Room: AAPM ePoster Library
Purpose: For complex cervical cancer cases, it is beneficial to use interstitial needles in combination with intracavitary applicators, e.g. tandem-and-ring/ovoids (T&R/T&O). Needles enable more customized dose distributions but also increase procedure time and risk of potential complications. Moreover, the decision to use needles is not standardized and depends on physician expertise. The purpose of this study is to determine whether knowledge-based models can predict cases where needle supplementation would be required to meet dose objectives for targets and organs-at-risk.
Methods: Previously validated dose-prediction models for intracavitary applicators were applied to hybrid cases where 1-3 needles were implanted. T&R (T&O) model precision is ±0.61Gy (±0.61Gy), ±0.42Gy (±0.46Gy) and ±0.56Gy (±0.47Gy) for bladder, rectum and sigmoid, respectively. Prediction accuracy was verified by re-planning 22 cases of each applicator without needles, aiming to meet at least HRCTV D90 between 85-90Gy, and if possible rectum D2cc<75Gy, bladder D2cc<90Gy and sigmoid D2cc<75Gy and HRCTV V100>95%. Predicted D2cc values were used to guide dose optimization.
Results: Re-planned D2cc values for bladder, rectum and sigmoid were within precision of T&R(T&O) model-predictions for 64%(27%), 45%(45%) and 59%(59%) of cases. 91%(45%) of the T&R(T&O) re-plans satisfied all dose constraints, suggesting that needles were not required. 45-55%(0-27%) of organ D2cc values were even decreased with T&R(T&O) re-planning. For cases where the T&R(T&O) model predicted D2cc values would meet constraints, 9%(36%), 0%(9%) and 0%(0%) of bladder, rectum and sigmoid doses, respectively, were over the limits. This typically occurred when predicted D2cc values of the considered fraction were >5Gy for bladder and >4Gy for rectum and sigmoid.
Conclusion: Model predicted D2cc values were beneficial for identifying cases that could be treated with intracavitary applicators alone. Needles were found to be over-used, particularly for T&R cases. Standardized planning driven by knowledge-based dose predictions could result in reduced needle usage and higher quality plans.
Funding Support, Disclosures, and Conflict of Interest: This work was funded by a research grant from Padres Pedal the Cause
Brachytherapy, Modeling, Treatment Planning
TH- Brachytherapy: Treatment planning using machine learning/automation