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.