Predictive value of digital breast tomosynthesis based intratumoral-peritumoral radiomics and clinical features for benign-malignant breast microcalcifications.

Objective: To investigate the value of clinical and radiomics features in distinguishing benign from malignant breast microcalcifications and develop a noninvasive model to reduce unnecessary biopsies. Methods: This retrospective study analyzed 181 microcalcification lesions from 172 patients. Lesions were divided into training/validation sets (7:3). Manual volumes of interest were delineated on digital breast tomosynthesis images. Radiomics features were extracted from intratumoral and peritumoral regions. Among 9 machine learning classifiers, the optimal performer was selected to develop 3 radiomics models: intratumoral model (RM0), intratumoral and peritumoral 1 mm model (RM1), intratumoral and peritumoral 3 mm model (RM3). Independent clinical risk factors identified by univariate and multivariate logistic regression were integrated with the optimal radiomics model to build a nomogram model (NM). Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and clinical impact curves. Results: RM0 performed best. Independent risk factors included: Breast Imaging Reporting and Data System (BI-RADS) category ( Conclusions: Single clinical features showed potential predictive value for microcalcifications, while the integrated model might not demonstrate adequate discriminative power. Advances in knowledge: This study highlights the need for noninvasive differentiation of microcalcifications and suggests that while individual clinical factors have value, combined modeling requires further optimization.