首个用于核药治疗预测的智能体框架，融合记忆进化与证据校准。
TheraAgent: Multi-Agent Framework with Self-Evolving Memory and Evidence-Calibrated Reasoning for PET Theranostics
- 分角色专家处理影像、检验和病历，带置信度共识
- 在35例真实患者上准确率达75.7%，合成数据超87%
- 基于临床试验证据推理，避免大模型幻觉
PET theranostics 正推动精准肿瘤学发展，但治疗反应差异大；许多转移性去势抵抗性前列腺癌（mCRPC）患者接受177Lu-PSMA放射配体疗法（RLT）后无效，亟需可靠的术前预测。尽管大语言模型（LLM）在复杂医学诊断中表现突出，其在PET theranostics结果预测中的应用尚未探索，面临三大挑战：（1）数据与知识匮乏：RLT仅2022年获FDA批准，训练案例少，通用大模型领域知识不足；（2）异构信息融合：稳健预测依赖从PET/CT、实验室检查及自由文本病历中提取结构化知识；（3）证据锚定推理：临床决策必须基于试验证据而非大模型幻觉。本文提出TheraAgent，据我们所知首个面向PET theranostics的智能体框架，包含三项核心创新：（1）多专家特征提取与置信加权共识，三名专业专家处理异构输入并量化不确定性；（2）自演化智能体记忆（SEA-Mem），从积累病例中学习预后模式，实现有限数据下的案例推理；（3）证据校准推理，整合经筛选的theranostics知识库，将预测锚定于VISION/TheraP试验证据。在35例真实患者和400例合成病例上评估，TheraAgent在真实患者上总体准确率达75.7%，合成数据上达87.0%，优于MDAgents和MedAgent-Pro超过20%。结果表明该框架为可信赖的AI代理在PET theranostics中的应用提供了可行蓝图，支持试验校准、多源决策辅助。代码将在录用后发布。
原文摘要 · Abstract (English)
PET theranostics is transforming precision oncology, yet treatment response varies substantially; many patients receiving 177Lu-PSMA radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC) fail to respond, demanding reliable pre-therapy prediction. While LLM-based agents have shown remarkable potential in complex medical diagnosis, their application to PET theranostic outcome prediction remains unexplored, which faces three key challenges: (1) data and knowledge scarcity: RLT was only FDA-approved in 2022, yielding few training cases and insufficient domain knowledge in general LLMs; (2) heterogeneous information integration: robust prediction hinges on structured knowledge extraction from PET/CT, laboratory tests, and free-text clinical documentation; (3) evidence-grounded reasoning: clinical decisions must be anchored in trial evidence rather than LLM hallucinations. In this paper, we present TheraAgent, to our knowledge, the first agentic framework for PET theranostics, with three core innovations: (1) Multi-Expert Feature Extraction with Confidence-Weighted Consensus, where three specialized experts process heterogeneous inputs with uncertainty quantification; (2) Self-Evolving Agentic Memory (SEA-Mem), which learns prognostic patterns from accumulated cases, enabling case-based reasoning from limited data; (3) Evidence-Calibrated Reasoning, integrating a curated theranostics knowledge base to ground predictions in VISION/TheraP trial evidence. Evaluated on 35 real patients and 400 synthetic cases, TheraAgent achieves 75.7% overall accuracy on real patients and 87.0% on synthetic cases, outperforming MDAgents and MedAgent-Pro by over 20%. These results highlight a promising blueprint for trustworthy AI agents in PET theranostics, enabling trial-calibrated, multi-source decision support. Code will be released upon acceptance.
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