Noblet Caroline, Maunet Mathis, Duthy Marie, Coste Frédéric, Moreau Matthieu
Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France.
Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France.
Phys Med. 2024 Feb;118:103208. doi: 10.1016/j.ejmp.2024.103208. Epub 2024 Jan 10.
Machine learning (ML) models have been demonstrated to be beneficial for optimizing the workload of patient-specific quality assurance (PSQA). Implementing them in clinical routine frequently requires third-party applications beyond the treatment planning system (TPS), slowing down the workflow. To address this issue, a PSQA outcomes predictive model was carefully selected and validated before being fully integrated into the TPS.
Nine ML algorithms were evaluated using cross-validation. The learning database was built by calculating complexity metrics (CM) and binarizing PSQA results into "pass"/"fail" classes for 1767 VMAT arcs. The predictive performance was evaluated using area under the ROC curve (AUROC), sensitivity, and specificity. The ML model was integrated into the TPS via a C# script. Script-guided reoptimization impact on PSQA and dosimetric results was evaluated on ten VMAT plans with "fail"-predicted arcs. Workload reduction potential was also assessed.
The selected model exhibited an AUROC of 0.88, with a sensitivity and specificity exceeding 50 % and 90 %, respectively. The script-guided reoptimization of the ten evaluated plans led to an average improvement of 1.4 ± 0.9 percentage points in PSQA results, while preserving the quality of the dose distribution. A yearly savings of about 140 h with the use of the script was estimated.
The proposed script is a valuable complementary tool for PSQA measurement. It was efficiently integrated into the clinical workflow to enhance PSQA outcomes and reduce PSQA workload by decreasing the risk of failing QA and thereby, the need for repeated replanning and measurements.
机器学习（ML）模型已被证明有助于优化患者特异性质量保证（PSQA）的工作量。在临床常规中实施这些模型通常需要治疗计划系统（TPS）之外的第三方应用程序，这会减慢工作流程。为了解决这个问题，在将一个PSQA结果预测模型完全集成到TPS之前，对其进行了精心挑选和验证。
使用交叉验证评估了九种ML算法。通过计算复杂度指标（CM）并将1767条容积调强放疗（VMAT）弧的PSQA结果二值化为“通过”/“未通过”类别来构建学习数据库。使用ROC曲线下面积（AUROC）、敏感性和特异性评估预测性能。通过C#脚本将ML模型集成到TPS中。在十个具有“未通过”预测弧的VMAT计划上评估脚本引导的重新优化对PSQA和剂量学结果的影响。还评估了工作量减少的潜力。
所选模型的AUROC为0.88，敏感性和特异性分别超过50%和90%。对十个评估计划进行脚本引导的重新优化后，PSQA结果平均提高了1.4±0.9个百分点，同时保持了剂量分布的质量。估计使用该脚本每年可节省约140小时。
所提出的脚本是PSQA测量的一个有价值的补充工具。它被有效地集成到临床工作流程中，以通过降低质量保证失败的风险，从而减少重复重新计划和测量的需求，提高PSQA结果并减少PSQA工作量。
