Abstract
Introduction
Breast cancer remains a major global health burden, particularly in low- and middle-income countries. The American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) standardises breast imaging interpretation and guides clinical decision-making. However, evidence on its diagnostic performance in the Democratic Republic of the Congo (DRC) is limited.
Purpose
To assess the diagnostic and clinical contribution of BI-RADS in the management of suspicious breast lesions in the DRC using radiological–histopathological correlation.
Methods
A retrospective multicentre diagnostic accuracy study was conducted across five hospitals in the DRC (2014–2025). Patients with BI-RADS-classified breast imaging and histopathological confirmation were included. BI-RADS 4–5 were considered positive for malignancy, while BI-RADS 2–3 were negative or low suspicion. Histopathology served as the reference standard. Diagnostic performance was evaluated using sensitivity, specificity, predictive values, Youden’s index, and AUC. Multivariable logistic regression identified factors associated with malignancy.
Results
Of 3,447 patients, 3,046 BI-RADS 1 cases were excluded due to lack of histological indication, leaving 401 patients for analysis. Malignancy was confirmed in 66.6% (267/401). Mean age was 49.5 ± 13.9 years, and 96.5% presented with a palpable mass. Mammography showed a sensitivity of 93.3%, specificity of 70.9%, and AUC of 0.857. Ultrasound showed a sensitivity of 88.0%, specificity of 75.4%, and AUC of 0.840. Age ≥70 years, multiparity, and advanced clinical stage were independently associated with malignancy, while physical activity was protective.
Conclusion
BI-RADS demonstrates good diagnostic performance for suspicious breast lesions in the DRC. Mammography is more sensitive, while ultrasound is more specific, supporting their complementary use. Strengthening radiologist training, standardised reporting, and access to biopsy and pathology services is essential for optimising BI-RADS effectiveness in low-resource settings.
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Copyright (c) 2026 Numbi, N. M., Iyoto, D M., Mwale, N. M., Nyemabo, R. K., Molua, A. A., Luyeye, G. M.
