Machine Learning for Patient Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.
PURPOSE To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans. METHODS AND MATERIALS Three hundred and three VMAT plans including 176 gynecological cancer (GYN) and 127 head and neck cancer (H&N) plans were chosen in this study. 54 complexity metrics were extracted from the QA plans and considered as inputs. Patient specific QA was performed, gamma passing rates (GPR) were used as outputs. One Poisson Lasso (PL) regression model was developed aiming to predict individual GPR and one Random Forest (RF) classification model was developed to classify QA results as "pass" or "fail" Both technical validation (TV) and clinical validation (CV) were used to evaluate the model reliability. GPR prediction accuracy of PL and classification performance of PL and RF were evaluated. RESULTS In TV, the mean prediction error of PL was 1.81% 2.39% and 4.18% at 3%/3mm, 3%/2mm and 2%/2mm, respectively. No significant differences in prediction errors between TV and CV were observed. In QA results classification, PL had a higher specificity (accurately identify plans that can pass QA) while RF had a higher sensitivity (accurately identify plans that may fail QA) By using 90% as action limits at 3%/2mm criteria, the specificity of PL and RF were 97.5% and 87.7% in TV, and 100% and 71.4% in CV, respectively; the sensitivity of PL and RF were 31.6% and 100% in TV and 33.3% and 100% in CV, respectively. With 100% sensitivity, the QA workload of 81.2% plans in TV and 62.5% plans in CV could be reduced by RF. CONCLUSIONS PL model could accurately predict GPR for majority VMAT plans. RF model with 100% sensitivity was preferred for QA results classification. Machine learning can be a useful tool to assist VMAT QA and reduce QA workload.
International journal of radiation oncology, biology, physics