Agentic LLM Workflow for MR Spectroscopy Volume-of-Interest Placements in Brain Tumors
This addresses variability in clinical VOI placement for brain tumor analysis, offering an adaptive strategy without retraining, but it is incremental as it builds on existing vision transformer and LLM methods.
The authors tackled the problem of high inter-operator variability in placing magnetic resonance spectroscopy volumes-of-interest (VOIs) for brain tumors by proposing an agentic LLM workflow that generates diverse candidate VOIs and selects an optimal one based on quantitative metrics, achieving improved solid tumor coverage and necrosis avoidance on 110 clinical cases compared to expert placements.
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.