CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study

2025-06-22

AI Summary

This study addresses the clinical challenge of differentiating benign from malignant endometrial lesions preoperatively. We developed and validated an interpretable radiomics model based on preoperative contrast-enhanced CT imaging. Radiomic features (n=1,132) were extracted using PyRadiomics and integrated into an ensemble of six machine learning algorithms; the optimized random forest classifier achieved an AUC of 0.96 on an independent test set (AUC=1.00 on the training set) across multicenter cohorts. To enhance clinical interpretability, we innovatively combined SHAP (Shapley Additive Explanations) analysis with feature atlas visualization. Decision curve analysis demonstrated substantial net clinical benefit, supporting improved risk stratification and reduction of unnecessary biopsies and surgical interventions. All top-ranked radiomic features showed statistically significant associations with histopathological diagnosis (P<0.05), underscoring the model’s biological plausibility and translational potential.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Aimed to develop and validate a CT radiomics-based explainable machine learning model for diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pre-surgical CT scans using Pyradiomics. Six explainable machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. The diagnostic performance of the radiomic model was evaluated by using sensitivity, specificity, accuracy, precision, F1 score, confusion matrices, and ROC curves. To enhance clinical understanding and usability, we separately implemented SHAP analysis and feature mapping visualization, and evaluated the calibration curve and decision curve. By comparing six modeling strategies, the Random Forest model emerged as the optimal choice for diagnosing EC, with a training AUC of 1.00 and a testing AUC of 0.96. SHAP identified the most important radiomic features, revealing that all selected features were significantly associated with EC (P < 0.05). Radiomics feature maps also provide a feasible assessment tool for clinical applications. DCA indicated a higher net benefit for our model compared to the "All" and "None" strategies, suggesting its clinical utility in identifying high-risk cases and reducing unnecessary interventions. In conclusion, the CT radiomics-based explainable machine learning model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.

Problem

• Differentiate malignant and benign endometrial tumors using CT radiomics
• Develop explainable machine learning model for endometrial cancer diagnosis
• Validate model performance with clinical data from two centers

Innovation

• CT radiomics-based machine learning model
• Explainable AI with SHAP analysis
• Random Forest for high diagnostic accuracy