用带推理能力的AI助手，让脑部放疗计划更安全透明。
Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent
- 引入带思维链的AI代理，逐步推理优化放疗方案。
- 放疗剂量分布与医生相当，且听力保护剂量降低2.2%。
- 可追踪决策过程，适合临床需要可解释性的场景。
立体定向放射外科（SRS）需精准避开关键结构，但黑箱AI因缺乏透明度难获临床采纳。本研究在41例脑转移瘤患者中测试了基于大模型的推理型规划系统——SAGE（生成剂量专长代理）。对比两种版本：无推理和带推理的模型。结果显示，推理版在主要指标（靶区覆盖、最大剂量、适形指数、梯度指数）上与人类规划者相当（所有p > 0.21），同时显著降低耳蜗剂量（p = 0.022）。当被要求提升适形性时，推理模型展现出系统性行为：前瞻性约束验证457次、权衡讨论609次；而标准模型未出现此类行为（0次和7次）。内容分析显示，约束验证与因果解释集中于推理模型。优化轨迹可作为可审计日志，为透明化自动化规划提供路径。
原文摘要 · Abstract (English)
Stereotactic radiosurgery (SRS) demands precise dose shaping around critical structures, yet black-box AI systems have limited clinical adoption due to opacity concerns. We tested whether chain-of-thought reasoning improves agentic planning in a retrospective cohort of 41 patients with brain metastases treated with 18 Gy single-fraction SRS. We developed SAGE (Secure Agent for Generative Dose Expertise), an LLM-based planning agent for automated SRS treatment planning. Two variants generated plans for each case: one using a non-reasoning model, one using a reasoning model. The reasoning variant showed comparable plan dosimetry relative to human planners on primary endpoints (PTV coverage, maximum dose, conformity index, gradient index; all p > 0.21) while reducing cochlear dose below human baselines (p = 0.022). When prompted to improve conformity, the reasoning model demonstrated systematic planning behaviors including prospective constraint verification (457 instances) and trade-off deliberation (609 instances), while the standard model exhibited none of these deliberative processes (0 and 7 instances, respectively). Content analysis revealed that constraint verification and causal explanation concentrated in the reasoning agent. The optimization traces serve as auditable logs, offering a path toward transparent automated planning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。