Frontiers in oncology · 2026
MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.
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Chai Danhong, Ma Fenghua, Chen Yan, Zhong Yapei, Zhang Guofu, Qian Zhaoxia, Qiang Jinwei
Аннотация
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.