Abstract
Patients with brain metastases (BMs) treated using stereotactic radiosurgery (SRS) undergo longitudinal MRI to monitor response, yet outcomes vary substantially between lesions across and within patients, reflecting biological heterogeneity and differences in radiosensitivity. Modeling response is challenging due to heterogeneous temporal dynamics and irregular follow-up schedules, with varying numbers of scans per patient. Conventional machine learning relying on fixed, regularly sampled inputs is therefore limited to predict BM response to SRS. We present a large retrospective post-SRS dataset of 4,766 lesions from 568 patients, monitored with contrast-enhanced T1-weighted MPRAGE and T2-weighted MRI over a mean/median duration of 316/214 days. We formulate lesion-level response prediction as (i) future complete response (CR) classification at flexible temporal horizons and (ii) survival analysis with time to complete response (TCR) as the event (53% event rate). We propose a time-aware framework for irregular longitudinal data based on gated recurrent units (GRU). Absolute time since SRS is explicitly encoded and integrated with dynamic lesion-level imaging features and patient-level static covariates. The model processes variable-length input sequences and supports flexible prediction horizons within a unified architecture. Our approach outperforms MLP and XGBoost baselines that require fixed input representations, achieving a C-index of up to 0.77 for TCR prediction and AUCs between 0.74 and 0.90 for CR classification. This work establishes a scalable framework for personalized longitudinal response modeling in SRS-treated BMs. Code will be released upon acceptance.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_009.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{AndVin_Dynamic_MICCAISAT2026,
author = { Andrearczyk, Vincent AND Kuhn, Lorenz Achim AND Richiardi, Jonas AND Hottinger, Andreas F. AND Schiappacasse, Luis AND Dunet, Vincent AND Depeursinge, Adrien},
title = { { Dynamic Modeling of Brain Metastasis Response with Multitask Temporal Learning } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}