Keywords: Diagnosis/Prediction, Treatment Response
Motivation: Accurately predicting pCR after neoadjuvant chemotherapy for patients with breast cancer supports personalized care, allowing for tailored surgical strategies and informed decisions on the extent of tissue resection.
Goal(s): To develop a fully automated pipeline that leverages deep learning to predict response to neoadjuvant chemotherapy non-invasively using DCE-MRI images taken before and after NAC.
Approach: We designed a contrastive learning loss to extract features reflecting tumor dynamics to predict pCR.
Results: The proposed model achieved predictive performance on the external dataset with an AUC ranging from 0.7319 to 0.7411, sensitivity between 0.8000 and 0.8333, and specificity from 0.5472 to 0.6491.
Impact: Our study demonstrated that analyzing longitudinal DCE-MRI data before and after NAC, integrated with deep learning, can effectively predict breast cancer response to neoadjuvant chemotherapy. This approach holds promise for guiding personalized treatment planning.
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