcomes in patients with locally advanced non-small cell lung cancer (NSCLC) who underwent immunotherapy-based multimodal treatments. MATERIAL AND METHODS Unresectable stage III NSCLC patients treated by induction immunochemotherapy, concurrent chemoradiotherapy (CCRT) with or without consolidative immunotherapy from two prospective clinical trials were screened. Using the two-compartment Extend Tofts model, the parameters including Ktrans, Kep, Ve, and Vp were calculated from DCE-MRI data. The apparent diffusion coefficient was calculated from diffusion-weighted-MRI data. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to assess the predictive performance of MRI parameters. The Cox regression model was used for univariate and multivariate analysis. RESULTS 111 unresectable stage III NSCLC patients were enrolled. Patients received two cycles of induction immunochemotherapy and CCRT, with or without consolidative immunotherapy. With the median follow-up of 22.3 months, the median progression-free survival (PFS) and overall survival (OS) were 16.3 and 23.8 months. The multivariate analysis suggested that Eastern Cooperative Oncology Group score, TNM stage and the response to induction immunochemotherapy were significantly related to both PFS and OS. After induction immunochemotherapy, 67 patients (59.8%) achieved complete response or partial response and 44 patients (40.2%) had stable disease or progressive disease. The Ktrans of primary lung tumor before induction immunochemotherapy yielded the best performance in predicting the treatment response, with an AUC of 0.800. Patients were categorized into two groups: high-Ktrans group (n=67, Ktrans＞164.3×10-3/min) and low-Ktrans group (n=44, Ktrans≤164.3×10-3/min) based on the ROC analysis. The high-Ktrans group had a significantly higher objective response rate than the low-Ktrans group (85.1% (57/67) vs 22.7% (10/44), p<0.001). The high-Ktrans group also presented better PFS (median: 21.1 vs 11.3 months, p=0.002) and OS (median: 34.3 vs 15.6 months, p=0.035) than the low-Ktrans group. CONCLUSIONS Pretreatment Ktrans value emerged as a significant predictor of the early response to induction immunochemotherapy and survival outcomes in unresectable stage III NSCLC patients who underwent immunotherapy-based multimodal treatments. Elevated Ktrans values correlated positively with enhanced treatment response, leading to extended PFS and OS durations.
Collapse
Affiliation(s)
- DaQuan Wang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - SongRan Liu
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Jia Fu
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - PengXin Zhang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - ShiYang Zheng
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Bo Qiu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Hui Liu
- United Imaging Healthcare, ShangHai, China
| | - YongQuan Ye
- United Imaging of Healthcare America, Houston, Texas, USA
| | - JinYu Guo
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Yin Zhou
- SuZhou TongDiao Company, Suzhou, China
| | | | - ShaoHan Yin
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - HaoQiang He
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - ChuanMiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Hui Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| |
Collapse
|
|
2
|
Wang KX, Yu J, Xu Q. Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer. BMC Med Imaging 2023; 23:77. [PMID: 37291527 PMCID: PMC10249234 DOI: 10.1186/s12880-023-01027-0] [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: 11/27/2022] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. METHODS This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (Ktrans, Kep and Ve) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). RESULTS The mean values of Ktrans and Ve in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in Ktrans skewness, Ktrans entropy, Ktrans kurtosis, and Ve maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The Ktrans kurtosis and Ktrans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. CONCLUSIONS Histogram Analysis of DCE-MRI Ktrans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores.
Collapse
Affiliation(s)
- Ke-Xin Wang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Gulou District, 300 Guangzhou Rd, Nanjing, 210029, Jiangsu, China
| | - Jing Yu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Gulou District, 300 Guangzhou Rd, Nanjing, 210029, Jiangsu, China
| | - Qing Xu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Gulou District, 300 Guangzhou Rd, Nanjing, 210029, Jiangsu, China.
