A Nomogram for Predicting Malignancy in Small and Non-parallel BI-RADS 4A Breast Lesions: A Novel Approach for Risk Stratification and Clinical Decision Support

Abstract Purpose Breast Imaging Reporting and Data System (BI-RADS) 4A breast lesions are often confusing for surgeons due to high false-positive outcomes. This study was conducted to analyze the factors of small and non-parallel BI-RADS 4A breast lesions and developed a predictive model to stratify the malignancy risk. Methods For this retrospective study, 282 patients were recruited in the First Affiliated Hospital of Nanjing Medical University from January 2020 to December 2023. Logistic regression analysis was used to identify risk factors and develop a predictive model to differentiate between benign and malignant BI-RADS 4A breast lesions. The effectiveness of the model was assessed by the receiver operating characteristic (ROC) curve and the decision curve analysis (DCA). Results The proportion of malignant tumors was 20.6% (58/282) in this study. A diagnostic model compromised age, menopausal status, and margin was built and shown as a nomogram. The area under the ROC curve was 0.747 and 0.741 in the training and test cohort, respectively. DCA demonstrated that the model could achieve benefits for patients. Moreover, we stratified the breast lesions into low-, medium- and high-risk groups according to the malignancy risk calculated by the model. Only 10% (5/50) and 4.8% (1/21) were malignant in the low-risk group in the training cohort and test cohort.