SU-F-T-544: Development of a Clinically Feasible Workflow of 4D MRI-Based Treatment Planning for Liver SBRT.

PURPOSE: To develop a clinically feasible workflow of 4D MRI-based treatment planning for liver SBRT. METHODS: A clinical workflow based on 4D MRI for liver SBRT was designed to largely simulate the same procedure as 4D CT for lung SBRT. Key features of the workflow include: (1) contouring tumor volume at each phases (2) contouring more structures, (3) assigning CT numbers to structures for dose calculation. Uncertainties (bone delineation, MRI distortion, CT number assignment, etc.) were investigated using five clinical treatment plans of liver SBRT by simulating the following scenarios: homogeneous calculation, calculation with CT number assignment (for each structure separately and for all combined), calculation with and without distortion (mimicked by shirking body structure by 1mm to 5 mm). Dose-histogram volume (DVF) parameters were collected from the simulated cases and compared to those in the original plan. Lastly, a patient example case of 4D MRI-based treatment plan was performed to demonstrate the feasibility of the proposed workflow. RESULTS: In homogeneous calculation, the average percentage volume error (ΔV30) of liver was 0.40% and maximum average dose error (GTV ΔD95) was 1.08Gy. Between MRI-based and original plans, liver ΔV30 was 0.28% and maximum average dose error (GTV ΔD95) was 0.75Gy. Basically dose errors and percentage volume error increased as distortion increased. In some special cases, in original plans, the minimum spinal cord max dose error appeared when distortion was 4mm; in MRI-based plans, the minimum liver ΔV30 appeared when distortion was 3mm. In the example case, mean liver dose was 20.7Gy in 4D MRI-based plan and 34.2Gy in 4D CT-based plan. CONCLUSION: A clinically feasible workflow of 4D MRI-based treatment planning for liver SBRT has been established. Dose uncertainties of the workflow were generally small, except for large MR geometric distortions (> 3 mm). NIH (1R21CA165384).

Duke Scholars

Author Manisha Palta Radiation Oncology