| |
Collapse
|
|
3
|
Horvat N, El Homsi M, Miranda J, Mazaheri Y, Gollub MJ, Paroder V. Rectal MRI Interpretation After Neoadjuvant Therapy. J Magn Reson Imaging 2023; 57:353-369. [PMID: 36073323 PMCID: PMC9851947 DOI: 10.1002/jmri.28426] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 02/01/2023] Open
Abstract
In recent years, several key advances in the management of locally advanced rectal cancer have been made, including the implementation of total mesorectal excision as the standard surgical approach; use of neoadjuvant chemoradiotherapy in selected patients with a high risk of local recurrence, and finally, adoption of organ preservation strategies, through either local excision or nonoperative management in selected patients with clinical complete response following neoadjuvant chemoradiotherapy. This review aims to shed light on the role of rectal MRI in the assessment of treatment response after neoadjuvant therapy, which is especially important given the growing feasibility of nonoperative management. First, an overview of current neoadjuvant therapies and response assessment based on digital rectal examination, endoscopy, and MRI will be provided. Second, the use of a high-quality restaging rectal MRI protocol will be presented. Third, a step-by-step approach to assessing treatment response on restaging rectal MRI following neoadjuvant treatment will be outlined, acknowledging challenges faced by radiologists during MRI interpretation. Finally, research related to response assessment will be discussed. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.
Collapse
Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, Brazil
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc J. Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
|
4
|
Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time? Cancers (Basel) 2023; 15:cancers15020432. [PMID: 36672381 PMCID: PMC9857080 DOI: 10.3390/cancers15020432] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/12/2023] Open
Abstract
In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.
Collapse
|
|
5
|
Su GY, Liu J, Xu XQ, Lu MP, Yin M, Wu FY. Texture analysis of conventional magnetic resonance imaging and diffusion-weighted imaging for distinguishing sinonasal non-Hodgkin's lymphoma from squamous cell carcinoma. Eur Arch Otorhinolaryngol 2022; 279:5715-5720. [PMID: 35731296 DOI: 10.1007/s00405-022-07493-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/06/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE To evaluate the value of texture analysis (TA) of conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) in the differential diagnosis between sinonasal non-Hodgkin's lymphoma (NHL) and squamous cell carcinoma (SCC). METHODS Forty-two patients with sinonasal SCC and 30 patients with NHL were retrospectively enrolled. TAs were performed on T2-weighted image (T2WI), apparent diffusion coefficient (ADC) and contrast-enhanced T1-weighted image (T1WI). Texture parameters, including mean value, skewness, kurtosis, entropy and uniformity were obtained and compared between sinonasal SCC and NHL groups. Receiver-operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic value and identify the independent TA parameters. RESULTS The mean value and entropy of ADC, and mean value of contrast-enhanced T1WI were significantly lower in the sinonasal NHL group than those in the SCC group (all P < 0.05). ROC analysis indicated that the entropy of ADC had the best diagnostic performance (AUC 0.832; Sensitivity 0.95; Specificity 0.67; Cutoff value 6.522). Logistic regression analysis showed that the entropy of ADC (P = 0.002, OR = 26.990) was the independent parameter for differentiating sinonasal NHL from SCC. CONCLUSION TA parameters of conventional MRI and DWI, particularly the entropy value of ADC, might be useful in the differentiating diagnosis between sinonasal NHL and SCC.
Collapse
Affiliation(s)
- Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Jun Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Mei-Ping Lu
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Min Yin
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China.
| |
Collapse
|
|
6
|
Fang J, Sun W, Wu D, Pang P, Guo X, Yu C, Lu W, Tang G. Value of texture analysis based on dynamic contrast-enhanced magnetic resonance imaging in preoperative assessment of extramural venous invasion in rectal cancer. Insights Imaging 2022; 13:179. [DOI: 10.1186/s13244-022-01316-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 10/19/2022] [Indexed: 11/24/2022] Open
Abstract
Abstract
Objective
Accurate preoperative assessment of extramural vascular invasion (EMVI) is critical for the treatment and prognosis of rectal cancer. The aim of our research was to develop an assessment model by texture analysis for preoperative prediction of EMVI.
