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Cited by 773

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

Lubner, Meghan G., Smith, Andrew D., Sandrasegaran, Kumar, Sahani, Dushyant V., Pickhardt, Perry J.·2017·RadioGraphics
DOI 10.1148/rg.2017170056
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Abstract

This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of “radiomics” that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions. Pretreatment CT texture features are associated with histopathologic correlates such as tumor grade, tumor cellular processes such as hypoxia or angiogenesis, and genetic features such as KRAS or epidermal growth factor receptor (EGFR) mutation status. In addition, and likely as a result, these CT texture features have been linked to prognosis and clinical outcomes in some tumor types. CTTA CT texture analysis has also been used to assess response to therapy, with decreases in tumor heterogeneity generally associated with pathologic response and improved outcomes. A variety of nononcologic applications of CTTA CT texture analysis are emerging, particularly quantifying fibrosis in the liver and lung. Although CTTA CT texture analysis seems to be a promising imaging biomarker, there is marked variability in methods, parameters reported, and strength of associations with biologic correlates. Before CTTA CT texture analysis can be considered for widespread clinical implementation, standardization of tumor segmentation and measurement techniques, image filtration and postprocessing techniques, and methods for mathematically handling multiple tumors and time points is needed, in addition to identification of key texture parameters among hundreds of potential candidates, continued investigation and external validation of histopathologic correlates, and structured reporting of findings. ©RSNA, 2017

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Research works printed but not linked: 19

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Metadata

Authors
Lubner, M. G., Smith, A. D., Sandrasegaran, K., Sahani, D. V., & Pickhardt, P. J.
Venue
RadioGraphics
Publisher
Radiological Society of North America (RSNA)
Published
2017-09
Volume
37
Issue
5
Pages
1483-1503
ISSN
  • 0271-5333 (ISSN-L)
  • 1527-1323
DOI
10.1148/rg.2017170056
OpenAlex
W2755496220
Cited by
773
References
57
FWCI
48.66
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. https://app.cochat.ai/papers/10.1148/rg.2017170056
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Lubner, M. G., Smith, A. D., Sandrasegaran, K., Sahani, D. V., & Pickhardt, P. J. (2017). CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. RadioGraphics. https://doi.org/10.1148/rg.2017170056

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