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

• URL: http://arxiv.org/abs/2506.18106v2
• Date: Sat, 08 Nov 2025 11:45:18 GMT
• Title: CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
• Authors: Tingrui Zhang, Honglin Wu, Zekun Jiang, Yingying Wang, Rui Ye, Huiming Ni, Chang Liu, Jin Cao, Xuan Sun, Rong Shao, Xiaorong Wei, Yingchun Sun,
• Abstract summary: We developed a CT radiomics-based explainable machine learning model for precise diagnosing malignancy and benignity in endometrial cancer patients.<n>The diagnostic performance of the model was evaluated by using sensitivity, specificity, accuracy, precision, F1 score, AUROC, and AUPRC.<n>The Random Forest model emerged as the optimal choice for diagnosing EC, with a training AUROC of 1.00 and a testing AUROC of 0.96.
• Score: 11.010138196822888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aimed to develop and validate a CT radiomics-based explainable machine learning model for precise 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 (ML) 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, AUROC, and AUPRC. 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 AUROC of 1.00 and a testing AUROC 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. Decision Curve Analysis (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 ML model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.

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