ReviewAbdominal radiology (New York)2019
Radiomics in esophageal and gastric cancer.
Review in Abdominal radiology (New York), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 3 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
54 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- 18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- 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
- Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model.European journal of medical research · 2022Trial
- Comparative Analysis of F-18 FDG PET-Based Metabolic Parameters in the Differential Diagnosis of Esophageal Squamous Cell Carcinoma and Adenocarcinoma.World journal of nuclear medicine · 2026Article
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- A Radiomics-Clinical Nomogram for Pre-Treatment Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Gastric Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Differentiating the differentiation degree of gastric cancer based on MRI radiomics.BMC medical imaging · 2025Article
- Repeatability and prognostic value of radiomic features: a study in esophageal cancer and nasopharyngeal carcinoma.Insights into imaging · 2025Article
- Radiomics to predict PNI in ESCC.Abdominal radiology (New York) · 2025Article
- Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- IntegratingBMC cancer · 2024Article
- Radiomics analysis in predicting vascular invasion in gastric cancer based on enhanced CT: a preliminary study.BMC cancer · 2024Article
- Review
- Review on radiomic analysis inCancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Article
- A CT-based radiomics nomogram involving the cystic fluid area for differentiating appendiceal mucinous neoplasms from appendicitis with intraluminal fluid.Journal of cancer research and clinical oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multi-modality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer.
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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.