ArticleEuropean journal of nuclear medicine and molecular imaging2017
Texture analysis of
Article 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 75 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
75 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The prognostic value of pretreatment [European radiology · 2025Pooled it
- Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.European journal of nuclear medicine and molecular imaging · 2021Pooled it
- Consensus statement of the Hellenic and Cypriot Oesophageal Cancer Study Group on the diagnosis, staging and management of oesophageal cancer.Updates in surgery · 2019Guideline
- Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.Radiation oncology (London, England) · 2022Trial
- Prognostic Value of Pretreatment [18F]FDG PET/CT Metabolic Parameters for Overall Survival in Esophageal Cancer: A Single-Center Retrospective Cohort with Long-Term Follow-Up.Diagnostics (Basel, Switzerland) · 2026Article
- The FAP-FDG Integration Score (FFIS): Development and prognostic validation of a dual-tracer PET phenotype for solid tumors.European journal of nuclear medicine and molecular imaging · 2026Article
- Radiomic Analysis Applied to Pretreatment Gd-EOB-DTPA-enhanced MR Imaging Predicts Response to Selective Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.BMC gastroenterology · 2025Article
- Importance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography heterogeneity indices in non-small cell lung cancer.Revista da Associacao Medica Brasileira (1992) · 2025Article
- Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool.Physics in medicine and biology · 2024Article
- CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn's disease.Insights into imaging · 2024Article
- Adrenal indeterminate nodules: CT-based radiomics analysis of different machine learning models for predicting adrenal metastases in lung cancer patients.Frontiers in oncology · 2024Article
- Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients.Scientific reports · 2023Article
- The Prognostic Value of SequentialWorld journal of nuclear medicine · 2023Article
- Four-dimensional quantitative analysis using FDG-PET in clinical oncology.Japanese journal of radiology · 2023Review
- Risk stratification of oesophageal squamous cell carcinoma using change in total lesion glycolysis and number of PET-positive lymph nodes.British journal of cancer · 2023Article
- Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer.Scientific reports · 2023Article
15 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis retrospective study was done to examine whether the heterogeneity in primary tumour F-18-fluorodeoxyglucose (
methodsThe enrolled 52 patients with esophageal cancer underwent
resultsThirty four non-responders showed significantly higher MTV (p = 0.006), TLG (p = 0.007), intensity variability (IV; p = 0.003) and size-zone variability (SZV; p = 0.004) than 18 responders. The positive and negative predictive values for non-responders were 77 % and 69 % in MTV, 76 % and 100 % in TLG, 78 % and 67 % in IV and 78 % and 82 % in SZV, respectively. Although PFS and OS were significantly shorter in patients with high MTV (PFS, p = 0.018; OS, p = 0.014), TLG (PFS, p = 0.009; OS, p = 0.025), IV (PFS, p = 0.013; OS, p = 0.007) and SZV (PFS, p = 0.010; OS, p = 0.007) at univariate analysis, none of them was an independent factor, while lymph node status, stage and tumour response status were independent factors at multivariate analysis.
conclusionTexture features IV and SZV, and volumetric parameters MTV and TLG can predict tumour response, but all of them have limited value in prediction of prognosis of patients with esophageal cancer treated by CRT.
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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.