Yangyang Kan1,2,3, Di Dong4,5, Yuchen Zhang6, Wenyan Jiang2,3, Nannan Zhao2,3, Lu Han2,3, Mengjie Fang4,5, Yali Zang4,5, Chaoen Hu4,5, Jie Tian4,5, Chunming Li6, Yahong Luo1,2,3. 1. Dalian Medical University, Dalian, P.R. China. 2. Cancer Hospital of China Medical University, Shenyang, P.R. China. 3. Liaoning Cancer Hospital & Institute, Shenyang, P.R. China. 4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China. 5. University of Chinese Academy of Sciences, Beijing, P.R. China. 6. University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China.
Abstract
BACKGROUND: Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. PURPOSE: To evaluate a radiomic signature of LN involvement based on sagittal T1 contrast-enhanced (CE) and T2 MRI sequences. STUDY TYPE: Retrospective. POPULATION: In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. FIELD STRENGTH/SEQUENCE: T1 CE and T2 MRI sequences at 3T. ASSESSMENT: The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. STATISTICAL TESTS: A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. RESULTS: The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. DATA CONCLUSIONS: A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.
BACKGROUND: Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. PURPOSE: To evaluate a radiomic signature of LN involvement based on sagittal T1 contrast-enhanced (CE) and T2 MRI sequences. STUDY TYPE: Retrospective. POPULATION: In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. FIELD STRENGTH/SEQUENCE: T1 CE and T2 MRI sequences at 3T. ASSESSMENT: The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. STATISTICAL TESTS: A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. RESULTS: The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. DATA CONCLUSIONS: A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancerpatients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.