Purpose: To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs).

Methods: A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018–2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.

Results: A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (p = 0.03), cystic fluid transmission (p = 0.02), longitudinal diameter (p 0.001), and age (p = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24\times X_{12}+1.97\times X_{20}+0.40\times X_7+0.11\times X_0; M=\frac{e^z}{1}+e^z (M is the malignancy score, e = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model.

Conclusions: A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.