Curr Med Imaging. 2026 Feb 11. doi: 10.2174/0115734056407923251129144547. Online ahead of print.
ABSTRACT
Introduction The Breast Imaging Reporting and Data System (BI-RADS) category 4 is subdivided into 4A, 4B, and 4C to reflect varying levels of suspicion for malignancy. However, the predictive consistency of these subcategories remains debated, especially in underrepresented populations. This study aims to assess the correlation between BI-RADS 4 subcategories and histopathological outcomes in Mexican women, identifying additional demographic and imaging predictors of malignancy. Materials and Methods This retrospective cross-sectional study included 173 female patients with BI-RADS 4 lesions who underwent mammography and/or ultrasound, followed by histopathological confirmation. Data were collected from the Hospital General de México between January 2023 and May 2024. Associations between BI-RADS subcategories and malignancy, age, lesion laterality, and imaging features were analyzed using chi-square tests and ANOVA.
RESULTS: Among 173 patients, 41.6% had BI-RADS 4A lesions, 35.8% had 4B, and 22.5% had 4C. Malignancy rates increased progressively across subcategories: 7.5% (4A), 40.0% (4B), and 85.0% (4C) (p < 0.001). The mean age rose with BI-RADS level (42.1, 47.8, and 55.3 years for 4A, 4B, and 4C, respectively), although this trend was not statistically significant (p = 0.063). Nodules were the most frequent imaging finding (83.2%), and fibroadenomas were the most common benign diagnosis. Left-sided lesions were more frequently malignant (p = 0.034).
DISCUSSION: The BI-RADS 4 subcategorization showed a clinically meaningful, although not statistically significant, trend in malignancy risk. Lesion laterality emerged as a potential independent predictor of malignancy, warranting further investigation. The findings reinforce the complementary role of demographic and imaging variables in risk assessment.
CONCLUSION: The BI-RADS 4 subclassification aligns with increasing malignancy risk, supporting its clinical utility. However, variability in diagnostic outcomes suggests the need to integrate histopathological and demographic data. Lesion laterality may represent a novel factor in malignancy prediction among breast lesions. Sustainable Development Goals (SDGs) Keywords SDG 3 Good Health and Well-being; SDG 5 Gender Equality; SDG 10 Reduced Inequalities; SDG 9 Industry Innovation and Infrastructure; SDG 4 Quality Education; SDG 17 Partnerships for the Goals.
PMID:41691672 | DOI:10.2174/0115734056407923251129144547
