Iymad R. Mansour, Nelson Miksys, Luc Beaulieu, Eric Vigneault, Rowan M. Thomson
{"title":"Haralick texture feature analysis for Monte Carlo dose distributions of permanent implant prostate brachytherapy","authors":"Iymad R. Mansour, Nelson Miksys, Luc Beaulieu, Eric Vigneault, Rowan M. Thomson","doi":"arxiv-2409.10324","DOIUrl":null,"url":null,"abstract":"Purpose: Demonstrate quantitative characterization of 3D patient-specific\nabsorbed dose distributions using Haralick texture analysis and interpret\nmeasures in terms of underlying physics and radiation dosimetry. Methods:\nRetrospective analysis is performed for 137 patients who underwent permanent\nimplant prostate brachytherapy using two simulation conditions: ``TG186''\n(realistic tissues including 0-3.8% intraprostatic calcifications; interseed\nattenuation) and ``TG43'' (water-model; no interseed attenuation). Haralick\nfeatures (homogeneity, contrast, correlation, local homogeneity, entropy) are\ncalculated using the original Haralick formalism, and a modified approach\ndesigned to reduce grey-level quantization sensitivity. Trends in textural\nfeatures are compared to clinical dosimetric measures (D90; minimum absorbed\ndose to the hottest 90% of a volume) and changes in patient target volume %\nintraprostatic calcifications by volume (%IC). Results: Both original and\nmodified measures quantify the spatial differences in absorbed dose\ndistributions. Strong correlations between differences in textural measures\ncalculated under TG43 and TG186 conditions and %IC are observed for all\nmeasures. For example, differences between measures of contrast and correlation\nincrease and decrease respectively as patients with higher levels of %IC are\nevaluated, reflecting the large differences across adjacent voxels (higher dose\nin voxels with calcification) when calculated under TG186 conditions.\nConversely, the D90 metric is relatively weakly correlated with textural\nmeasures, as it generally does not characterize the spatial distribution of\nabsorbed dose. Conclusion: patient-specific 3D dose distributions may be\nquantified using Haralick analysis, and trends may be interpreted in terms of\nfundamental physics.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Medical Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Demonstrate quantitative characterization of 3D patient-specific
absorbed dose distributions using Haralick texture analysis and interpret
measures in terms of underlying physics and radiation dosimetry. Methods:
Retrospective analysis is performed for 137 patients who underwent permanent
implant prostate brachytherapy using two simulation conditions: ``TG186''
(realistic tissues including 0-3.8% intraprostatic calcifications; interseed
attenuation) and ``TG43'' (water-model; no interseed attenuation). Haralick
features (homogeneity, contrast, correlation, local homogeneity, entropy) are
calculated using the original Haralick formalism, and a modified approach
designed to reduce grey-level quantization sensitivity. Trends in textural
features are compared to clinical dosimetric measures (D90; minimum absorbed
dose to the hottest 90% of a volume) and changes in patient target volume %
intraprostatic calcifications by volume (%IC). Results: Both original and
modified measures quantify the spatial differences in absorbed dose
distributions. Strong correlations between differences in textural measures
calculated under TG43 and TG186 conditions and %IC are observed for all
measures. For example, differences between measures of contrast and correlation
increase and decrease respectively as patients with higher levels of %IC are
evaluated, reflecting the large differences across adjacent voxels (higher dose
in voxels with calcification) when calculated under TG186 conditions.
Conversely, the D90 metric is relatively weakly correlated with textural
measures, as it generally does not characterize the spatial distribution of
absorbed dose. Conclusion: patient-specific 3D dose distributions may be
quantified using Haralick analysis, and trends may be interpreted in terms of
fundamental physics.