Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans.

Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiation therapy treatment workflow. Paired with technological refinements in modern radiation therapy, research toward measurement-free PSQA has seen increased interest during the past 5 years. However, these efforts have not been clinically implemented or prospectively validated in the United States. We propose a virtual QA (VQA) system and workflow to assess the safety and workload reduction of measurement-free PSQA. An XGBoost machine learning model was designed to predict PSQA outcomes of volumetric modulated arc therapy plans, represented as percent differences between the measured ion chamber point dose in a phantom and the corresponding planned dose. The final model was deployed within a web application to predict PSQA outcomes of clinical plans within an existing clinical workflow. The application also displays relevant feature importance and plan-specific distribution analyses relative to database plans for documentation and to aid physicist interpretation and evaluation. VQA predictions were prospectively validated over 3 months of measurements at our clinic to assess safety and efficiency gains. Over 3 months, VQA predictions for 445 volumetric modulated arc therapy plans were prospectively validated at our institution. VQA predictions for these plans had a mean absolute error of 1.08% ± 0.77%, with a maximum absolute error of 2.98%. Using a 1% prediction threshold (ie, plans predicted to have an absolute error <1% would not require a measurement) would yield a 69.2% reduction in QA workload, saving 32.5 hours per month on average, with 81.5% sensitivity, 72.4% specificity, and an area under the curve of 0.81 at a 3% clinical threshold and 100% sensitivity, 70% specificity, and an area under the curve of 0.93 at a 4% clinical threshold. This is the first prospective clinical implementation and validation of VQA in the United States, which we observed to be efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for PSQA, leading to more effective allocation of clinical resources.

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