TH‐A‐116‐05: A Robust Direct Aperture Optimization Approach for Left‐Sided Breast IMRT

Abstract

Purpose: To test the feasibility of a robust direct aperture optimization (DAO) model for cardiac sparing IMRT planning of left‐sided breast cancer patients. Methods: We extended a previously developed robust optimization method to obtain a DAO approach for tangential breast IMRT that is robust against uncertainty in the patient's breathing pattern. The robust DAO model contains additional constraints on the segment shapes and weights in order to reduce the complexity of delivery and ensure that high weight segments cover the whole breast volume. A 4DCT dataset was used to test the model, and 100 simulated breathing patterns were generated. Results were compared with the previously developed robust fluence map optimization (FMO) model as well as the clinical treatment methods for breast cancer (inhale breath‐hold with active breathing control (ABC) and free breathing). Results: On average, the robust DAO approach reduced the mean heart dose by 15.1% compared to the conventional IMRT method for breast cancer (with free breathing) using an equal number of segments. The maximum dose to 10cc of the heart was reduced by 19.7%. Compared to the previous robust FMO method, the robust DAO method delivered less than 1% higher dose to the heart and 1% lower dose to the CTV on average. The dose‐volume criteria for the heart and CTV were met in all of the breathing scenarios. The robust DAO approach resulted in a 20.8% reduction in the number of cases that would have required ABC treatment in comparison with the clinical method with free breathing. Conclusions: The proposed DAO approach results in lower heart dose compared to the conventional method with free breathing and shows low sensitivity to breathing pattern uncertainty. The robust DAO method is able to derive simpler IMRT segments during free breathing and can potentially reduce the need for breath‐hold techniques. This research was supported in part by the Ontario Ministry of Economic Development and Innovation, the Canadian Breast Cancer Foundation ‐ Ontario Region, and the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF).