SynthesisCancer medicine2021
Risk prediction models for esophageal cancer: A systematic review and critical appraisal.
Synthesis in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 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.
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Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Progress and current trends in prediction models for the occurrence and prognosis of cancer and cancer-related complications: a bibliometric and visualization analysis.Frontiers in oncology · 2025Pooled it
- Profiles and Findings of Population-Based Esophageal Cancer Screening With Endoscopy in China: Systematic Review and Meta-analysis.JMIR public health and surveillance · 2023Pooled it
- Risk prediction models for esophageal cancer: A systematic review and critical appraisal.Cancer medicine · 2021Pooled it
- Development of a Combined Risk Factors Prediction Model for Esophageal Squamous Cell Carcinoma: A Secondary Analysis of the Linxian Nutrition Intervention Trial Cohort.Research square · 2026Article
- Risk score for esophageal and gastric cancer in the over 50-year-old population based on self-reported information -the RISC-GAP project.BMC gastroenterology · 2026Article
- Development and Validation of EsoTIME, a Prognostication Tool for Resected Esophageal and Gastroesophageal Cancer.Annals of surgical oncology · 2026Article
- Development and Validation of an Interpretable Machine Learning Model Based on Peripheral Blood Biomarkers for Esophageal Cancer Risk Prediction.International journal of general medicine · 2026Article
- Liquid Biopsy and Nonendoscopic Biomarkers for Precision Early Detection of Esophageal Cancer: Evidence, Risk Stratification and Translational Challenges.Journal of Cancer · 2026Review
- The neoadjuvant esophageal score: a prognostic tool for predicting survival and postoperative complications in esophageal squamous cell carcinoma.Frontiers in immunology · 2025Article
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
- Prognostic Value of UBE2S, HIF-1α, and FOXM1 Expression in Esophageal Squamous Cell Carcinoma.Analytical cellular pathology (Amsterdam) · 2025Article
- A Systematic Review of Risk Prediction Models for Esophageal Adenocarcinoma in the General Population.Gastro hep advances · 2025Review
- Review
- The Global, Regional and National Burden of Pancreatic Cancer Attributable to Smoking, 1990 to 2019: A Systematic Analysis from the Global Burden of Disease Study 2019.International journal of environmental research and public health · 2023Article
- Development and Validation of Esophageal Squamous Cell Carcinoma Risk Prediction Models Based on an Endoscopic Screening Program.JAMA network open · 2023Article
- Time-trend of the incidence and mortality of esophageal cancer from 2010 to 2018 and its statistics in 2018 in Henan, China.Annals of translational medicine · 2022Article
- Cost-effectiveness of precision screening for esophageal cancer based on individualized risk stratification in China: Real-world evidence from the ESECC trial.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
aimsEsophageal cancer risk prediction models allow for risk-stratified endoscopic screening. We aimed to assess the quality of these models developed in the general population.
methodsA systematic search of the PubMed and Embase databases from January 2000 through May 2021 was performed. Studies that developed or validated a model of esophageal cancer in the general population were included. Screening, data extraction, and risk of bias (ROB) assessment by the Prediction model Risk Of Bias Assessment Tool (PROBAST) were performed independently by two reviewers.
resultsOf the 13 models included in the qualitative analysis, 8 were developed for esophageal squamous cell carcinoma (ESCC) and the other 5 were developed for esophageal adenocarcinoma (EAC). Only two models conducted external validation. In the ESCC models, cigarette smoking was included in each model, followed by age, sex, and alcohol consumption. For EAC models, cigarette smoking and body mass index were included in each model, and gastroesophageal reflux disease, uses of acid-suppressant medicine, and nonsteroidal anti-inflammatory drug were exclusively included. The discriminative performance was reported in all studies, with C statistics ranging from 0.71 to 0.88, whereas only six models reported calibration. For ROB, all the models had a low risk in participant and outcome, but all models showed high risk in analysis, and 60% of models showed a high risk in predictors, which resulted in all models being classified as having overall high ROB. For model applicability, about 60% of these models had an overall low risk, with 30% of models of high risk and 10% of models of unclear risk, concerning the assessment of participants, predictors, and outcomes.
conclusionsMost current risk prediction models of esophageal cancer have a high ROB. Prediction models need further improvement in their quality and applicability to benefit esophageal cancer screening.
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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.