Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge‐based dose prediction
Abstract
Purpose
Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on‐treatment process is resource‐intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high‐priority patients.
Methods
For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard‐of‐care (InitialSOC) and a reduced margin initial plan (InitialART) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (DailySOC and DailyART) were determined by re‐segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit (= DailySOC‐DailyART) versus initial plan differences (= InitialSOC–InitialART) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences () of (cc), (Gy), and (Gy). Anatomy (intact uterus or post‐hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high‐benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave‐one‐out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge‐based plans for the ΔInitial plans () and repeated the analysis.
Results
In both and our multivariate analysis showed low R2 values 0.34–0.52 versus 0.14–0.38. The most significant predictor in each multivariate model was the corresponding ∆Initial metric (e.g., Bowel (V40 Gy), p < 1e−05). In the logistic model, the metrics with the strongest correlation to the high‐benefit patients were (cc), (Gy), , and prescription. The models for original and knowledge‐based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92/0.72 and 0.69/0.80, respectively.
Conclusion
This methodology will allow clinics to prioritize patients for resource‐intensive daily online ART.