What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Prediction of traetment response to neoadjuvant chemotherapy in breast cancer with deep learning for multi-time dynamic contrast-enhanced MRI

Key Points

The predictive model achieved an AUC between 0.7319 and 0.7411, indicating strong predictive performance.
Sensitivity levels ranged from 0.8000 to 0.8333, showcasing the model's ability to identify true positive responses to treatment.
Specificity varied from 0.5472 to 0.6491, demonstrating some limitations in correctly identifying non-responses.
Deep learning techniques analyzing longitudinal DCE-MRI data hold potential for informing personalized surgical strategies.

Abstract

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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