Combining transvaginal ultrasound radiomics with clinical-ultrasound semantic features for the identification of high-risk endometrial lesions.

OBJECTIVE: Preoperative risk stratification of high-risk endometrial lesions remains a clinical challenge. This study aimed to preliminarily explore an integrated machine learning approach combining transvaginal ultrasound (TVUS) radiomics, clinical indicators, and ultrasound semantic attributes to assist in clinical triage. METHODS: TVUS images (n = 956) from 239 patients were retrospectively analyzed across two centers. Clinical and ultrasound semantic features were evaluated blindly. Within a patient-level 5-fold cross-validation scheme repeated across five independent random seeds, 1,125 radiomics features were extracted and selected via nested LASSO regression to compute a Rad-score. Frameworks using nine machine learning algorithms were evaluated via Area Under the Curve (AUC), DeLong tests, and Decision Curve Analysis (DCA). RESULTS: The optimal validation AUCs for the Radiomics, Clinical-Semantic, and Combined models were 0.7589, 0.8965, and 0.9077 (CatBoost), respectively. The Combined framework yielded a statistically significant increase in AUC values compared with the Radiomics model (P < 0.001) and showed significant differences over the Clinical-Semantic model across seven algorithms (all P < 0.05). Endometrial-myometrial junction appearance was identified as the primary predictor. DCA indicated that the combined model's soft-voting configuration optimized initial triage at a 0.20 threshold, while its CatBoost configuration sustained higher net benefit across medium-to-high risk thresholds (0.30-0.60). CONCLUSION: Integrating clinical-ultrasound semantic features with radiomics offers an objective, non-invasive approach to potentially assist in individualized risk stratification. This combined framework may provide a potential stratified decision-support pathway for further clinical evaluation.