Radiomics for the Discrimination of Infiltrative vs In Situ Breast Cancer

Breast cancer is the most common malignant tumor in women worldwide. Its early diagnosis relies on radiology and clinical evaluation, supplemented by biopsy confirmation. Technological advances in medical imaging, especially in the field of artificial intelligence, allow to address clinical challenges in cancer detection and classification, as well as in the assessment of treatment response, and in monitoring disease progression. Radiomics allows to extract features from images, related to tumor size, shape, intensity, and texture, providing comprehensive tumor characterization. In this paper, we briefly review some Radiomics approaches in breast cancer, focusing on the non-invasive distinction between in-situ and infiltrating breast tumors, and present a preliminary test using Radiomics signatures in DCE-MRI and machine learning, aimed to investigate the feasibility of distinguishing infiltrating cancer from ductal carcinoma in situ (DCIS) diagnosed by preoperative core needle biopsy.