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用机器学习对口腔癌患者的评估治疗计划系统进行评估

Ahmed Ghanim Glayl¹, Karrar Hazim Salem², Harith Muthanna Noori³

¹Department of Radiation Oncology, University Medical Center Groningen, Netherlands.

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概括

机器学习模型用于口腔喉癌 (OPC) 和体积调制弧线疗法 (VMAT) 的表现与临床计划相当或优于临床计划. 这项研究优化了OPC的VMAT治疗策略,旨在改善正常组织并发症概率.

科学领域:

辐射瘤学辐射瘤学
医学物理医学物理
医疗保健中的机器学习

背景情况:

口腔大癌 (OPC) 是一个复杂的治疗挑战.
卷度调节弧线疗法 (VMAT) 是一种标准的放射治疗技术.
优化VMAT计划对于改善患者的治疗结果和减少毒性至关重要.

研究的目的:

评估基于机器学习 (ML) 的VMAT模型对OPC的性能.
使用剂量计参数将ML生成的VMAT计划与使用临床创建的计划进行比较.
确定OPC治疗最有效的战略计划方案.

主要方法:

为OPC开发了一种机器学习 (ML) VMAT模型,并使用各种方式进行训练.
根据剂量限制和目标覆盖范围,对五名患者的ML计划与参考临床计划进行了比较.
使用PTV (PTV+7000 cGy和PTV_5425 cGy) 使用360°VMAT光束进行标准OPC剂量处方 (70 Gy in 2 Gy/Fx).

主要成果:

虽然一个案例在临床计划中显示了更高的L-Eye体积,但ML计划在五分之四的案例中显示了对关键器官的优越保护.
对于PTVs,剂量同质性指数 (DHI) 从1到1.34不等.
对于PTV的剂量合规指数 (DCI) 从0.98到1.

结论:

基于ML的VMAT模型显示了与OPC临床计划的性能相匹配或超过的潜力.
ML-VMAT为优化治疗计划提供了一个有希望的策略,有可能减少正常组织并发症.
进一步完善ML模型可以导致更个性化和更有效的OPC放射治疗.

关键词:

机器学习 (ML) 是指机器学习.口腔大癌 (OPC) 是一种癌症.放射治疗 (RT) 是一种疗法.

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