Materials and methods
This study enrolled 44 rectal patients as train cohort, 7 patients as validation cohort and 18 patients as test cohort. A total of 236 texture features from DCE MR imaging quantitative parameters were extracted for each patient (59 features of Ktrans, Kep, Ve and Vp), and key features were selected by least absolute shrinkage and selection operator regression (LASSO). Finally, clinical independent risk factors, conventional MRI assessment, and T-score were incorporated to construct an assessment model using multivariable logistic regression.
Results
The T-score calculated using the 4 selected key features were significantly correlated with EMVI (p < 0.010). The area under the receiver operating characteristic curve (AUC) was 0.797 for discriminating between EMVI-positive and EMVI-negative patients with a sensitivity of 88.2% and specificity of 70.4%. The conventional MRI assessment of EMVI had a sensitivity of 23.53% and a specificity of 96.30%. The assessment model showed a greatly improved performance with an AUC of 0.954 (sensitivity, 88.2%; specificity, 92.6%) in train cohort, 0.833 (sensitivity, 66.7%; specificity, 100%) in validation cohort and 0.877 in test cohort, respectively.
Conclusions
The assessment model showed an excellent performance in preoperative assessment of EMVI. It demonstrates strong potential for improving the accuracy of EMVI assessment and provide a reliable basis for individualized treatment decisions.
Collapse
|
|
7
|
Recent Advances in Functional MRI to Predict Treatment Response for Locally Advanced Rectal Cancer. CURRENT COLORECTAL CANCER REPORTS 2021. [DOI: 10.1007/s11888-021-00470-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
|
8
|
Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27:5306-5321. [PMID: 34539134 PMCID: PMC8409167 DOI: 10.3748/wjg.v27.i32.5306] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/13/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.
Collapse
Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesca Boccadifuoco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| |
Collapse
|
|
9
|
Su GY, Xu XQ, Zhou Y, Zhang H, Si Y, Shen MP, Wu FY. Texture analysis of dual-phase contrast-enhanced CT in the diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer. Acta Radiol 2021; 62:890-896. [PMID: 32757639 DOI: 10.1177/0284185120946711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Computed tomography texture analysis (CTTA) provides objective and quantitative information regarding tumor heterogeneity beyond visual inspection. However, no study has yet used CTTA to differentiate metastatic from non-metastatic cervical lymph node in patients with papillary thyroid cancer (PTC). PURPOSE To evaluate the value of texture analysis of dual-phase contrast-enhanced CT images in diagnosing cervical lymph node metastasis in patients with PTC. MATERIAL AND METHODS Metastatic (n = 27) and non-metastatic (n = 32) cervical lymph nodes were analyzed retrospectively. Texture analyses were performed on both arterial (A) and venous (V) phase CT images. Texture parameters, including mean gray-level intensity, skewness, kurtosis, entropy, and uniformity, were obtained and compared between groups. Receiver operating characteristic (ROC) curves analyses and multivariate logistic regression analysis were used in our study. RESULTS Metastatic lymph nodes showed significantly higher A-mean gray-level intensity, A-entropy, and lower A-kurtosis and V-kurtosis (all P < 0.001) than non-metastatic mimics. The ROC curve analyses indicated that A-kurtosis demonstrated an optimal diagnostic area under the curve (AUC; 0.884) and specificity (92.59%), while the A-mean gray-level intensity showed optimal diagnostic sensitivity (90.62%). Multivariate logistic regression analysis showed that A-mean gray-level intensity (P = 0.006, odds ratio [OR] = 24.297) and V-kurtosis (P = 0.014, OR = 19.651) were the independent predictor for metastatic cervical lymph node. CONCLUSION Dual-phase contrast-enhanced CCTA-especially A-mean gray-level intensity and V-kurtosis-may have the potential to diagnose metastatic cervical lymph node in patients with PTC.
Collapse
Affiliation(s)
- Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yan Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Hao Zhang
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yan Si
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Mei-Ping Shen
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| |
Collapse
|
|
10
|
Song L, Li C, Yin J. Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer. Front Oncol 2021; 11:675160. [PMID: 34168994 PMCID: PMC8217832 DOI: 10.3389/fonc.2021.675160] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/14/2021] [Indexed: 12/29/2022] Open
Abstract
Objective To evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer. Materials and Methods This study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis. Results Among the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SIslope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%). Conclusions Texture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.
