Forecasting patient-specific dosimetric benefit from daily online adaptive radiotherapy for cervical cancer

Abstract Objective . Adaptive Radiotherapy (ART) is an emerging technique for treating cancer patients which facilitates higher delivery accuracy and has the potential to reduce toxicity. However, ART is also resource-intensive, Requiring extra human and machine time compared to standard treatment methods. In this analysis, we sought to predict the subset of node-negative cervical cancer patients with the greatest benefit from ART, so resources might be properly allocated to the highest-yield patients. Approach . CT images, initial plan data, and on-treatment Cone-Beam CT (CBCT) images for 20 retrospective cervical cancer patients were used to simulate doses from daily non-adaptive and adaptive techniques. We evaluated the coefficient of determination (R 2 ) between dose and volume metrics from initial treatment plans and the dosimetric benefits to the Bowel V 40 Gy , Bowel V 45 Gy , Bladder D mean , and Rectum D mean from adaptive radiotherapy using reduced 3 mm or 5 mm CTV-to-PTV margins. The LASSO technique was used to identify the most predictive metrics for Bowel V 40 Gy . The three highest performing metrics were used to build multivariate models with leave-one-out validation for Bowel V 40 Gy . Main results . Patients with higher initial bowel doses were correlated with the largest decreases in Bowel V 40 Gy from daily adaptation (linear best fit R 2 = 0.77 for a 3 mm PTV margin and R 2 = 0.8 for a 5 mm PTV margin). Other metrics had intermediate or no correlation. Selected covariates for the multivariate model were differences in the initial Bowel V 40 Gy and Bladder D mean using standard versus reduced margins and the initial bladder volume. Leave-one-out validation had an R 2 of 0.66 between predicted and true adaptive Bowel V 40 Gy benefits for both margins. Significance . The resulting models could be used to prospectively triage cervical cancer patients on or off daily adaptation to optimally manage clinical resources. Additionally, this work presents a critical foundation for predicting benefits from daily adaptation that can be extended to other patient cohorts.