SU-E-T-551: PTV Is the Worst-Case of CTV in Photon Therapy

Abstract

Purpose: To examine the supposition of the static dose cloud and adequacy of the planning target volume (PTV) dose distribution as the worst-case representation of clinical target volume (CTV) dose distribution for photon therapy in head and neck (H&N) plans. Methods: Five diverse H&N plans clinically delivered at our institution were selected. Isocenter for each plan was shifted positively and negatively in the three cardinal directions by a displacement equal to the PTV expansion on the CTV (3 mm) for a total of six shifted plans per original plan. The perturbed plan dose was recalculated in Eclipse (AAA v11.0.30) using the same, fixed fluence map as the original plan. The dose distributions for all plans were exported from the treatment planning system to determine the worst-case CTV dose distributions for each nominal plan. Two worst-case distributions, cold and hot, were defined by selecting the minimum or maximum dose per voxel from all the perturbed plans. The resulting dose volume histograms (DVH) were examined to evaluate the worst-case CTV and nominal PTV dose distributions. Results: Inspection demonstrates that the CTV DVH in the nominal dose distribution is indeed bounded by the CTV DVHs in the worst-case dose distributions. Furthermore, comparison of the D95% for the worst-case (cold) CTV and nominal PTV distributions by Pearson's chi-square test shows excellent agreement for all plans. Conclusion: The assumption that the nominal dose distribution for PTV represents the worst-case dose distribution for CTV appears valid for the five plans under examination. Although the worst-case dose distributions are unphysical since the dose per voxel is chosen independently, the cold worst-case distribution serves as a lower bound for the worst-case possible CTV coverage. Minor discrepancies between the nominal PTV dose distribution and worst-case CTV dose distribution are expected since the dose cloud is not strictly static. This research was supported by the NCI through grant K25CA168984, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, and by the Fraternal Order of Eagles Cancer Research Fund, the Career Development Award Program at Mayo Clinic.