Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction

2026-07-06

AI Summary

This study addresses the significant variability in breast cancer patients’ response to neoadjuvant chemotherapy (NACT) and the urgent need for accurate prediction of pathological complete response (pCR). To this end, the authors propose a novel approach that, for the first time, employs a 3D spatiotemporal graph neural network to model longitudinal dynamic contrast-enhanced MRI (DCE-MRI) data. Their method explicitly captures temporal interactions across multiple imaging timepoints and incorporates three complementary self-supervised learning objectives to enable personalized treatment response prediction. Evaluated on the ISPY-2 dataset comprising 585 patients, the proposed framework substantially outperforms existing visual and self-supervised baselines, establishing a new state-of-the-art benchmark for pCR prediction. The authors further release their code and data processing library to support reproducible research in this domain.

Abstract

In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective treatment decision-making remains challenging, as therapeutic response can vary substantially across patients, calling for predictive models capable of accurately estimating individualized treatment response. To address this, we propose an imaging-based 3D spatio-temporal framework for treatment response prediction that integrates a state-of-the-art graph neural network with relational modeling of temporal interactions across timepoints alongside three novel complementary self-supervised treatment trajectory representation learning objectives. Experiments across a cohort of 585 patients from the public ISPY-2 dataset demonstrate that our method substantially outperforms both vision and self-supervised learning baselines across several classification metrics. Alongside establishing a breast cancer pCR prediction benchmark, we include a principled ablation of our method and further introduce and empirically assess the impact of the available number of DCE-MRI timepoints per patient trajectory and the inclusion of inter-scan time-differences. Overall, our study substantiates the utility of clinically meaningful longitudinal medical imagaging modeling for predicting NACT-induced pCR. We will publicly share our code repository and a user-friendly PyPI library for dataset curation upon publication, effectively promoting reproducible open-source research.

Problem

• treatment response prediction
• pathological complete response
• neoadjuvant chemotherapy
• longitudinal medical imaging
• breast cancer

Innovation

• graph neural network
• longitudinal medical imaging
• self-supervised learning
• treatment response prediction
• spatio-temporal modeling