Journal
TRANSLATIONAL CANCER RESEARCH
Volume 5, Issue 4, Pages 371-+Publisher
AME PUBL CO
DOI: 10.21037/tcr.2016.07.18
Keywords
Radiomics; head and neck cancer; texture analysis; machine learning; imaging biomarker
Categories
Funding
- National Institutes of Health National Cancer Institute [K12 CA088084-06, L30 CA136381-02, P50CA097007-10]
- National Institutes of Health National Institute of Dental and Craniofacial Research research grant [R56DE025248-01, R01DE025248-01]
- General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award
- Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant
- Center for Radiation Oncology Research at MD Anderson Cancer Center
- MD Anderson Institutional Research Grant Program
- National Institutes of Health Cancer Center Support (Core) Grant [CA016672]
- Elekta AB
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In the context of clinical oncology, a fundamental goal of radiomics is the extraction of large amounts of quantitative features whose subsequent analysis can be used for decision support towards personalized and actionable cancer care. Head and neck cancers present a unique set of diagnostic and therapeutic challenges by nature of its complex anatomy and heterogeneity. Radiomics holds the potential to address these barriers, but only if as a collective field we direct future effort towards investigating specific oncologic function and oncologic outcomes, with external validation and collaborative multi-institutional efforts to begin standardizing and refining radiomic signatures. Here we present an overview of radiomic texture analysis methods as well as the software infrastructure, review the developments of radiomics in head and neck cancer applications, discuss unmet challenges, and propose key recommendations for moving the field forward.
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