Development and External Testing of an Interpretable MRI-Based Machine Learning Model for Differentiating Endometrial Stromal Sarcoma from Cellular Leiomyoma.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Obstetrics and Gynecology Hospital of Fudan University, 128 Shenyang Road, Shanghai, China.
- Department of Radiology, Shanghai First Maternity and Infant Hospital, 2699 Gaoke West Road, Pudong New Area, Shanghai, China.
- Department of Pathology, Obstetrics and Gynecology Hospital of Fudan University, 128 Shenyang Road, Shanghai, China.
- Department of Radiology, Obstetrics and Gynecology Hospital of Fudan University, 128 Shenyang Road, Shanghai, China. [email protected].
- Department of Radiology, Obstetrics and Gynecology Hospital of Fudan University, 128 Shenyang Road, Shanghai, China. [email protected].
- Department of Radiology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, 2800 Gongwei Road, Shanghai, China. [email protected].
Abstract
This retrospective two-center diagnostic study was aimed at developing and externally testing an interpretable MRI-based machine learning model for differentiating endometrial stromal sarcoma (ESS) from cellular leiomyoma (CL). A total of 155 women with pathologically confirmed ESS or CL were assigned to a derivation cohort (n = 111) and an external testing cohort (n = 44). Clinical variables, qualitative MRI features, and apparent diffusion coefficient (ADC) histogram parameters were evaluated. Three MRI-derived predictors-irregular tumor margin, cystic areas on T2-weighted imaging, and mean ADC-were used to train six classifiers. The final support vector machine model with a radial basis function kernel was evaluated at a fixed threshold determined in the derivation cohort using receiver operating characteristic, calibration, decision curve, and Shapley additive explanations (SHAP) analyses. A reader study assessed the effect of model assistance. The model achieved areas under the curve of 0.843 (95% CI, 0.760-0.917) in the derivation cohort and 0.835 (95% CI, 0.669-0.960) in the external testing cohort. Calibration and decision curve analyses indicated acceptable performance and potential clinical utility. In an exploratory model-assisted reader analysis, junior radiologist specificity increased from 46.4 to 82.1% and accuracy from 52.3 to 77.3%. This interpretable three-feature model demonstrated promising performance across cohorts and may serve as an adjunctive tool for preoperative MRI assessment, although prospective multicenter validation is warranted.