Embodiments predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BCa) from pre-treatment dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Embodiments compute, using a machine learning (ML) classifier, a first probability of response based on a set of radiomic features extracted from a tumoral region represented in a pre-treatment DCE-MRI image of a region of tissue (ROT) demonstrating BCa; extract patches from the tumoral region; provide the patches to a convolutional neural network (CNN); receive, from the CNN, a pixel-level localized patch probability of response; compute a second probability of response based on the pixel-level localized patch probability; compute a combined ML probability from the first and second probabilities; compute a final probability of response based on the combined ML probability and clinical information associated with the ROT; classify the ROT as a responder or non-responder based on the final probability of response; and display the classification.
PREDICTING NEO-ADJUVANT CHEMOTHERAPY RESPONSE FROM PRE-TREATMENT BREAST MAGNETIC RESONANCE IMAGING USING ARTIFICIAL INTELLIGENCE AND HER2 STATUS