A Mammography-Based Radiomic Nomogram for Predicting Malignancy in Breast Suspicious Microcalcifications

Rationale and Objectives: Preoperative accurate identification of benign and malignant breast lesions is vital for patients to achieve individualized treatment. This study aimed to develop and validate a mammography-based radiomic nomogram for predicting malignant risk of breast suspicious microcalcifications (MCs). Materials and Methods: 496 patients with histologically confirmed breast suspicious MCs were randomly divided into the training set (n = 346) and validation set (n = 150). Radiomics features was extracted from the craniocaudal and mediolateral oblique images. Least absolute shrinkage and selection operator algorithm were used to select radiomics features, then radiomics score (Rad-score) was calculated. Univariate analysis was used to identify malignant MCs-related clinical independent risk factors. Multivariate logistic regression was used to establish a clinical-radiomics model by incorporating Rad-score and clinic factors. A nomogram was developed to visualize the clinical-radiomics model. The receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: The Rad-score was consisted of 29 optimal radiomics features. We developed a nomogram by incorporating Rad-score, menopause status, MCs morphology and distribution, the area under the curve value of the combined model was 0.926(95% confidence interval [CI]: 0.878-0.975) for the validation set. The calibration curves and DCA indicated the combined model had favorable calibration and clinical utility. Conclusion: The combined model could be considered as a potential imaging marker to predict malignant risk of breast suspicious MCs.