Diagnostic accuracy of a machine learning model using radiomics features from breast synthetic MRI

In breast magnetic resonance imaging (MRI), the differentiation between benign and malignant breast masses relies on the Breast Imaging Reporting and Data System Magnetic Resonance Imaging (BI-RADS-MRI) lexicon. While BI-RADS-MRI classification demonstrates high sensitivity, specificities vary. This study aimed to evaluate the feasibility of machine learning models utilizing radiomics features derived from synthetic MRI to distinguish benign from malignant breast masses. Patients who underwent breast MRI, including a multi-dynamic multi-echo (MDME) sequence using 3.0 T MRI, and had histopathologically diagnosed enhanced breast mass lesions were retrospectively included. Clinical features, lesion shape features, texture features, and textural evaluation metrics were extracted. Machine learning models were trained and evaluated, and an ensemble model integrating BI-RADS and the machine learning model was also assessed. A total of 199 lesions (48 benign, 151 malignant) in 199 patients were included in the cross-validation dataset, while 43 lesions (15 benign, 28 malignant) in 40 new patients were included in the test dataset. For the test dataset, the sensitivity, specificity, accuracy, and area under the curve (AUC) of the receiver operating characteristic for BI-RADS were 100%, 33.3%, 76.7%, and 0.667, respectively. The logistic regression model yielded 64.3% sensitivity, 80.0% specificity, 69.8% accuracy, and an AUC of 0.707. The ensemble model achieved 82.1% sensitivity, 86.7% specificity, 83.7% accuracy, and an AUC of 0.883. The AUC of the ensemble model was significantly larger than that of both BI-RADS and the machine learning model. The ensemble model integrating BI-RADS and machine learning improved lesion classification.