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

Abstract

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 (R2) 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 R2 = 0.77 for a 3 mm PTV margin and R2 = 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 R2 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.