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 and complementary treatment trajectory representation learning objectives. Experiments across a cohort of 585 patients from the public ISPY2 dataset demonstrate that our method substantially outperforms both supervised and supervised representation 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 imaging modeling for predicting NACT-induced pCR. The code accompanying this work (\url{https://github.com/compai-lab/2026-miccai-grail-kiechle}) alongside a user-friendly Python library for data curation (\url{https://pypi.org/project/ISPY2-pCR-dataset}), is publicly available.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/GRAIL_004.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=Szyob3muFG
BibTex
@InProceedings{KieJoh_Graph_MICCAISAT2026,
author = { Kiechle, Johannes AND Osuala, Richard AND Lang, Daniel M. AND Fischer, Stefan M. AND Janíčková, Ivana AND Lekadir, Karim AND Schnabel, Julia A. AND Peeken, Jan C.},
title = { { Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17278},
month = {pending},
page = {pending}
}