Prediction model for complex venous malformation complete remission after sirolimus using clinicopathological and CEUS parameters. Download PDF Abstract To develop and validate a machine learning model incorporating clinicopathological features and contrast-enhanced ultrasound (CEUS) parameters for predicting complete remission (CR) following sirolimus therapy in patients with complex venous malformations (CVM), thereby supporting individualized treatment decisions. A retrospective cohort of 260 CVM patients treated with sirolimus (January 2014-December 2024) was enrolled.
Patients were randomly allocated to training (n = 182) and validation (n = 78) sets (7:3 ratio). Baseline clinicopathological (e.g., maximum lesion diameter, volume), CEUS (time-to-peak [TTP], peak intensity [PI]), and laboratory (D-dimer, hemoglobin) data were collected. Univariate analysis and multivariate logistic regression were performed to identify independent predictors.
Later, random forest, logistic regression, and K-nearest neighbors models were constructed. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. SHAP values and a nomogram enhanced interpretability.
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Medical Disclaimer: This article is for informational purposes only and does not constitute medical advice. Always consult with qualified healthcare professionals for medical decisions and treatment options.
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