![]() 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.Disney has done it again with another enchanting movie, Encanto. 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. Tumours are heterogeneous entities at all scales (macroscopic, physiological, microscopic, genetic). As a multimodal imaging modality, PET/CT is a promising tool for noninvasive exploration of intratumour heterogeneity at the macroscopic scale in both the anatomical and functional dimensions. The term heterogeneity usually conveys different meanings depending on the image modality. When considering the PET component, it refers to radiotracer uptake spatial distribution, which may reflect, depending on the radiotracer used, the combination of underlying biological processes such as metabolism, hypoxia, cellular proliferation, vascularization and necrosis. Regarding the low-dose CT component of PET/CT, usually without contrast enhancement, heterogeneity refers to the variability in tissue density, which may result from spatially varying vascularization, necrosis or cellularity, as well as the proportions of fat, air and water. With other modalities such as contrast-enhanced CT, as well as in MRI using various sequences (for example, T1, T2, FLAIR, DCE-MRI), heterogeneity can also include the spatial variability of vessel density, perfusion, proton density and physiological tissue characteristics. The heterogeneity of image voxel intensities can be quantified by different image processing and analysis methods, including texture analysis (TA), fractal analysis, shape models, intensity histogram analysis and filtering combined with statistical and frequency-based methods. This critical review focuses on the use of TA in PET/CT images, although for completeness a section dedicated to alternative heterogeneity metrics can be found in Supplementary material section 1. Systematically constructing higher-dimensional information from data falls under the general rubric of ‘-omics’, which includes genomics, proteomics and others. The potential of such an approach is to quantify properties of tissues and/or organs beyond the capability of visual interpretation or simple metrics.Įxtracting a large number of features from images (including TA metrics, shape descriptors and other quantitative metrics) has become popular under the denomination radiomics. ![]() The use of TA has been widespread in MR and CT imaging since the early 1990s and more recently (end of the 2000s) for PET intratumour heterogeneity characterization. PET images have a priori less-favourable properties for TA than MRI or CT, due to a lower signal-to-noise ratio and spatial resolution, as well as poorer spatial sampling.
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