Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

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-,...

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Published in	Insights into imaging Vol. 3; no. 6; pp. 573 - 589
Main Authors	Davnall, Fergus, Yip, Connie S. P., Ljungqvist, Gunnar, Selmi, Mariyah, Ng, Francesca, Sanghera, Bal, Ganeshan, Balaji, Miles, Kenneth A., Cook, Gary J., Goh, Vicky
Format	Journal Article
Language	English
Published	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2012
Subjects	Diagnostic Radiology Imaging Internal Medicine Interventional Radiology Medicine Medicine & Public Health Neuroradiology Radiology Review Ultrasound CT Texture analysis MRI Fractal analysis PET Cancer
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Abstract	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.
AbstractList	BACKGROUNDTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images METHODSImage 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. RESULTSEarly 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. CONCLUSIONThis 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. Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images 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. 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. 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. 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.
Author	Sanghera, Bal Ganeshan, Balaji Davnall, Fergus Cook, Gary J. Goh, Vicky Miles, Kenneth A. Ljungqvist, Gunnar Selmi, Mariyah Ng, Francesca Yip, Connie S. P.
Author_xml	– sequence: 1 givenname: Fergus surname: Davnall fullname: Davnall, Fergus organization: Division of Imaging Sciences and Biomedical Engineering, King’s College London – sequence: 2 givenname: Connie S. P. surname: Yip fullname: Yip, Connie S. P. organization: Department on Oncology, Guy’s & St Thomas’ NHS Foundation Trust – sequence: 3 givenname: Gunnar surname: Ljungqvist fullname: Ljungqvist, Gunnar organization: Division of Imaging Sciences and Biomedical Engineering, King’s College London – sequence: 4 givenname: Mariyah surname: Selmi fullname: Selmi, Mariyah organization: Division of Imaging Sciences and Biomedical Engineering, King’s College London – sequence: 5 givenname: Francesca surname: Ng fullname: Ng, Francesca organization: Paul Strickland Scanner Centre, Mount Vernon Hospital – sequence: 6 givenname: Bal surname: Sanghera fullname: Sanghera, Bal organization: Paul Strickland Scanner Centre, Mount Vernon Hospital – sequence: 7 givenname: Balaji surname: Ganeshan fullname: Ganeshan, Balaji organization: Clinical Imaging Sciences Centre, Brighton and Sussex Medical School – sequence: 8 givenname: Kenneth A. surname: Miles fullname: Miles, Kenneth A. organization: Clinical Imaging Sciences Centre, Brighton and Sussex Medical School – sequence: 9 givenname: Gary J. surname: Cook fullname: Cook, Gary J. organization: Division of Imaging Sciences & Biomedical Engineering, King’s College London, PET Imaging Centre – sequence: 10 givenname: Vicky surname: Goh fullname: Goh, Vicky email: vicky.goh@kcl.ac.uk organization: Division of Imaging Sciences and Biomedical Engineering, King’s College London, Department on Oncology, Guy’s & St Thomas’ NHS Foundation Trust, Chair of Clinical Cancer Imaging, Lambeth Wing, St Thomas Hospital
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/23093486$$D View this record in MEDLINE/PubMed
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PageCount	17
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_3505569 proquest_miscellaneous_1220362267 crossref_primary_10_1007_s13244_012_0196_6 pubmed_primary_23093486 springer_journals_10_1007_s13244_012_0196_6
PublicationCentury	2000
PublicationDate	2012-12-01
PublicationDateYYYYMMDD	2012-12-01
PublicationDate_xml	– month: 12 year: 2012 text: 2012-12-01 day: 01
PublicationDecade	2010
PublicationPlace	Berlin/Heidelberg
PublicationPlace_xml	– name: Berlin/Heidelberg – name: Germany
PublicationTitle	Insights into imaging
PublicationTitleAbbrev	Insights Imaging
PublicationTitleAlternate	Insights Imaging
PublicationYear	2012
Publisher	Springer Berlin Heidelberg
Publisher_xml	– name: Springer Berlin Heidelberg
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Snippet	Background Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images Methods Image texture analysis is an... Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images Image texture analysis is an approach of quantifying... BACKGROUNDTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images METHODSImage texture analysis is an approach...
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SubjectTerms	Diagnostic Radiology Imaging Internal Medicine Interventional Radiology Medicine Medicine & Public Health Neuroradiology Radiology Review Ultrasound
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Title	Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
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