关于人工智能驱动的自动化放射治疗计划在癌症类型中的多功能性和采用性的多中心研究
Lei Yu1,2,3,4, Qianxi Ni5,6, Binbing Wang7,8
1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Nature communications
|December 15, 2025
概括
深度学习自动化治疗计划 (ATP) 显示了放射治疗的前景. 将DL与临床目标相结合的混合方法可以快速生成高质量,个性化的计划,提高效率和结果.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 人工智能在医学中的应用
背景情况:
- 基于深度学习 (DL) 的自动化治疗计划 (ATP) 提供了简化放射治疗工作流程和提高计划质量一致性的潜力.
- 目前基于DL的ATP往往缺乏在临床实践中进行个性化治疗调整所需的灵活性.
研究的目的:
- 开发和评估一种混合策略,将基于DL的剂量预测与临床目标引导的逆优化相结合,以快速,可交付的放射治疗计划生成.
- 在多个机构和疾病地点评估拟议的混合ATP方法的临床适用性和计划质量.
主要方法:
- 开发了一种混合策略,将基于DL的剂量预测与临床目标引导的逆优化整合在一起.
- 针对五个疾病部位的单一机构数据,DL模型进行了训练,并在三个机构进行了追溯测试.
- 量身定制的优先级临床目标被用来指导优化过程,以便在五分钟内生成直接可交付的计划.
主要成果:
- 在250个自动生成计划中,超过80%的计划符合预定义的临床标准.
- 盲目审查表明,60%的自动计划比手动生成的计划更受欢迎.
- 剂量测量分析表明,自动计划的数量与人为驱动的计划的质量相匹配或超过.
结论:
- 基于DL的混合ATP方法显示了通过提供高质量,个性化的计划来有效地改变放射治疗实践的巨大潜力.
- 需要进一步的研究来增强多功能性,并促进ATP在各种临床环境中得到更广泛的采用.
- 这种方法成功地将自动剂量预测与目标导向优化相结合,以实现更快,更好的放射治疗计划.
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