Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study
Zihan Niu, Jiawei Tian, Haitao Ran, Wei-Dong Ren, Cai Chang, Jianjun Yuan, Chunsong Kang, Youbin Deng, Hui Wang, Baoming Luo, Shenglan Guo, Qi Zhou, Ensheng Xue, Weiwei Zhan, Qing Zhou, Jie Li, Ping Zhou, Chunquan Zhang, Man Chen, Ying Gu, Jinfeng Xu, Chen Wu, Yuhong Zhang, Hongqiao Wang, Jianchu Li, Hongyan Wang, Yuxin Jiang
Abstract
Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either "high risk" or "low risk" in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model. Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound