■具有过度复杂性的放射治疗计划表现出更高的不确定性和更差的患者特定质量保证(PSQA)结果,而基于测量的PSQA的工作量会影响放射治疗工作流程的效率。机器学习(ML)和精益六西格玛,一种工艺优化方法,被实施为采用有针对性的PSQA方法,旨在减少工作量,失败的风险,监控复杂性。
■使用DMAIC应用精益六西格玛(定义,measure,分析,改进,和控制)步骤。从我们研究所交付的28,612个计划(2013-2021年)中,为69,811个体积调制电弧治疗(VMAT)弧计算了10个复杂性指标。异常值复杂性被定义为>历史分布的第95百分位数,按治疗分层。考虑到电弧的复杂性,对ML模型进行了训练,以预测电弧的伽马通过率(GPR-3%/1mm)。开发了决策支持系统来监视复杂性和预期的GPR。计划面临PSQA失败的风险,要么极其复杂,要么平均探地雷达<90%,已确定。在临床使用9个月后评估该工具的影响。
■在预期监测的1722个VMAT计划中,29人(1.7%)被发现有失败的风险。规划人员通过执行PSQA测量和重新优化计划来做出反应。异常复杂性的发生保持在5%以内。由于计划重新优化,预期的GPR从97.4%的中位数增加到98.2%(Mann-Whitneyp<0.05)。
■ML和精益六西格玛已在临床实践中实施,从而为面临失败风险的计划提供针对性的基于测量的PSQA方法,以提高整体质量和患者安全性。
UNASSIGNED: Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity.
UNASSIGNED: Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool\'s impact was assessed after nine months of clinical use.
UNASSIGNED: Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization.
UNASSIGNED: ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.