用AI自动完成前列腺癌PET/CT的个性化放射剂量计算，准确高效。
DosimeTron: Automating Personalized Monte Carlo Radiation Dosimetry in PET/CT with Agentic AI
- 通过智能体AI系统自动处理影像、分割器官并进行蒙特卡洛模拟
- 96%以上器官剂量相关性达0.96以上，误差中位数仅2.5%
- 适合核医学与放射肿瘤医生快速实现精准个体化剂量评估
目的：开发并评估DosimeTron——一种用于PET/CT检查中患者特异性蒙特卡洛内照射剂量计算的智能体AI系统。方法：本回顾性研究在公开的PSMA-PET/CT数据集上评估了该系统，包含378名男性患者的597例扫描（18-F：369例；68-Ga：228例），覆盖三种扫描仪型号。系统以GPT-5.2为推理引擎，通过四个模型上下文协议服务器调用23个工具，实现DICOM元数据提取、图像预处理、蒙特卡洛模拟、器官分割与剂量报告的全自动化，支持自然语言交互。智能体性能通过多种提示模板（单轮指令与多轮对话）评估，并通过OpenTelemetry追踪监控。剂量准确性在114例样本和22个器官上，采用皮尔逊相关系数、林氏一致性相关系数（CCC）及Bland-Altman分析与OpenDose3D对比验证。结果：所有提示模板与运行中均未出现执行失败、管道错误或幻觉输出。皮尔逊相关系数为0.965~1.000（中位数0.997；全部p<0.001），CCC为0.963~1.000（中位数0.996）。22个器官中有19个的平均绝对百分比差异低于5%（中位数2.5%）。每例总处理时间为32.3±6.0分钟。结论：DosimeTron在多样化提示配置下自主执行复杂剂量计算流程，与OpenDose3D具有高度一致性，且处理时间符合临床需求，证明了智能体AI在PET/CT患者特异性蒙特卡洛剂量计算中的可行性。
原文摘要 · Abstract (English)
Purpose: To develop and evaluate DosimeTron, an agentic AI system for automated patient-specific MC internal radiation dosimetry in PET/CT examinations. Materials and Methods: In this retrospective study, DosimeTron was evaluated on a publicly available PSMA-PET/CT dataset comprising 597 studies from 378 male patients acquired on three scanner models (18-F, n = 369; 68-Ga, n = 228). The system uses GPT-5.2 as its reasoning engine and 23 tools exposed via four Model Context Protocol servers, automating DICOM metadata extraction, image preprocessing, MC simulation, organ segmentation, and dosimetric reporting through natural-language interaction. Agentic performance was assessed using diverse prompt templates spanning single-turn instructions of varying specificity and multi-turn conversational exchanges, monitored via OpenTelemetry traces. Dosimetric accuracy was validated against OpenDose3D across 114 cases and 22 organs using Pearson's r, Lin's concordance correlation coefficient (CCC), and Bland-Altman analysis. Results: Across all prompt templates and all runs, no execution failures, pipeline errors, or hallucinated outputs were observed. Pearson's r ranged from 0.965 to 1.000 (median 0.997; all p < 0.001) and CCC from 0.963 to 1.000 (median 0.996). Mean absolute percentage difference was below 5% for 19 of 22 organs (median 2.5%). Total per-study processing time (SD) was 32.3 (6.0) minutes. Conclusion: DosimeTron autonomously executed complex dosimetry pipelines across diverse prompt configurations and achieved high dosimetric agreement with OpenDose3D at clinically acceptable processing times, demonstrating the feasibility of agentic AI for patient-specific Monte Carlo dosimetry in PET/CT.
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