SU‐F‐BRB‐10: A Statistical Voxel Based Normal Organ Dose Prediction Model for Coplanar and Non‐Coplanar Prostate Radiotherapy

Abstract

Purpose:Knowledge learned from previous plans can be used to guide future treatment planning. Existing knowledge‐based treatment planning methods study the correlation between organ geometry and dose volume histogram (DVH), which is a lossy representation of the complete dose distribution. A statistical voxel dose learning (SVDL) model was developed that includes the complete dose volume information. Its accuracy of predicting volumetric‐modulated arc therapy (VMAT) and non‐coplanar 4π radiotherapy was quantified. SVDL provided more isotropic dose gradients and may improve knowledge‐based planning.Methods:12 prostate SBRT patients originally treated using two full‐arc VMAT techniques were re‐planned with 4π using 20 intensity‐modulated non‐coplanar fields to a prescription dose of 40 Gy. The bladder and rectum voxels were binned based on their distances to the PTV. The dose distribution in each bin was resampled by convolving to a Gaussian kernel, resulting in 1000 data points in each bin that predicted the statistical dose information of a voxel with unknown dose in a new patient without triaging information that may be collectively important to a particular patient. We used this method to predict the DVHs, mean and max doses in a leave‐one‐out cross validation (LOOCV) test and compared its performance against lossy estimators including mean, median, mode, Poisson and Rayleigh of the voxelized dose distributions.Results:SVDL predicted the bladder and rectum doses more accurately than other estimators, giving mean percentile errors ranging from 13.35–19.46%, 4.81–19.47%, 22.49–28.69%, 23.35–30.5%, 21.05–53.93% for predicting mean, max dose, V20, V35, and V40 respectively, to OARs in both planning techniques. The prediction errors were generally lower for 4π than VMAT.Conclusion:By employing all dose volume information in the SVDL model, the OAR doses were more accurately predicted. 4π plans are better suited for knowledge‐based planning than the VMAT plans that are strongly biased in its dose gradient orientation.This project is supported by Varian Medical Systems.