SU‐G‐TeP3‐14: Three‐Dimensional Cluster Model in Inhomogeneous Dose Distribution

Abstract

Purpose:We aim to investigate 3D cluster formation in inhomogeneous dose distribution to search for new models predicting radiation tissue damage and further leading to new optimization paradigm for radiotherapy planning.Methods:The aggregation of higher dose in the organ at risk (OAR) than a preset threshold was chosen as the cluster whose connectivity dictates the cluster structure. Upon the selection of the dose threshold, the fractional density defined as the fraction of voxels in the organ eligible to be part of the cluster was determined according to the dose volume histogram (DVH). A Monte Carlo method was implemented to establish a case pertinent to the corresponding DVH. Ones and zeros were randomly assigned to each OAR voxel with the sampling probability equal to the fractional density. Ten thousand samples were randomly generated to ensure a sufficient number of cluster sets. A recursive cluster searching algorithm was developed to analyze the cluster with various connectivity choices like 1‐, 2‐, and 3‐connectivity. The mean size of the largest cluster (MSLC) from the Monte Carlo samples was taken to be a function of the fractional density. Various OARs from clinical plans were included in the study.Results:Intensive Monte Carlo study demonstrates the inverse relationship between the MSLC and the cluster connectivity as anticipated and the cluster size does not change with fractional density linearly regardless of the connectivity types. An initially‐slow‐increase to exponential growth transition of the MSLC from low to high density was observed. The cluster sizes were found to vary within a large range and are relatively independent of the OARs.Conclusion:The Monte Carlo study revealed that the cluster size could serve as a suitable index of the tissue damage (percolation cluster) and the clinical outcome of the same DVH might be potentially different.