Yong'ai Li1, Junming Jian2,3, Perry J Pickhardt4, Fenghua Ma5, Wei Xia2, Haiming Li6, Rui Zhang2, Shuhui Zhao7, Songqi Cai8, Xingyu Zhao2,3, Jiayi Zhang2, Guofu Zhang5, Jingxuan Jiang9, Yan Zhang10, Keying Wang11, Guangwu Lin12, Feng Feng13, Jing Lu1, Lin Deng1, Xiaodong Wu2, Jinwei Qiang1, Xin Gao2. 1. Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China. 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. 3. University of Science and Technology of China, Hefei, China. 4. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. 5. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China. 6. Department of Radiology, Cancer Hospital, Fudan University, Shanghai, China. 7. Department of Radiology, Xinhua Hospital, Medical College of Shanghai Jiao Tong University, Shanghai, China. 8. Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China. 9. Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China. 10. Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China. 11. Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. 12. Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China. 13. Department of Radiology, Cancer Hospital, Nantong University, Nantong, China.
Abstract
BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE: Retrospective study of eight clinical centers. POPULATION: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE: Level 4. TECHNICAL EFFICACY: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.
BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE: Retrospective study of eight clinical centers. POPULATION: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE: Level 4. TECHNICAL EFFICACY: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.