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

• URL: http://arxiv.org/abs/2607.04912v1
• Date: Mon, 06 Jul 2026 10:39:14 GMT
• Title: Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
• Abstract summary: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes.<n>We propose an imaging-based 3D-temporal framework for treatment response prediction that integrates a state-of-the-art neural graph with temporal modeling.<n>Our study substantiates the utility of clinically meaningful longitudinal medical imagaging modeling for predicting NACT-induced pCR.
• Score: 2.9823166626381212
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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