2026-10-10T17:32:27Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33717790 2026-09-14T04:31:33Z category_388 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.pdf Danhong Chai (24867712) Fenghua Ma (14783455) Yan Chen (4308) Yapei Zhong (24867715) Guofu Zhang (463042) Zhaoxia Qian (1417342) Jinwei Qiang (3147915) Oncology and Carcinogenesis not elsewhere classified atypical endometrial hyperplasia deep learning endometrial carcinoma magnetic resonance imaging radiomics Background<p>Differentiating stage I endometrial carcinoma (EC) from atypical endometrial hyperplasia (AEH) before surgery is critical for guiding treatment decisions, yet current magnetic resonance imaging (MRI) assessment is limited by overlapping appearances and inter-observer variability.</p>Methods<p>In this retrospective, two-center study, 297 patients (153 stage I EC and 144 AEH) who underwent multiparametric MRI were included. Radiomics features were extracted from T2-weighted imaging with fat saturation (T2WI-FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. Feature selections were performed using univariate filtering, correlation analysis, least absolute shrinkage and selection operator (LASSO) and multivariate stepwise regression. A Radiomics model, a Clin+Rad model, and a DenseNet121-derived deep-feature model were constructed and compared for differentiating stage I EC from AEH. 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> 2026-09-14T04:31:33Z Dataset Dataset 10.3389/fonc.2026.1943943.s001 https://figshare.com/articles/dataset/Data_Sheet_1_MRI-derived_deep-feature_model_for_differentiating_stage_I_endometrial_carcinoma_from_atypical_endometrial_hyperplasia_a_two-center_retrospective_study_pdf/33717790 CC BY 4.0