Nouveaux indices en TEP/TDM : mythe et réalités

Hatt, Mathieu; Tixier, Florent; Rest, Catherine Cheze Le; Visvikis, Dimitris

doi:10.1016/j.mednuc.2015.03.187

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…More recently, a few studies have also focused on extracting features from both the PET and CT components (see the section Promising clinical results below). For a more exhaustive list, we refer the reader to other recent reviews [2–4, 9, 45–49]. From a critical review of these studies, several common issues can be identified.…”

Section: The Present: An Era Of Rapid Expansionmentioning

confidence: 99%

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Hatt

Tixier

Pierce

et al. 2016

Eur J Nucl Med Mol Imaging

414

383

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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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