University of Toronto, Department of Mechanical and Industrial Engineering, 5 King's College Road, Toronto, Ontario, M5S 3G8, Canada.
Princess Margaret Cancer Centre, Radiation Medicine Program, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.
Phys Med Biol. 2022 Mar 9;67(6). doi: 10.1088/1361-6560/ac568f.
Radiotherapy is a common treatment modality for the treatment of cancer, where treatments must be carefully designed to deliver appropriate dose to targets while avoiding healthy organs. The comprehensive multi-disciplinary quality assurance (QA) process in radiotherapy is designed to ensure safe and effective treatment plans are delivered to patients. However, the plan QA process is expensive, often time-intensive, and requires review of large quantities of complex data, potentially leading to human error in QA assessment. We therefore develop an automated machine learning algorithm to identify 'acceptable' plans (plans that are similar to historically approved plans) and 'unacceptable' plans (plans that are dissimilar to historically approved plans). This algorithm is a supervised extension of projective adaptive resonance theory, called SuPART, that learns a set of distinctive features, and considers deviations from them indications of unacceptable plans. We test SuPART on breast and prostate radiotherapy datasets from our institution, and find that SuPART outperforms common classification algorithms in several measures of accuracy. When no falsely approved plans are allowed, SuPART can correctly auto-approve 34% of the acceptable breast and 32% of the acceptable prostate plans, and can also correctly reject 53% of the unacceptable breast and 56% of the unacceptable prostate plans. Thus, usage of SuPART to aid in QA could potentially yield significant time savings.
放射治疗是治疗癌症的一种常见方法，治疗方案必须精心设计，以将适当的剂量输送到目标部位，同时避免健康器官受到影响。放射治疗的综合多学科质量保证（QA）流程旨在确保为患者提供安全有效的治疗计划。然而，计划 QA 流程昂贵且耗时，需要对大量复杂数据进行审查，这可能导致 QA 评估中的人为错误。因此，我们开发了一种自动化机器学习算法，以识别“可接受”的计划（与历史上批准的计划相似的计划）和“不可接受”的计划（与历史上批准的计划不同的计划）。该算法是投影自适应谐振理论（projective adaptive resonance theory）的有监督扩展，称为 SuPART，它可以学习一组独特的特征，并将其偏差视为不可接受计划的指示。我们在来自我们机构的乳房和前列腺放射治疗数据集上测试了 SuPART，并发现 SuPART 在准确性的几个度量上优于常见的分类算法。当不允许有错误批准的计划时，SuPART 可以正确地自动批准 34%的可接受乳房计划和 32%的可接受前列腺计划，还可以正确地拒绝 53%的不可接受乳房计划和 56%的不可接受前列腺计划。因此，使用 SuPART 辅助 QA 可能会节省大量时间。
