{"ID":5937150,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T10:34:05.479686507Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04912","arxiv_id":"2607.04912","title":"Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction","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.","short_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 substantial...","url_abs":"https://arxiv.org/abs/2607.04912","url_pdf":"https://arxiv.org/pdf/2607.04912v1","authors":"[\"Johannes Kiechle\",\"Richard Osuala\",\"Daniel M. Lang\",\"Stefan M. Fischer\",\"Ivana Janíčková\",\"Karim Lekadir\",\"Julia A. Schnabel\",\"Jan C. Peeken\"]","published":"2026-07-06T10:39:14Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}