Collapse
Affiliation(s)
- Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chunli Li
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
|
11
|
Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY, Ding Y. MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends. Cancer Manag Res 2021; 13:4317-4328. [PMID: 34103987 PMCID: PMC8179813 DOI: 10.2147/cmar.s309252] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/08/2021] [Indexed: 12/29/2022] Open
Abstract
Complete tumor response can be achieved in a certain proportion of patients with locally advanced rectal cancer, who achieve maximal response to neoadjuvant therapy (NAT). For these patients, a watch-and-wait (WW) or nonsurgical strategy has been proposed and is becoming widely practiced in order to avoid unnecessary surgical complications. Therefore, a non-invasive, reliable diagnostic tool for accurately evaluating complete tumor response is needed. Magnetic resonance imaging (MRI) plays a crucial role in both primary staging and restaging tumor response to NAT in rectal cancer without relying on resected specimen. In recent years, numerous efforts have been made to research the value of MRI in predicting and evaluating complete response in rectal cancer. Current MRI evaluation is mainly based on morphological and functional images. Morphologic MRI yields high soft tissue resolution, multiplanar images, and provides detailed depictions of rectal cancer and its surrounding structures. Functional MRI may help to distinguish residual tumor from fibrosis, therefore improving the diagnostic performance of morphologic MRI in identifying complete tumor response. Both morphologic and functional MRI have several promising parameters that may help accurately evaluate and/or predict complete response of rectal cancer. However, these parameters still have limitations and the results remain inconsistent. Recent development of new techniques, such as textural analysis, radiomics analysis and deep learning, demonstrate great potential based on MRI-derived parameters. This article aimed to review and help better understand the strengths, limitations, and future trends of these MRI-derived methods in evaluating complete response in rectal cancer.
Collapse
Affiliation(s)
- Qiaoyu Xu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yanyan Xu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Hongliang Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Tao Jiang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Bee Yen Ooi
- Department of Radiology, Hospital Seberang Jaya, Penang, Malaysia
| | - Yi Ding
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| |
Collapse
|
|
12
|
Shayesteh S, Nazari M, Salahshour A, Sandoughdaran S, Hajianfar G, Khateri M, Yaghobi Joybari A, Jozian F, Fatehi Feyzabad SH, Arabi H, Shiri I, Zaidi H. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer. Med Phys 2021; 48:3691-3701. [PMID: 33894058 DOI: 10.1002/mp.14896] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT). MATERIALS AND METHODS This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT. Ninety-six radiomic features, including intensity, morphological and second- and high-order texture features were extracted from segmented 3D volumes from T2W MRI. All features were harmonized using ComBat algorithm. Max-Relevance-Min-Redundancy (MRMR) algorithm was used as feature selector and k-nearest neighbors (KNN), Naïve Bayes (NB), Random forests (RF), and eXtreme Gradient Boosting (XGB) algorithms were used as classifiers. The evaluation was performed using the area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, specificity and accuracy. RESULTS In univariate analysis, the highest AUC in pre-, post-, and delta-radiomic features were 0.78, 0.70, and 0.71, for GLCM_IMC1, shape (surface area and volume) and GLSZM_GLNU features, respectively. In multivariate analysis, RF and KNN achieved the highest AUC (0.85 ± 0.04 and 0.81 ± 0.14, respectively) among pre- and post-treatment features. The highest AUC was achieved for the delta-radiomic-based RF model (0.96 ± 0.01) followed by NB (0.96 ± 0.04). Overall. Delta-radiomics model, outperformed both pre- and post-treatment features (P-value <0.05). CONCLUSION Multivariate analysis of delta-radiomic T2W MRI features using machine learning algorithms could potentially be used for response prediction in LARC patients undergoing nCRT. We also observed that multivariate analysis of delta-radiomic features using RF classifiers can be used as powerful biomarkers for response prediction in LARC.
