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arXiv · 2603.13371

Agentic LLM Workflow for MR Spectroscopy Volume-of-Interest Placements in Brain Tumors

Sangyoon Lee·Francesca Branzoli·Małgorzata Marjańska·Patrick Bolan
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Abstract

Magnetic resonance spectroscopy (MRS) provides clinically valuable metabolic characterization of brain tumors, but its utility depends on accurate placement of the spectroscopy volume-of-interest (VOI). However, VOI placement typically has a broad operating window: for a given tumor there are multiple possible VOIs that would lead to high-quality MRS measurements. Thus, a VOI place-ment can be tuned for clinician preference, case-specific anatomy, and clinical pri-orities, which leads to high inter-operator variability, especially for heterogeneous tumors. We propose an agentic large language model (LLM) workflow that de-composes VOI placement into generation of diverse candidate VOIs, from which the LLM selects an optimal one based on quantitative metrics. Candidate VOIs are generated by vision transformer-based placement models trained with differ-ent objective function preferences, which allows selection from acceptable alterna-tives rather than a single deterministic placement. On 110 clinical brain tumor cas-es, the agentic workflow achieves improved solid tumor coverage and necrosis avoidance depending on the user preferences compared to the general-purpose expert placements. Overall, the proposed workflow provides a strategy to adapt VOI placement to different clinical objectives without retraining task-specific models.

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Sangyoon Lee, Francesca Branzoli, Małgorzata Marjańska, Patrick Bolan. 2026-03-09. Agentic LLM Workflow for MR Spectroscopy Volume-of-Interest Placements in Brain Tumors. https://arxiv.org/abs/2603.13371

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