Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine

• DOI: 10.3390/diagnostics13050943
• Publication Date: 2023-03-02T08:54:56Z
• Authors: Noémie Moreau; Laurine Bonnor; Cyril Jaudet; Laetitia Lechippey; Nadia Falzone; Alain Batalla; Cindy Bertaut; Aurélien Corroyer...
• Keywords: Medicine (General); [SDV]Life Sciences [q-bio]; [SDV.CAN]Life Sciences [q-bio]/Cancer; VMAT; [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine; quality assurance; Article; [SDV.IB.MN] Life Sciences [q-bio]/Bioengineering/Nuclear medicine; 3. Good health; [SDV] Life Sciences [q-bio]; clinical routine; machine learning; R5-920; [SDV.CAN] Life Sciences [q-bio]/Cancer; deep hybrid learning; radiotherapy

Background: 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. Methods. 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. Results. 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%. Conclusions. 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.