Collapse
Affiliation(s)
- Sajad Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Salahshour
- Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran
| | - Saleh Sandoughdaran
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Centre, Iran University of Medical Science, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Yaghobi Joybari
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fariba Jozian
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
|
13
|
Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer. Eur Radiol 2021; 31:7031-7038. [PMID: 33569624 DOI: 10.1007/s00330-021-07724-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/24/2020] [Accepted: 01/27/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data. METHODS Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2Wvolume/T2Wentropy/ADCmean/SUVmean/TLG/CTmean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset. RESULTS When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture). CONCLUSION In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research. KEY POINTS • Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.
Collapse
|
|
14
|
Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer 2020; 20:52-71. [PMID: 33349519 DOI: 10.1016/j.clcc.2020.11.001] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 09/03/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023]
Abstract
Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.
Collapse
Affiliation(s)
- Femke C R Staal
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Denise J van der Reijd
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Marjaneh Taghavi
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| |
Collapse
|
|
15
|
Song L, Yin J. Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer. Front Oncol 2020; 10:1364. [PMID: 32850437 PMCID: PMC7426518 DOI: 10.3389/fonc.2020.01364] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/29/2020] [Indexed: 12/18/2022] Open
Abstract
Objective: To investigate the value of texture features derived from T2-weighted magnetic resonance imaging (T2WI) for predicting preoperative lymph node invasion (N stage) in rectal cancer. Materials and Methods: One hundred and eighty-two patients with histopathologically confirmed rectal cancer and preoperative magnetic resonance imaging were retrospectively analyzed, who were divided into high (N1-2) and low N stage (N0). Texture features were calculated from histogram, gray-level co-occurrence matrix, and gray-level run-length matrix from sagittal fat-suppression and oblique axial T2WI. Independent sample t-test or Mann-Whitney U-test were used for statistical analysis. Multivariate logistic regression analysis was conducted to build the predictive models. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Results: Energy (ENE), entropy (ENT), information correlation (INC), long-run emphasis (LRE), and short-run low gray-level emphasis (SRLGLE) extracted from sagittal fat-suppression T2WI, and ENE, ENT, INC, low gray-level run emphasis (LGLRE), and SRLGLE from oblique axial T2WI were significantly different between stage N0 and stage N1-2 tumors. The multivariate analysis for features from sagittal fat-suppression T2WI showed that higher SRLGLE and lower ENE were independent predictors of lymph node invasion. The model reached an area under ROC curve (AUC) of 0.759. The analysis for features from oblique axial T2WI showed that higher INC and SRLGLE were independent predictors. The model achieved an AUC of 0.747. The analysis for all extracted features showed that lower ENE from sagittal fat-suppression T2WI and higher INC and SRLGLE from oblique axial T2WI were independent predictors. The model showed an AUC of 0.772. Conclusions: Texture features derived from T2WI could provide valuable information for identifying the status of lymph node invasion in rectal cancer.
