Exploration of the Application of Multimodal Feature Analysis Based on Random Forest Algorithm Combining Ultrasound...

This study aimed to build a multimodal ultrasound (color Doppler flow imaging/shear wave elastography/contrast-enhanced ultrasound) combined with machine learning (ML) model, evaluating random forest (RF) for early ovarian malignancy diagnosis.

A retrospective analysis included 130 patients (72 benign, 58 malignant) pathologically confirmed ovarian lesions. Patients were split 7:3 into training/test sets. Data included demographics, lab tests and ultrasound features (32 variables). Key predictors were selected via univariate analysis, RF-recursive feature elimination and multivariate logistic regression. Six ML models were built and evaluated with 10-fold cross-validation.

Significant differences were observed between the benign and malignant groups in the training set for 24 indicators (all p < 0.005), including age, menopausal, CA125 and so on. After feature selection, five core predictors were identified: Peak intensity (PIy), maximum elasticity (Emax), CA125, human epididymis protein 4 (HE4) and internal composition. The RF model achieved area under the curves of 0.986 (training set) and 0.886 (test set), significantly outperforming other algorithms. Decision curve analysis demonstrated its highest net benefit within the 0-0.74 threshold probability range and the lowest Brier score (0.014 for training, 0.128 for test). SHapley Additive exPlanations (SHAP) analysis revealed that Emax, PIy and internal composition were the key features influencing model decisions, with the solid component having the largest impact on the malignant probability (ΔSHAP = -0.125).

The multimodal ultrasound-RF model constructed in this study exhibits excellent diagnostic performance and quantifies the contribution of key features, providing a reliable imaging tool for the early and precise diagnosis of ovarian malignancies.