AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery

• URL: http://arxiv.org/abs/2509.06396v1
• Date: Mon, 08 Sep 2025 07:29:45 GMT
• Title: AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery
• Authors: Lorenz Achim Kuhn, Daniel Abler, Jonas Richiardi, Andreas F. Hottinger, Luis Schiappacasse, Vincent Dunet, Adrien Depeursinge, Vincent Andrearczyk,
• Abstract summary: Brain Metastases (BM) are a large contributor to mortality of patients with cancer.<n>They are treated with Stereotactic Radiosurgery (SRS) and monitored with Magnetic Resonance Imaging (MRI) at regular follow-up intervals according to treatment guidelines.<n> Response to treatment in longitudinal imaging is being studied, to better understand growth trajectories and predict treatment success or toxicity as early as possible.<n>In this study, we implement an automated pipeline to curate a large longitudinal dataset of SRS treatment data, resulting in a cohort of 896 BMs in 177 patients who were monitored for >360 days at approximately two-month intervals at Lausanne
• Score: 1.3844235712582453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain Metastases (BM) are a large contributor to mortality of patients with cancer. They are treated with Stereotactic Radiosurgery (SRS) and monitored with Magnetic Resonance Imaging (MRI) at regular follow-up intervals according to treatment guidelines. Analyzing and quantifying this longitudinal imaging represents an intractable workload for clinicians. As a result, follow-up images are not annotated and merely assessed by observation. Response to treatment in longitudinal imaging is being studied, to better understand growth trajectories and ultimately predict treatment success or toxicity as early as possible. In this study, we implement an automated pipeline to curate a large longitudinal dataset of SRS treatment data, resulting in a cohort of 896 BMs in 177 patients who were monitored for >360 days at approximately two-month intervals at Lausanne University Hospital (CHUV). We use a data-driven clustering to identify characteristic trajectories. In addition, we predict 12 months lesion-level response using classical as well as graph machine learning Graph Machine Learning (GML). Clustering revealed 5 dominant growth trajectories with distinct final response categories. Response prediction reaches up to 0.90 AUC (CI95%=0.88-0.92) using only pre-treatment and first follow-up MRI with gradient boosting. Similarly, robust predictive performance of up to 0.88 AUC (CI95%=0.86-0.90) was obtained using GML, offering more flexibility with a single model for multiple input time-points configurations. Our results suggest potential automation and increased precision for the comprehensive assessment and prediction of BM response to SRS in longitudinal MRI. The proposed pipeline facilitates scalable data curation for the investigation of BM growth patterns, and lays the foundation for clinical decision support systems aiming at optimizing personalized care.

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