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Incorporating Tumor Regression Into Robust Plan Optimization for Head and Neck Radiotherapy

P Zhang

P Zhang*, J Hu , N Tyagi , G Mageras , N Lee , M Hunt , Memorial Sloan-Kettering Cancer Center, New York, NY


SU-E-J-137 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: To develop a robust planning paradigm which incorporates a tumor regression model into the optimization process to ensure tumor coverage in head and neck radiotherapy.

Methods: Simulation and weekly MR images were acquired for a group of head and neck patients to characterize tumor regression during radiotherapy. For each patient, the tumor and parotid glands were segmented on the MR images and the weekly changes were formulated with an affine transformation, where morphological shrinkage and positional changes are modeled by a scaling factor, and centroid shifts, respectively. The tumor and parotid contours were also transferred to the planning CT via rigid registration. To perform the robust planning, weekly predicted PTV and parotid structures were created by transforming the corresponding simulation structures according to the weekly affine transformation matrix averaged over patients other than him/herself. Next, robust PTV and parotid structures were generated as the union of the simulation and weekly prediction contours. In the subsequent robust optimization process, attainment of the clinical dose objectives was required for the robust PTV and parotids, as well as other organs at risk (OAR). The resulting robust plans were evaluated by looking at the weekly and total accumulated dose to the actual weekly PTV and parotid structures. The robust plan was compared with the original plan based on the planning CT to determine its potential clinical benefit.

Results: For four patients, the average weekly change to tumor volume and position was -4% and 1.2 mm laterally-posteriorly. Due to these temporal changes, the robust plans resulted in an accumulated PTV D95 that was, on average, 2.7 Gy higher than the plan created from the planning CT. OAR doses were similar.

Conclusion: Integration of a tumor regression model into target delineation and plan robust optimization is feasible and may yield improved tumor coverage.

Funding Support, Disclosures, and Conflict of Interest: Part of this research is supported by Varian Medical System

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