In a groundbreaking advancement at the intersection of medical imaging and artificial intelligence, researchers have unveiled a CT radiomics-based explainable machine learning model that revolutionizes the diagnosis of endometrial tumors. This innovative approach is designed to differentiate malignant from benign conditions in patients with endometrial cancer, a malignancy notoriously challenging to diagnose accurately using conventional methods. Through a meticulous two-center study, scientists have demonstrated a highly precise, explainable, and clinically valuable diagnostic tool, which could significantly impact patient outcomes and decision-making in oncology.
The study involved 83 patients diagnosed with endometrial cancer across two medical centers, among whom 46 had malignant tumors, while 37 presented with benign conditions. The research team embarked on a comprehensive analysis, initially splitting the dataset into training and testing subsets to ensure robust model validation. This division was critical to prevent overfitting and to confirm the model’s generalizability. The training set consisted of 59 patients’ data, while the testing set included the remaining 24. Such a design is crucial in machine learning studies, particularly in medical diagnostics, where real-world applicability is paramount.
Central to the methodology was the extraction of an extensive array of 1,132 radiomic features from pre-surgical CT scans using the Pyradiomics platform. These features encapsulate complex quantitative information embedded in the images, far beyond what human eyes can perceive. Radiomics allows for the conversion of visual data into mineable high-dimensional data, representing tumor heterogeneity in terms of texture, shape, intensity, and wavelet features. This granularity enables a more detailed tissue characterization than traditional imaging interpretations.
The research team implemented six different explainable machine learning algorithms to determine the optimal model for classifying malignancy in endometrial tumors. Each algorithm was tested rigorously, with performance evaluated across multiple metrics including sensitivity, specificity, accuracy, precision, F1 score, and notably, the area under the receiver operating characteristic (AUROC) and precision-recall curves (AUPRC). Such comprehensive evaluation ensures that the model not only identifies tumors correctly but also balances false positives and false negatives effectively.
Among the six algorithms tested, the Random Forest model surfaced as the superior choice, showcasing exceptional diagnostic precision. Remarkably, it achieved an AUROC of 1.00 in the training set, indicating perfect discrimination ability, and maintained a strong AUROC of 0.96 in the independent testing set. This level of performance signals remarkable robustness and promises reliable real-world applications, marking an important step forward in non-invasive cancer diagnostics.
Beyond model accuracy, the study prioritized interpretability to foster clinical acceptance and utility. To this end, the researchers integrated SHAP (Shapley Additive Explanations) analysis, which elucidates the contribution of each radiomic feature to the model’s predictions. This approach not only identifies the most influential features but also provides clinicians with understandable insights into why a particular tumor is adjudged malignant or benign, addressing a usual black-box criticism in AI applications in medicine.
The SHAP analysis revealed that all radiomic features selected by the model were statistically significant (p < 0.05), reinforcing their relevance in distinguishing malignant from benign tumors. Moreover, the study introduced feature mapping visualization, a novel tool that overlays the critical radiomic features onto the original CT images. This visual representation bridges the gap between complex data analytics and clinical intuition, allowing physicians to see actionable patterns on familiar diagnostic images.
Another critical aspect explored was the assessment of the model’s clinical utility through decision curve analysis (DCA). The DCA demonstrated that the Random Forest model provided a higher net benefit compared to traditional strategies that either treat all patients as high risk (“All”) or none as affected (“None”). This indicates the model’s potential to refine risk stratification, reduce unnecessary interventions, and optimize personalized management pathways in endometrial cancer care.
Calibration curves were also examined, verifying the accuracy of predicted probabilities against observed outcomes. This step is essential to confirm that the model’s confidence scores can be trusted for clinical decision-making, thus supporting its integration as an intelligent auxiliary tool in diagnostic workflows. The combination of high performance, explainability, and clinical applicability underscores the promise of AI-enhanced radiomics in oncology.
Endometrial cancer diagnosis traditionally relies on histopathological examination following biopsy or surgical intervention, procedures that carry risks and delays in treatment initiation. The study’s non-invasive approach, grounded in CT imaging that is routinely available in many clinical settings, presents a compelling alternative or adjunct to current diagnostic paradigms. By harnessing machine learning to distill meaningful insights from imaging data, this technology has the potential to accelerate and refine diagnosis without added patient burden.
This research embodies a significant milestone in the precision medicine landscape, highlighting the synergy between advanced imaging techniques and cutting-edge AI tools. It opens avenues for applying similar strategies to other cancer types, where early and accurate delineation between malignant and benign lesions remains a clinical challenge. The explainable nature of the model ensures that its deployment in diverse healthcare environments can be met with confidence and transparency.
Future directions will likely include larger multi-institutional studies to further validate and refine the model across varied populations and imaging equipment. Integrating this tool into clinical decision support systems could help tailor individualized treatment plans, reduce healthcare costs by avoiding unnecessary procedures, and ultimately improve patient survival and quality of life. The fusion of radiomics and explainable ML promises to transform oncologic imaging into a powerful predictive medicine cornerstone.
In summary, the innovative CT radiomics-based explainable machine learning model developed by Zhang, Wu, Jiang, and colleagues represents a quantum leap forward in differentiating malignant from benign endometrial tumors. Its superior diagnostic performance, coupled with transparent interpretability and clear clinical benefit, sets a new standard for AI-aided cancer diagnosis. As this technology advances towards clinical implementation, it signals a new era in personalized oncology grounded in data-driven insights and sophisticated computational techniques.
Subject of Research: Development and validation of a CT radiomics-based explainable machine learning model to accurately differentiate malignant and benign endometrial tumors.
Article Title: CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.
Article References: Zhang, T., Wu, H., Jiang, Z. et al. CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study. BioMed Eng OnLine 24, 129 (2025). https://doi.org/10.1186/s12938-025-01462-w
Image Credits: AI Generated
DOI: 04 November 2025
