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Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges
Usman Bashir1, Muhammad Musib Siddique1, Emma Mclean2
11 Department of Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, UK.
AJR. American Journal of Roentgenology
|June 16, 2016
Summary
Texture analysis of medical images can characterize lung cancer and predict outcomes. More heterogeneous tumors often indicate more aggressive disease, but results vary.
Area of Science:
- Medical Imaging
- Radiomics
- Oncology
Background:
- Texture analysis quantifies image heterogeneity using mathematical processing.
- Previous studies suggest texture analysis aids in lung cancer characterization and outcome prediction.
Purpose of the Study:
- To review the mathematical basis of texture analysis in lung cancer imaging.
- To summarize recent literature on texture analysis applications in lung cancer.
- To discuss challenges in the clinical implementation of texture analysis.
Main Methods:
- Review of mathematical principles underlying texture analysis.
- Synthesis of current research findings on texture analysis in lung cancer imaging (FDG PET, CT).
- Identification of implementation barriers for clinical use.
Main Results:
- Texture parameters derived from FDG PET and CT scans predict lung cancer characteristics and patient outcomes.
- Increased tumor heterogeneity on imaging generally correlates with more aggressive disease and poorer outcomes.
- Tumor heterogeneity often decreases following treatment.
Conclusions:
- Texture analysis shows promise in assessing lung cancer aggressiveness and treatment response.
- Significant variability exists in reported texture analysis data and effect sizes.
- Further standardization and validation are needed for widespread clinical adoption.