Collapse
Affiliation(s)
- Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
|
16
|
Alvarez-Jimenez C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, Marderstein E, Kalady MF, Purysko A, Willis JE, Stein S, Friedman K, Paspulati R, Delaney CP, Romero E, Madabhushi A, Viswanath SE. Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers (Basel) 2020; 12:cancers12082027. [PMID: 32722082 PMCID: PMC7463898 DOI: 10.3390/cancers12082027] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/29/2020] [Accepted: 07/03/2020] [Indexed: 02/06/2023] Open
Abstract
(1) Background: The relatively poor expert restaging accuracy of MRI in rectal cancer after neoadjuvant chemoradiation may be due to the difficulties in visual assessment of residual tumor on post-treatment MRI. In order to capture underlying tissue alterations and morphologic changes in rectal structures occurring due to the treatment, we hypothesized that radiomics texture and shape descriptors of the rectal environment (e.g., wall, lumen) on post-chemoradiation T2-weighted (T2w) MRI may be associated with tumor regression after neoadjuvant chemoradiation therapy (nCRT). (2) Methods: A total of 94 rectal cancer patients were retrospectively identified from three collaborating institutions, for whom a 1.5 or 3T T2w MRI was available after nCRT and prior to surgical resection. The rectal wall and the lumen were annotated by an expert radiologist on all MRIs, based on which 191 texture descriptors and 198 shape descriptors were extracted for each patient. (3) Results: Top-ranked features associated with pathologic tumor-stage regression were identified via cross-validation on a discovery set (n = 52, 1 institution) and evaluated via discriminant analysis in hold-out validation (n = 42, 2 institutions). The best performing features for distinguishing low (ypT0-2) and high (ypT3-4) pathologic tumor stages after nCRT comprised directional gradient texture expression and morphologic shape differences in the entire rectal wall and lumen. Not only were these radiomic features found to be resilient to variations in magnetic field strength and expert segmentations, a quadratic discriminant model combining them yielded consistent performance across multiple institutions (hold-out AUC of 0.73). (4) Conclusions: Radiomic texture and shape descriptors of the rectal wall from post-treatment T2w MRIs may be associated with low and high pathologic tumor stage after neoadjuvant chemoradiation therapy and generalized across variations between scanners and institutions.
Collapse
Affiliation(s)
- Charlems Alvarez-Jimenez
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (C.A.-J.); (J.T.A.); (K.B.); (K.F.); (A.M.)
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Jacob T. Antunes
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (C.A.-J.); (J.T.A.); (K.B.); (K.F.); (A.M.)
| | - Nitya Talasila
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (C.A.-J.); (J.T.A.); (K.B.); (K.F.); (A.M.)
| | - Justin T. Brady
- Department of General Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (J.T.B.); (S.S.)
| | - Jayakrishna Gollamudi
- Department of Abdominal Imaging, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Eric Marderstein
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH 44106, USA;
| | - Matthew F. Kalady
- Department of Colorectal Surgery, Cleveland Clinic, Cleveland, OH 44106, USA; (M.F.K.); (C.P.D.)
| | - Andrei Purysko
- Section of Abdominal Imaging and Nuclear Radiology Department, Cleveland Clinic, Cleveland, OH 44195, USA;
| | - Joseph E. Willis
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Sharon Stein
- Department of General Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (J.T.B.); (S.S.)
| | - Kenneth Friedman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (C.A.-J.); (J.T.A.); (K.B.); (K.F.); (A.M.)
| | - Rajmohan Paspulati
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Conor P. Delaney
- Department of Colorectal Surgery, Cleveland Clinic, Cleveland, OH 44106, USA; (M.F.K.); (C.P.D.)
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (C.A.-J.); (J.T.A.); (K.B.); (K.F.); (A.M.)
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH 44106, USA;
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (C.A.-J.); (J.T.A.); (K.B.); (K.F.); (A.M.)
