Related Experiment Video
Updated: Jul 28, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Predictive Model for the Diagnosis of Benign/Malignant Complex Cystic and Solid Breast Nodules
Han Liu1, Chun-Jie Hou1, Jing-Lan Tang1
1Cancer Center, Department of Ultrasound Medicine, Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, 310011 Hangzhou, Zhejiang, China.
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×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (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.
