Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Open accessAbstract
BACKGROUND: Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images METHODS: Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods. RESULTS: Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice. CONCLUSION: This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging. TEACHING POINTS : • Tumor spatial heterogeneity is an important prognostic factor. • Image texture analysis is an approach of quantifying heterogeneity. • Different methods can be applied, including statistical-, model-, and transform-based methods. • Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.
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In the past 10 years, radiomics and radiogenomics research in tomographic imaging has increased dramatically.
“In the past 10 years, radiomics and radiogenomics research in tomographic imaging (CT, MR imaging, and PET) has increased dramatically.”
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- Authors
- Davnall, F., Yip, C. S. P., Ljungqvist, G., Selmi, M., Ng, F., Sanghera, B., Ganeshan, B., Miles, K. A., Cook, G. J., & Goh, V.
- Affiliations
- King's College London (GB)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Mount Vernon Hospital (GB)
- Brighton and Sussex Medical School (GB)
- St Thomas' Hospital (GB)
- Corresponding author
- Goh, Vicky
- ORCID
- Venue
- Insights into Imaging
- Published
- 2012-10-24
- Volume
- 3
- Issue
- 6
- Pages
- 573-589
- ISSN
- 1869-4101 (ISSN-L)
- PMC
- PMC3505569
- OpenAlex
- W2052295501
- Cited by
- 878
- References
- 65
- Citations by year
- 2026: 28 · 2025: 42 · 2024: 62 · 2023: 67 · 2022: 86 · 2021: 105 · 2020: 98 · 2019: 92 · 2018: 91 · 2017: 75 · 2016: 63 · 2015: 26 · 2014: 32 · 2013: 9
- FWCI
- 16.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Insights into Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- King's College Hospital NHS Foundation Trust
- King's College London
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- tumor heterogeneity, texture analysis, spatial heterogeneity, CT, magnetic resonance imaging, PET, tumor staging, therapy response assessment, prognostic factors, tumor imaging
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