Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.
发表日期： 2016.09.01 来源：American Journal of Roentgenology. 2016;207: 534-543.
Usman Bashir1, Muhammad Musib Siddique1, Emma Mclean2, Vicky Goh1,3 and Gary J. Cook1,4
1. Department of Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, UK.
2. Department of Histopathology, Guy's and St. Thomas' Hospitals, London, UK.
3. Department of Radiology, Guy's and St. Thomas' Hospitals, London, UK.
4. PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis.
Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.