Data Sheet 1_MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.pdf

Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA).</p>Results<p>Seven radiomics features and eight deep-learning features were ultimately selected to construct the models. In the external validation cohort, the DenseNet121-derived deep-feature model achieved the numerically highest AUC of 0.854 (95% CI, 0.743-0.965), compared with 0.803 (95% CI, 0.673-0.933) for the Radiomics model and 0.773 (95% CI, 0.626-0.919) for the Clin+Rad model, although the pairwise differences in AUC were not statistically significant.

In addition, the DenseNet121-derived model showed a relatively greater net clinical benefit at threshold probabilities of approximately 0.15-0.80. SHAP analysis indicated that several influential deep features were derived from ADC images, suggesting that ADC-derived features contributed substantially to the model predictions.</p>Conclusions<p>The DenseNet121-derived model achieved the numerically highest AUC in the external validation cohort, although the pairwise differences were not statistically significant, indicating its potential as a non-invasive tool to support individualized decision-making.</p>