ArticleBiomedical engineering online2025
CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.
Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Correction: CT radiomics‑based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two‑center study.Biomedical engineering online · 2026Article
- Radiomics-based MRI model for differentiating ovarian cystadenoma and cystadenocarcinoma.Frontiers in oncology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
objectivesThis study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.
methodsA 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, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). To enhance clinical understanding and usability, we separately implemented SHAP analysis and feature mapping visualization and evaluated the calibration curve and decision curve.
resultsBy 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.
conclusionCT 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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