SU-G-TeP1-09: Modality-Specific Dose Gradient Modeling for Prostate IMRT Using Spherical Distance Maps of PTV and Isodose Contours

Abstract

Purpose: Overlapping volume histogram (OVH) and distance-to-target histogram (DTH) calculations rely on the assumption that dose gradients are symmetric with respect to primary target volume (PTV) expansion and minimum distance to PTV surface, respectively. It is desirable to lift this assumption and instead account for achievable modality-specific dose gradients (MSDG) for a given PTV shape. Methods: From a library of 96 prostate 7-beam IMRT plans, we computed spherical distance maps (SDMs) for PTVs and 3 iso-dose contours. Each SDM contains the minimum distances between plan isocenter and the object's surface for a fixed set of azimuthal and polar angles. We performed principal component analysis (PCA)-based missing data recovery with PTV SDM as input and a single iso-dose contour SDM as output. Repeating this process for the set of iso-dose contours sparsely reconstructed the MSDG for a given PTV. DVH points were computed from the MSDG for bladder and rectum (OARs) as a natural way of casting patient-specific geometric information into modality-specific dose space (vs. OVH and DTH where data is purely geometric). For comparison, we implemented a cumulative (c)DTH-based prediction algorithm, and produced DVH for both OARs separately. We then computed linear regressions between each method and the DVH of the original patient plan for three different OAR DVH points. Results: R-squared for MSD-Gcomputed DVH vs. original plan DVH at V90%, V80% and V60% of max dose were 0.86, 0.83, and 0.78, for bladder and 0.36, 0.52, and 0.55 for rectum, respectively; R-squared for cDTH-based predicted DVH vs. original plan DVH were 0.67, 0.81, and 0.83, for bladder and 0.44, 0.50, and 0.51, for rectum, respectively. Conclusion: By simply modeling the MSDG about a given PTV, we are able to reproduce DVHs consistent with a validated cDTH-based DVH prediction. Regression analysis suggests MSDG could further improve predictions.