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Characterization of PET/CT images using texture analysis: the past, the present… any future?
Hatt, Mathieu; Tixier, Florent; Pierce, Larry; Kinahan, Paul E; Le Rest, Catherine Cheze; Visvikis, Dimitris.
Afiliação
  • Hatt M; INSERM, UMR 1101, LaTIM, University of Brest IBSAM, Brest, France. hatt@univ-brest.fr.
  • Tixier F; Nuclear Medicine, University Hospital, Poitiers, France.
  • Pierce L; Medical school, EE DACTIM, University of Poitiers, Poitiers, France.
  • Kinahan PE; Imaging Research Laboratory, University of Washington, Seattle, WA, USA.
  • Le Rest CC; Imaging Research Laboratory, University of Washington, Seattle, WA, USA.
  • Visvikis D; Nuclear Medicine, University Hospital, Poitiers, France.
Eur J Nucl Med Mol Imaging ; 44(1): 151-165, 2017 Jan.
Article em En | MEDLINE | ID: mdl-27271051
After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aumento da Imagem / Imageamento Tridimensional / Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada / Previsões Tipo de estudo: Guideline / Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aumento da Imagem / Imageamento Tridimensional / Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada / Previsões Tipo de estudo: Guideline / Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article