Rethinking Breast Imaging Reporting and Data System (BI-RADS) 4: Clinical Insights From Mammographic Subclassification

Introduction: This study assesses the predictive value of clinical factors and Breast Imaging Reporting and Data System (BI-RADS) 4 subcategories in identifying malignant breast lesions. A retrospective review of women with BI-RADS 4 lesions undergoing biopsy evaluated histopathological outcomes in correlation with imaging subcategories and clinical parameters. The predictive performance of subclassification and relevant patient factors was analyzed to refine risk assessment for malignancy. Methods: A review of 190 women with BI-RADS 4 lesions identified between January 2022 and August 2025 was performed. Palpable lesions underwent excision and frozen section, while non-palpable lesions had wire-guided biopsy. Histopathology was correlated with imaging subcategories and clinical variables. Statistical analyses included chi-square tests and logistic regression. Results: Of 190 lesions, 136 (71.6%) were 4a, 32 (16.8%) were 4b, and 22 (11.6%) were 4c. Final histology revealed 49 invasive carcinomas (25.8%), nine (4.7%) ductal carcinoma in situ, and 132 (69.4%) benign lesions. Malignancy rates were 18.4% for 4a, 31.3% for 4b, and 63.6% for 4c. Non-palpable lesion malignancy increased with subcategory (16.7%, 38.9%, and 100%). Age >50 significantly predicted malignancy (35.5% vs. 16.5%, OR: 3.0, p = 0.003). Prior breast cancer history showed a negative association (OR: 3.3, p = 0.001), especially in contralateral lesions. Diagnostic validity improved with higher BI-RADS subcategories, with 4c showing the best specificity (94.3%) and accuracy (77.4%). Fibroadenoma was the most common benign diagnosis (62.8%). Conclusion: Age over 50 years and prior breast cancer are strong independent predictors of malignancy in BI-RADS 4 lesions. Subcategories stratify risk and malignancy rates, especially in 4a, which exceeded expected ranges, underscoring limitations in current classification. Incorporating clinical history and radiomics into BI-RADS may refine risk prediction and reduce unnecessary biopsies.