Moreau Noémie, Bonnor Laurine, Jaudet Cyril, Lechippey Laetitia, Falzone Nadia, Batalla Alain, Bertaut Cindy, Corroyer-Dulmont Aurélien
Medical Physics Department, CLCC François Baclesse, 14000 Caen, France.
GenesisCare Theranostics, Building 1 & 11, The Mill, 41-43 Bourke Road, Alexandria, NSW 2015, Australia.
Diagnostics (Basel). 2023 Mar 2;13(5):943. doi: 10.3390/diagnostics13050943.
Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload.
Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance.
For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%.
The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.
弧形治疗能够实现更好的剂量沉积形态，但放射治疗计划（RT计划）更为复杂，需要针对患者进行治疗前质量保证（QA）。反过来，治疗前QA增加了工作量。本研究的目的是基于RT计划复杂性指数开发Delta4-QA结果的预测模型，以减少QA工作量。
从1632个RT VMAT计划中提取了六个复杂性指数。开发了一种机器学习（ML）模型用于分类目的（两类：符合QA计划与否）。对于更复杂的部位（乳腺、骨盆以及头颈部），训练了创新的深度混合学习（DHL）以实现更好的性能。
对于不复杂的RT计划（脑和胸部肿瘤部位），ML模型实现了100%的特异性和98.9%的敏感性。然而，对于更复杂的RT计划，特异性降至87%。对于这些复杂的RT计划，开发了一种使用DHL的创新QA分类方法，实现了100%的敏感性和97.72%的特异性。
ML和DHL模型以高度准确性预测了QA结果。我们的预测QA在线平台在加速器占用时间和工作时间方面节省了大量时间。
