ReviewEuropean journal of nuclear medicine and molecular imaging2017
Characterization of PET/CT images using texture analysis: the past, the present… any future?
Review in European journal of nuclear medicine and molecular imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 246 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
246 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.European radiology · 2020Trial
- 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier.Scientific reports · 2019Trial
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Development of aAnnals of nuclear medicine · 2026Article
- Hyperspectral imaging and healthy aging: an observational study using hand skin as surface for monitoring healthy aging processes.Biogerontology · 2026Observational
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.Scientific reports · 2026Article
- Multimodal Radiogenomic Imaging in Oropharyngeal Squamous Cell Carcinoma: Implications for Dentomaxillofacial Radiology.Medical sciences (Basel, Switzerland) · 2026Review
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- Radiomics-based high-resolution CT analysis for differentiating primary tumor sources of pulmonary metastases.Frontiers in oncology · 2026Article
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- Using Imaris to rigorously track PET-defined sites of lung inflammation inAmerican journal of diagnostic imaging · 2026Article
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- An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.European journal of nuclear medicine and molecular imaging · 2025Article
- Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer.Cancers · 2025Article
- Effect of reconstruction settings on radiomics feature robustness in positron emission tomography images: a clinical study.Quantitative imaging in medicine and surgery · 2025Article
186 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.