Characterization of PET/CT images using texture analysis: the past, the present… any future?
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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.
KeywordsPET/CT Image texture Heterogeneity Critical review Recommendations
The authors thank Issam El Naqa, Philippe Lambin, Hugo Aerts, Ralph Leijenaar, Floris Van Velden, Martin Vallières, Arman Rahmim, Matt Nyflot and Art Chaovalitwongse, as well as the members of the RSNA Quantitative Imaging Biomarkers Alliance and the NCI Quantitative Imaging Network for many helpful discussions.
Compliance with ethical standards
Supported in part by NIH grant U01-CA148131.
This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01. With the support of the National Institute of Cancer (INCa project #C14020NS).
Conflicts of interest
P.E.K. has received a research grant from GE Healthcare and is cofounder of PET/X LLC.
The other authors declare that they have no conflicts of interest.
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