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NCT Number: NCT07605312

AI-Based Risk Prediction Model for Upper Digestive Tract Cancer

Upper digestive tract cancers are often preceded by pre-malignant lesions, but there is limited evidence regarding optimal risk prediction models and screening strategies for disease progression and cancer development. This prospective multicenter cohort study aims to establish a longitudinal database integrating clinical information, endoscopic findings, pathology, genetics, epigenetics, and gastrointestinal microbiota data from subjects undergoing upper digestive tract endoscopy.

The study will develop explainable artificial intelligence (AI)-based risk prediction models to identify factors associated with disease progression, treatment response, and cancer development. Participants will be followed longitudinally to evaluate changes in lesion severity and clinical outcomes.

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Key information

Conditions

Gastric Cancer (GC) Atrophic Gastritis Digestive System Diseases Digestive System Neoplasms Dysplasia Stomach Esophageal Cancer (EsC) Esophageal Diseases Esophageal Neoplasms Gastric Intestinal Metaplasia Gastritis Gastritis, Atrophic Gastroenteritis Gastrointestinal Diseases Gastrointestinal Neoplasms Head and Neck Neoplasms Neoplasms Neoplasms by Site Premalignant Lesion Stomach Diseases Stomach Neoplasms

Age range

40 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

Objectives:

There is no solid evidence about the risk prediction model and screening duration for upper digestive tract pre-malignant lesions and its progression. There is also no longitudinal study combining multi-omic approach, endoscopic and pathologic images and the association with disease development. Hence we design a prospective cohort targeting upper digestive tract disease progression and cancer development, with standardized clinical data collection, quality control and explainable AI (artificial intellegence) model for better reliability of risk prediction model.

Aims:

We aim to develop risk prediction model for the progression of upper digestive tract disease and cancer development.

Methods:

The study is disigned as a multi-center prospective cohort, targeting subjects undergoing upper digestive tract endoscopy. The development of AI risk prediction models will combine endoscopic pre-malignant lesion, pathology, genetics, epigenetics, oro-gastro-intestinal microbiota, and follow-up longitudinally with change in lesion severity, medication response, cancer development.

Outcome measurement:

Primary endpoints: upper digestive tract cancer development. Secondary endpoints: progression in pre-malignant lesions, recurrent colon polyps, other cancer developement, metabolic and cardiovascular disease, response to medication in gastro-esophageal reflux and dyspepsia population.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients undergoing upper gastrointestinal endoscopy.
  • Patients with at least one of the following conditions or indications:
  • Previous or current Helicobacter pylori infection (confirmed by serology, histopathology, urea breath test, rapid urease test, or stool antigen test);
  • Dyspeptic symptoms;
  • Gastroesophageal reflux disease;
  • History of oral, oropharyngeal, or hypopharyngeal squamous cell carcinoma;
  • Barrett's esophagus;
  • Gastric premalignant lesions (intestinal metaplasia or atrophic gastritis);
  • Gastric subepithelial lesions.

Exclusion criteria

-

Treatment and study plan

Primary outcomes

  1. Number of participants with upper digestive tract cancer confirmed by histopathological examination

    Time frame: "From enrollment to the end of follow-up at 10 years"

    Upper digestive tract cancer development will be defined as newly diagnosed upper digestive tract malignancy during follow-up, including esophageal cancer and gastric cancer. Diagnosis will be confirmed by histopathological examination of biopsy or resection specimens.

Secondary outcomes

  1. Number of participants with progression of gastric premalignant lesions assessed by OLGA, OLGIM, and EGGIM staging systems

    Time frame: From enrollment to the end of follow-up at 10 years

    Progression of premalignant lesions will be defined as worsening of gastric atrophy and intestinal metaplasia during follow-up based on histological and endoscopic assessment. Histological progression will be evaluated using changes in OLGA and OLGIM stages, while endoscopic progression will be assessed using EGGIM scores. Progression is defined as an increase in stage or score compared with baseline evaluation.

  2. Recurrent colon polyps

    Time frame: From enrollment to the end of follow-up at 10 years

  3. Number of participants with non-upper digestive tract malignancies confirmed by histopathological examination

    Time frame: From enrollment to the end of follow-up at 10 years

    Other cancer development will be defined as newly diagnosed malignancies other than upper digestive tract cancers during follow-up, including but not limited to colorectal cancer, hepatobiliary cancer, pancreatic cancer, lung cancer, breast cancer, prostate cancer, and hematologic malignancies. Diagnosis will be confirmed by histopathological examination, imaging findings, or cancer registry records.

  4. Number of participants with newly diagnosed metabolic and cardiovascular diseases

    Time frame: From enrollment to the end of follow-up at 10 years

    Metabolic and cardiovascular disease development will be defined as newly diagnosed metabolic or cardiovascular conditions during follow-up, including diabetes mellitus, hypertension, dyslipidemia, coronary artery disease, cerebrovascular disease, heart failure, and peripheral arterial disease. Diagnoses will be confirmed based on medical records and clinical assessments.

  5. Number of participants with symptom or endoscopic improvement after medication treatment in gastroesophageal reflux disease and dyspepsia populations

    Time frame: From enrollment to the end of follow-up at 10 years

    Treatment response will be defined as improvement of reflux and dyspeptic symptoms after medical therapy during follow-up. Symptom response will be assessed based on patient-reported symptom improvement and medical records. Endoscopic response will be assessed endoscopically

Study contacts

Contact information is provided by the study sponsor or research team.

Jyh-Ming Liou, MD, PhD

CONTACT

[email protected]

+886972651883

Tzu-Chan Hong, MD, PhD

CONTACT

[email protected]

+886-2-2322-0322 ext. 238061

Sponsors and collaborators

Lead sponsor

National Taiwan University Hospital

Other

Registry information

Official study title

Development of Artificial Intelligence Risk Prediction Model for Upper Digestive Tract Cancer Using High Resolution Endoscopic Image, Digital Pathology, Genetics, and Oro-gastro-intestinal Microbiota.

Important dates

Study start
2026
Primary completion
2030
Study completion
2030
First posted
May 22, 2026
Registry last updated
May 28, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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