Year: 2026
Type: article
Abstract: Neoadjuvant chemotherapy (NAC) can eliminate all invasive cancer in some breast cancer patients, achieving a pathologic complete response (pCR) that is associated with a better prognosis. Prediction of pCR from pre-treatment radiology imaging is challenging but could provide immense value in the treatment planning. We propose a novel multimodal deep learning approach that combines pre-treatment dynamic contrast-enhanced MRI and clinical data for predicting pCR before chemotherapy. It is the first to integrate attention-based multiple instance learning technique for slice aggregation and a self-supervised contrastive objective to align image and clinical embeddings. Our model was trained on 1491 patients across four cohorts with expert tumor segmentations. On a stratified hold-out test set, the multimodal model achieved AUC 0.83 (95% CI 0.78-0.89), outperforming late fusion without contrastive alignment and single-modality baselines. These results, obtained on the largest multi-cohort breast MRI dataset comprising four clinical trials, underscore the potential of contrastive learning-based multimodal AI models to improve pCR prediction. With further validation, our proposed approach could support pre-treatment decision-making by identifying patients likely to achieve pCR and those who may benefit from alternative regimens.
Authors: Manu Goyal, Tanmay Shukla, Saeed Hassanpour
Institution: Dartmouth College
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,690
Award: G2131560578
OpenAlex ID: W7204267744