Early Gastric CancerXAI-EGCNCT07551466
AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging
An observational study of Early Gastric Cancer, run by The First Affiliated Hospital of Soochow University at 1 site in China.
Trial summary
Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Early Gastric Cancer
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2026to 1 Feb 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with suspected gastric lesions who underwent endoscopic examination and endoscopic submucosal dissection (ESD), with available white-light imaging (WLI), magnifying endoscopy with narrow-band imaging (ME-NBI), and histopathological diagnosis at participating centers.
Who may be able to join
- Age ≥18 years
- Suspicious gastric lesions identified on white-light imaging (WLI)
- Preoperative biopsy indicating precancerous lesions (dysplasia or intraepithelial neoplasia) or adenocarcinoma, with preoperative magnifying endoscopy with narrow-band imaging (ME-NBI) performed
- Patients meeting the absolute indications for endoscopic submucosal dissection (ESD) and who underwent ESD
Who cannot join
- Non-adenocarcinoma histological types (e.g., lymphoma)
- Patients who did not undergo ME-NBI examination or did not receive ESD
- Lesions invading the muscularis propria or deeper layers
- Missing or indeterminate postoperative histopathological results
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Soochow University
Suzhou
Lihe Liu, +8615943593759, lhliu2042@stu.suda.edu.cn
About this study
Official title: Development and Validation of an Explainable Artificial Intelligence Model for Early Gastric Cancer Diagnosis Using Multimodal Endoscopic Imaging
Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging.
This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support.
Read more
The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.
Read the detailed description
This study is designed to develop and evaluate an explainable multimodal artificial intelligence (AI) model for the diagnosis of early gastric cancer (EGC) based on endoscopic imaging.
Patients who underwent endoscopic submucosal dissection (ESD) will be collected from one or more medical centers. Eligible patients will include those with histopathologically confirmed early gastric cancer or non-cancerous lesions who have undergone both white-light imaging (WLI) and image-enhanced endoscopy. For each lesion, representative endoscopic images will be selected according to predefined quality criteria.
The study will be conducted in several steps. First, a lesion detection model will be developed to identify regions of interest in endoscopic images. Second, quantitative image features, including color, texture, morphological, and mucosal structural features, will be extracted from the detected regions. Third, deep learning models will be used to derive predictive features from both WLI and image-enhanced endoscopic images. These features will be integrated with clinical variables to construct a multimodal prediction model.
The dataset will be divided into training, validation, and testing subsets. Model performance will be evaluated using standard diagnostic metrics, including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. Calibration performance and clinical utility may also be assessed using calibration curves and decision curve analysis.
To enhance model interpretability, feature contribution will be analyzed using Shapley additive explanations (SHAP), and visualization techniques such as gradient-weighted class activation mapping (Grad-CAM) will be applied to highlight regions of interest contributing to model predictions.
This study aims to develop an accurate and interpretable AI-based diagnostic tool for early gastric cancer and to provide evidence supporting its potential application in clinical endoscopic practice.
- Conditions
- Early Gastric Cancer
- Being tested
- No intervention (observational study)
- Keywords
- Early Gastric Cancer, Artificial Intelligence, Multimodal Imaging, Endoscopic Imaging, Explainable AI, Deep Learning
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention (observational study)Other
This is an observational study with no intervention assigned to participants.
| Study group | Role | What they receive |
|---|---|---|
| Early Gastric Cancer | Participants with histopathologically confirmed early gastric cancer who underwent endoscopic examination, including white-light imaging and image-enhanced endoscopy. Receives: Other: No intervention (observational study) | |
| Non-Early Gastric Lesions | Participants with non-cancerous gastric lesions or non-early gastric cancer confirmed by histopathology who underwent endoscopic examination, including white-light imaging and image-enhanced endoscopy. Receives: Other: No intervention (observational study) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of the artificial intelligence model for detecting early gastric cancer
The primary outcome is the diagnostic performance of the artificial intelligence model for identifying early gastric cancer, evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and F1 score, using histopathological diagnosis as the reference standard.
Measured: Up to 14 days after endoscopy, when histopathological results are available
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Apr 2026
- Study start1 May 2026 (estimated)
- Primary completion1 Dec 2026 (estimated)
- Study completion1 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Early Gastric CancerStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital of Soochow UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07551466
- Sponsor's protocol ID
- 2022-098
- