Evidence map›Paper›PMID 36669752›Full record

ArticleClinics and research in hepatology and gastroenterology2023

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients.

Kai Man Alexander Ho, Avi Rosenfeld, Áine Hogan, Hazel McBain, Margaret Duku, Paul Bd Wolfson, Ashley Wilson, Sharon My Cheung, Laura Hennelly, Lester Macabodbod and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinics and research in hepatology and gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. 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 · 2025
    Pooled it
  2. Fivefold Cross-Validation Approach in Evaluating the Robustness of Machine Learning Models for Prediction of Esophageal Cancer.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Kai Man Alexander HoDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK. Electronic address: alexander.ho@ucl.ac.uk.
Avi RosenfeldDepartment of Computer Science, Jerusalem College of Technology, Havaad Haleumi 21, Givat Mordechai 91160 Jerusalem, Israel.
Áine HoganDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Hazel McBainDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Margaret DukuDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Paul Bd WolfsonDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Ashley WilsonDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Sharon My CheungDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Laura HennellyDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
Lester MacabodbodDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
David G GrahamDepartment of Gastrointestinal Services, University College London Hospital, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK.
Vinay SehgalDepartment of Gastrointestinal Services, University College London Hospital, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK.
Amitava BanerjeeInstitute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, UK; Department of Cardiology, St Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK.
Laurence B LovatDivision of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Department of Gastrointestinal Services, University College London Hospital, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK.
SPIT Study Group Collaborators

Funding

Wellcome Trust
6 · The paper itself

Abstract

introductionOesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses.

methodsWe used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset.

results807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified.

conclusionsWe have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.

Indexed as

COVID-19Esophageal NeoplasmsCross-Sectional StudiesHumansPandemicsProspective StudiesRisk FactorsState MedicineXX

Identifiers

PMID36669752
PMCPMC10602932

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.