arXiv 2606.283922026-06-23cs.CVcs.AIcs.LG

RADIANT-PET：结合大语言模型和强化学习的推理增强型PET/CT病灶分割

RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning

Division of Hematology, The Ohio State University Comprehensive Cancer Center（血液科，俄亥俄州立大学综合癌症中心）；Department of Radiology, Mahidol University（医学放射科，玛希多大学）；Department of Radiology, Mettapracharak Hospital（医学放射科，Mettapracharak医院）；Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center（放射肿瘤科，俄亥俄州立大学综合癌症中心）

Jiasheng Wang, Tanun Jitwatcharakomol, Piyawadee Jongpradubgiat, Simeng Zhu

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AI总结：

提出RADIANT-PET框架，通过大语言模型和强化学习对候选摄取区域进行推理分类，抑制生理性假阳性，提升PET/CT病灶分割准确性。

摘要：

在PET/CT中准确的病灶分割对肿瘤学至关重要，但由于生理性示踪剂摄取和伪影可能模拟恶性信号，仍然具有挑战性。我们提出RADIANT-PET，一个推理增强框架，将高灵敏度体素级分割模型与病灶级大语言模型（LLM）判定相结合。通过故意宽松的分割阶段生成候选摄取区域，然后将其转换为结构化文本描述，总结摄取强度、形态以及区域和全局解剖背景。LLM将每个候选区域分类为真实病灶或假阳性，并可选择利用放射学报告作为额外的临床背景。为了加强病灶级推理，我们进一步通过组相对策略优化使用强化学习优化本地LLM，奖励正确的病灶分类和解剖一致的位置分配。在AutoPET和OSU测试队列中，RADIANT-PET始终优于仅基于图像的强基线，当提供放射学报告时观察到最大改进。总体而言，这些结果表明，基于LLM的病灶级推理在传统分割之外增加了一个新的推理层，抑制了生理性假阳性，并使体素级预测与临床解释保持一致。项目仓库地址：https://github.com/jwang-580/RADIANT-PET。

英文摘要：

Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal. We present RADIANT-PET, a reasoning-augmented framework that couples a high-sensitivity voxel-level segmentation model with lesion-level large language model (LLM) adjudication. Candidate uptake regions are generated with a deliberately permissive segmentation stage, then converted into structured textual descriptions that summarize uptake intensity, morphology, and regional and global anatomical context. An LLM classifies each candidate as true lesion vs. false positive, optionally leveraging the radiology report as additional clinical context. To strengthen lesion-level reasoning, we further optimize a local LLM via reinforcement learning using Group Relative Policy Optimization, rewarding correct lesion classification and anatomically concordant site assignment. Across AutoPET and an OSU test cohort, RADIANT-PET consistently outperforms strong image-only baselines, with the largest improvements observed when radiology reports are provided. Overall, these results demonstrate that LLM-based lesion-level reasoning adds a novel reasoning layer beyond conventional segmentation, suppressing physiologic false positives and aligning voxel-level predictions with clinical interpretation. The project repository is available at: https://github.com/jwang-580/RADIANT-PET.

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