Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge-based dose prediction.

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 (Initial) and a reduced margin initial plan (Initial) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (Daily and Daily) 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 ( = Daily-Daily) versus initial plan differences ( = Initial-Initial) 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 R 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.