Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.
OBJECTIVE
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.


CONCLUSIONS
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.
1 Department of Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, UK.
Usman
U
Bashir
1 Department of Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, UK.
Muhammad Musib
MM
Siddique
2 Department of Histopathology, Guy's and St. Thomas' Hospitals, London, UK.
Emma
E
Mclean
1 Department of Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, UK.|3 Department of Radiology, Guy's and St. Thomas' Hospitals, London, UK.
Vicky
V
Goh
1 Department of Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, UK.|4 PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Gary J
GJ
Cook
D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008433:Mathematics; D011237:Predictive Value of Tests
heterogeneity; informatics; lung cancer; radiogenomics; radiomics; texture analysis