用智能大模型自动优化脑肿瘤磁共振波谱的采样区域，提升精准度。
Agentic LLM Workflow for MR Spectroscopy Volume-of-Interest Placements in Brain Tumors
- 分步生成多种候选采样区，由模型根据需求选最优解。
- 在110例临床数据中，相比通用方案更优覆盖实体肿瘤并避开坏死区。
- 适合需要个性化诊疗的医生或研究者，尤其处理复杂异质肿瘤时。
磁共振波谱（MRS）可提供脑肿瘤的代谢特征，但其效果依赖于采样体积（VOI）的准确放置。然而，同一肿瘤存在多个合理采样位置，导致不同医生间差异大，尤其在异质性肿瘤中更为明显。本文提出一种基于智能体的大语言模型（LLM）工作流，将VOI放置分解为生成多样候选区域，并依据定量指标选出最优方案。候选区域由基于视觉变换器的放置模型生成，这些模型采用不同目标函数训练，实现从多个可接受方案中选择。在110例临床脑肿瘤病例中，该工作流根据用户偏好，在实体肿瘤覆盖和坏死区规避方面均优于通用专家方案。整体上，该方法无需为特定任务重新训练模型，即可灵活适配不同临床目标。
原文摘要 · Abstract (English)
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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