Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy
Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy
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DOI：
10.1016/j.prro.2020.05.013
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发表时间：
2021-01-01
影响因子：
3.3
通讯作者：
McVicar, Nevin
中科院分区：
文献类型：
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作者：
Zabel, W. Jeffrey;Conway, Jessica L.;McVicar, Nevin
目的：自动轮廓绘制可减少工作量、观察者间差异以及与危险器官手动轮廓绘制相关的时间。手动轮廓仍然是标准，部分原因是考虑到自动轮廓的审查和编辑后，时间和工作量节省方面的不确定性。本初步研究比较了标准手动轮廓绘制工作流程与2个自动轮廓绘制工作流程（图谱和深度学习），用于绘制前列腺癌患者的膀胱和直肠轮廓。方法和材料：基于初始轮廓生成方法定义了三个轮廓绘制工作流程，包括手动（MAN），基于图谱的自动轮廓（ATLAS）和深度学习自动轮廓（DEEP）。对于每个工作流程，对15名前列腺癌患者进行回顾性初始轮廓生成。然后，放射肿瘤学家（RO）编辑每个轮廓，同时对初始轮廓的生成方式不知情。工作流程进行了比较的时间（无论是在初始轮廓生成和RO编辑），轮廓相似性，和剂量evaluation.Results：平均持续时间为初始轮廓生成分别为10.9分钟，1.4分钟，和1.2分钟MAN，深，ATLAS，分别。初始DEEP轮廓在几何上与初始MAN轮廓更相似。MAN、DEEP和ATLAS轮廓的RO编辑步骤的平均持续时间分别为4.1分钟、4.7分钟和10.2分钟。与MAN和DEEP相比，ATLAS轮廓的RO编辑的几何范围始终较大。Workflows.Conclusion：Auto-contouring软件为初始轮廓生成节省了时间;然而，重要的是还量化RO编辑步骤中的工作量变化。使用深度学习自动轮廓生成膀胱和直肠轮廓减少了轮廓时间，而不会对RO编辑时间、轮廓几何形状或临床相关剂量体积指标产生负面影响。这项工作有助于越来越多的证据表明，深度学习方法是放射治疗中危险器官轮廓的临床可行解决方案。(C)2020作者（S）爱思唯尔公司出版代表美国放射肿瘤学会。
Purpose: Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.Methods and Materials: Three contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation.Results: Mean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows.Conclusion: Auto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose-volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy. (C) 2020 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.