- Correspondence:
| |
Collapse
|
|
17
|
Kamimura K, Nakajo M, Yoneyama T, Bohara M, Nakanosono R, Fujio S, Iwanaga T, Nickel MD, Imai H, Fukukura Y, Yoshiura T. Quantitative pharmacokinetic analysis of high-temporal-resolution dynamic contrast-enhanced MRI to differentiate the normal-appearing pituitary gland from pituitary macroadenoma. Jpn J Radiol 2020; 38:649-657. [PMID: 32162178 DOI: 10.1007/s11604-020-00942-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 02/26/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To evaluate the usefulness of high-temporal-resolution dynamic contrast-enhanced (DCE) MRI and quantitative pharmacokinetic analysis to differentiate the normal-appearing pituitary gland from a pituitary macroadenoma. MATERIALS AND METHODS Twenty-seven patients with macroadenomas underwent preoperative DCE-MRI with a temporal resolution of 5 s using compressed sensing to obtain pharmacokinetic parameters. Two independent observers localized the normal-appearing pituitary gland on post-contrast T1-weighted images before and after referring to the corresponding Ktrans maps. Agreements between the localizations and intraoperative findings were evaluated using the kappa statistics. The Mann-Whitney U test was used to compare the pharmacokinetic parameters of the normal-appearing pituitary gland and adenoma. RESULTS For both observers, the agreement between the MRI-based localization and the intraoperative findings increased after referring to the Ktrans maps (observer 1, 0.930-1; observer 2, 0.636-0.855). The normal-appearing pituitary gland had significantly higher Ktrans [/min] (1.50 ± 0.80 vs 0.58 ± 0.49, P < 0.0001), kep [/min] (3.19 ± 1.29 vs 2.15 ± 1.18, P = 0.0049), and ve (0.43 ± 0.15 vs 0.25 ± 0.17, P = 0.0003) than adenoma. CONCLUSION High-temporal-resolution DCE-MRI and quantitative pharmacokinetic analysis help accurately localize the normal-appearing pituitary gland in patients with macroadenomas. The normal-appearing pituitary gland was characterized by higher Ktrans, kep, and ve than macroadenoma. Dynamic contrast-enhanced MRI with high-temporal-resolution using compressed sensing was used for quantitative pharmacokinetic analysis of pituitary macroadenomas. An observer study, the use of Ktrans maps improved accuracy in localizing the normal-appearing pituitary gland. As compared to an adenoma, the normal-appearing pituitary gland had significantly higher Ktrans, kep, and ve values.
Collapse
Affiliation(s)
- Kiyohisa Kamimura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Masanori Nakajo
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tomohide Yoneyama
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Manisha Bohara
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Ryota Nakanosono
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Shingo Fujio
- Department of Neurosurgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Takashi Iwanaga
- Clinical Engineering Department Radiation Section, Kagoshima University Hospital, Kagoshima, Japan
| | | | | | - Yoshihiko Fukukura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| |
Collapse
|
|
18
|
Euler A, Blüthgen C, Wurnig MC, Jungraithmayr W, Boss A. Can texture analysis in ultrashort echo-time MRI distinguish primary graft dysfunction from acute rejection in lung transplants? A multidimensional assessment in a mouse model. J Magn Reson Imaging 2019; 51:108-116. [PMID: 31150142 DOI: 10.1002/jmri.26817] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 05/22/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Differentiation of early postoperative complications affects treatment options after lung transplantation. PURPOSE To assess if texture analysis in ultrashort echo-time (UTE) MRI allows distinction of primary graft dysfunction (PGD) from acute transplant rejection (ATR) in a mouse lung transplant model. STUDY TYPE Longitudinal. ANIMAL MODEL Single left lung transplantation was performed in two cohorts of six mice (strain C57BL/6) receiving six syngeneic (strain C57BL/6) and six allogeneic lung transplants (strain BALB/c (H-2Kd )). FIELD STRENGTH/SEQUENCE 4.7T small-animal MRI/eight different UTE sequences (echo times: 50-5000 μs) at three different postoperative timepoints (1, 3, and 7 days after transplantation). ASSESSMENT Nineteen different first- and higher-order texture features were computed on multiple axial slices for each combination of UTE and timepoint (24 setups) in each mouse. Texture features were compared for transplanted (graft) and contralateral native lungs between and within syngeneic and allogeneic cohorts. Histopathology served as a reference. STATISTICAL TESTS Nonparametric tests and correlation matrix analysis were used. RESULTS Pathology revealed PGD in the syngeneic and ATR in the allogeneic cohort. Skewness and low-gray-level run-length features were significantly different between PGD and ATR for all investigated setups (P < 0.03). These features were significantly different between graft and native lung in ATR for most setups (minimum of 20/24 setups; all P < 0.05). The number of significantly different features between PGD and ATR increased with elapsing postoperative time. Differences in significant features were highest for an echo-time of 1500 μs. DATA CONCLUSION Our findings suggest that texture analysis in UTE-MRI might be a tool for the differentiation of PGD and ATR in the early postoperative phase after lung transplantation. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:108-116.
Collapse
Affiliation(s)
- André Euler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Christian Blüthgen
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | | | - Andreas Boss
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| |
Collapse
|