PDF(Pubmed)
Abstract：
OBJECTIVE: Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup.
METHODS: An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day\'s registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance.
RESULTS: Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.
CONCLUSIONS: In this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors\' knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.
METHODS: An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day\'s registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance.
RESULTS: Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.
CONCLUSIONS: In this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors\' knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.
摘要：
目的：自动化和计算机辅助可以支持放射治疗中的质量保证任务。回顾性图像审查需要大量的人力资源，图像审查的自动化仍然是以前工作中值得注意的缺失元素。这里,我们介绍了用于患者设置的CBCT注册的AI辅助审查的概念验证临床实施的初步结果.
方法：每晚开发并执行自动化管道，利用python脚本通过DICOM网络协议与临床数据库进行交互，并自动进行数据检索和分析。先前开发的人工智能（AI）算法基于未对准似然对CBCT设置配准进行评分，使用从0(最不可能)到1(最可能)的标度。在45天的时间里，通过管道检索并分析了197例患者的1357例治疗前CBCT注册。制作了前一天注册的每日总结报告。最初的行动水平针对10％的案例，以进行深入的物理审查。由三个独立的观察者对100个案例的验证子集进行评分，以表征AI模型的性能。
结果：在ROC分析之后，确定了模型预测的全球阈值为0.87，灵敏度为100%，特异性为82%。检查分层验证数据集的观察者分数显示观察者分数与模型预测之间具有统计上的显着相关性。
结论：在这项工作中，我们描述了用于CBCT引导的患者设置注册的每日定量分析的自动化AI分析管道的实施.AI模型经过了独立专家观察者的验证，并确定适当的作用水平以在不牺牲灵敏度的情况下将假阳性降至最低。案例研究证明了这种管道在支持放射治疗质量和安全计划方面的潜在好处。就作者所知，以前没有对基于CBCT的治疗前患者排列进行AI辅助评估的工作.
方法：每晚开发并执行自动化管道，利用python脚本通过DICOM网络协议与临床数据库进行交互，并自动进行数据检索和分析。先前开发的人工智能（AI）算法基于未对准似然对CBCT设置配准进行评分，使用从0(最不可能)到1(最可能)的标度。在45天的时间里，通过管道检索并分析了197例患者的1357例治疗前CBCT注册。制作了前一天注册的每日总结报告。最初的行动水平针对10％的案例，以进行深入的物理审查。由三个独立的观察者对100个案例的验证子集进行评分，以表征AI模型的性能。
结果：在ROC分析之后，确定了模型预测的全球阈值为0.87，灵敏度为100%，特异性为82%。检查分层验证数据集的观察者分数显示观察者分数与模型预测之间具有统计上的显着相关性。
结论：在这项工作中，我们描述了用于CBCT引导的患者设置注册的每日定量分析的自动化AI分析管道的实施.AI模型经过了独立专家观察者的验证，并确定适当的作用水平以在不牺牲灵敏度的情况下将假阳性降至最低。案例研究证明了这种管道在支持放射治疗质量和安全计划方面的潜在好处。就作者所知，以前没有对基于CBCT的治疗前患者排列进行AI辅助评估的工作.
