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.

翻译：立体定向放射外科（SRS）要求围绕关键结构进行精确的剂量塑形，然而黑盒人工智能系统因其不透明性而临床采纳有限。我们在一个回顾性队列（41例接受18 Gy单次分割SRS治疗的脑转移瘤患者）中测试了思维链推理是否能提升代理规划能力。我们开发了SAGE（安全生成剂量专业代理），一种基于大语言模型的自动SRS治疗规划代理。针对每个病例，两个变体生成了治疗计划：一个使用非推理模型，一个使用推理模型。在主要终点指标（靶区覆盖率、最大剂量、适形指数、梯度指数；所有p > 0.21）上，推理变体显示出与人工规划师相当的剂量学表现，同时将耳蜗剂量降至低于人工基准水平（p = 0.022）。当被提示改进适形性时，推理模型展现出系统性的规划行为，包括前瞻性约束验证（457次）和权衡考量（609次），而标准模型则未表现出任何此类审慎过程（分别为0次和7次）。内容分析表明，约束验证和因果解释主要集中在推理代理中。优化轨迹可作为可审计日志，为透明化自动规划提供了路径。