Multiple Computed Tomography Robust Optimization to Account for Random Anatomic Density Variations During Intensity Modulated Proton Therapy

PURPOSE: To propose a method of optimizing intensity modulated proton therapy (IMPT) plans robust against dosimetric degradation caused by random anatomic variations during treatment. METHODS AND MATERIALS: Fifteen patients with prostate cancer treated with IMPT to the pelvic targets were nonrandomly selected. On the repeated quality assurance computed tomography (QACTs) for some patients, bowel density changes were observed and caused dose degradation because the treated plans were not robustly optimized (non-RO). To mitigate this effect, we developed a robust planning method based on 3 CT images, including the native planning CT and its 2 copies, with the bowel structures being assigned to air and tissue, respectively. The RO settings included 5 mm setup uncertainty and 3.5% range uncertainty on 3 CTs. This method is called pseudomultiple-CT RO (pMCT-RO). Plans were also generated using RO on the native CT only, with the same setup and range uncertainties. This method is referred to as single-CT RO (SCT-RO). Doses on the QACTs and the nominal planning CT were compared for the 3 planning methods. RESULTS: All 3 plan methods provided sufficient clinical target volumes D95% and V95% on the QACTs. For pMCT-RO plans, the normal tissue D(max) on QACTs of all patients was at maximum 109.1%, compared with 144.4% and 116.9% for non-RO and SCT-RO plans, respectively. On the nominal plans, the rectum and bladder doses were similar among all 3 plans; however, the volume of normal tissue (excluding the rectum and bladder) receiving the prescription dose or higher is substantially reduced in either pMCT-RO plans or SCT-RO plans, compared with the non-RO plans. CONCLUSIONS: We developed a robust optimization method to further mitigate undesired dose heterogeneity caused by random anatomic changes in pelvic IMPT treatment. This method does not require additional patient CT scans. The pMCT-RO planning method has been implemented clinically since 2017 in our center.