Detection of Suspicious Lesions in Breast MRI: Radiomics Patch-based Granular Classification Approach

In addition to mammography and ultrasound, breast magnetic resonance imaging (MRI) is indicative in a number of cases. Both cost and low reader availability hinder the more common use of MRI. This paper builds on the previously published work on breast region segmentation in MRI and evaluates the possibility of automated detection of suspicious lesions in the breast region using radiomics breast tissue analysis. The processing pipeline assumes regular grid division of breast tissue and characterization of each image patch by radiomics features for further binary classification using Random Forest (RF) and XGBoost to differentiate between suspicious lesions and fibro-glandular tissue. The patch-wise results obtained reveal F1 scores ≥ 0.92, with balanced precision (0.94) and recall (0.95) for different patch sizes. Reassembling patch decisions supports positioning of the identified suspicious breast regions and patient-level decision making.Clinical relevance- With high incidence rates of breast cancer, an automated detection of suspicious lesions in MRI breast scans can offer a valuable support in patient prioritization and clinical decision making, especially in settings with low reader availability.