Development and validation of a malignant risk prediction model for breast cystic-solid lesions based on clinical and ultrasound features

Accurate differentiation of benign and malignant breast cystic-solid lesions (BCSLs) is crucial due to the significant differences in their treatment strategies. This study aimed to develop and validate a malignant risk prediction model for BCSLs based on clinical and ultrasound (US) features. A total of 312 patients with BCSLs were enrolled and divided into a training cohort (218 cases) and a validation cohort (94 cases). The least absolute shrinkage and selection operator (LASSO) was used to screen the most critical clinical and US features, and a prediction model was constructed using logistic regression. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were applied to evaluate the model’s performance and clinical value. Five key features were identified through LASSO regression: age, maximum diameter, edge, calcification in the solid component, and vascularity in the solid component. A prediction model was subsequently established and visualized as a nomogram. Calibration curves demonstrated good consistency of the model. The area under the curve (AUC) values of the training and validation cohorts were 0.821 (95% CI = 0.764–0.879) and 0.837 (95% CI = 0.754–0.919), respectively. DCA indicated that the model had practical value in clinical application. The prediction model based on clinical and US features can accurately predict the malignant risk of BCSLs and optimize clinical decision-making.