模型大小和构成对全面知识型计划质量的影响
Robert Kaderka1, Nesrin Dogan1, William Jin1
1Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Journal of applied clinical medical physics
|October 6, 2023
概括
对头癌治疗的基于知识的规划 (KBP) 模型产生了高质量的计划. 与处方剂量相比,模型大小和医生特定培训对计划质量的影响最小.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 人工智能在医学中的应用
背景情况:
- 基于知识的计划 (KBP) 自动化和标准化放射治疗治疗计划.
- 了解模型大小和医生变化的影响对于KBP采用至关重要.
研究的目的:
- 评估训练数据大小和医生特异性对头KBP模型的影响.
- 为了确定更大的KBP模型或单个医生的模型是否会产生优越的治疗计划.
主要方法:
- 训练了六个头KBP模型,不同的计划号码和医生来源.
- 在使用标准剂量计参数的独立患者队列上验证的模型.
- 进行盲人医生审查,比较KBP和手册计划.
主要成果:
- 完整的KBP_计划显示,相对于手动计划,剂量测量方面有所改善,在20/39个案例中,医生优先考虑.
- 不同大小的KBP模型和医生来源之间的差异通常很小 (<187 cGy).
- 模型大小和医生特定培训对整体计划质量的影响最小.
结论:
- 所有开发的KBP模型都产生了高质量的头部治疗计划.
- 增加KBP模型大小只会在计划质量方面取得微不足道的改善.
- 医生对训练数据的影响对KBP模型输出的影响最小.
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