PurposeVolumetric‐modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time‐consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.MethodsFor patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep‐learning and RapidPlan models developed in‐house. The autoplans were, then, applied to the original, physician‐drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a “two one‐sided tests” (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria.ResultsFor HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively.ConclusionsThis study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.

中文翻译：

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评估自动生成的正常组织轮廓，以便在头颈癌和宫颈癌治疗计划中安全使用

目的容量调节弧疗法 (VMAT) 是一种广泛接受的头颈癌 (HN) 和宫颈癌治疗方法；然而，为 VMAT 计划创建轮廓和计划优化是一个耗时的过程。我们的团队创建了一种自动化治疗计划工具，即放射计划助手 (RPA)，它使用深度学习模型来生成处于危险中的器官 (OAR)、计划结构并自动执行计划优化。本研究定量评估 RPA 工具生成的轮廓的质量。方法对于 HN (54) 和宫颈 (39) 癌症患者，我们使用 RPA 回顾性生成自动计划。自动计划是使用内部开发的深度学习和 RapidPlan 模型生成的。然后，将自动计划应用于医生绘制的原始轮廓，并将其用作地面实况 (GT) 来与自动轮廓 (RPA) 进行比较。使用“两个单侧测试”（TOST）程序，我们评估了自动轮廓正常组织剂量是否与地面真实剂量相当，δ，我们根据临床判断确定。我们还计算了满足已建立的临床可接受剂量标准的计划数量。结果对于 HN 计划，分别有 91.8% 和 91.7% 的结构满足自动和手动轮廓的剂量标准；对于宫颈计划，95.6% 和 95.7% 的结构分别满足自动和手动轮廓的剂量测定标准。对于 HN 和宫颈癌，自动轮廓分别相当于 71% 和 75% 的常见 DVH 指标的地面实况。 结论 这项研究表明，使用深度学习技术可以为 HN 和宫颈癌创建剂量学等效的正常组织轮廓。一般来说，轮廓之间的差异不会影响临床剂量耐受性的通过或失败。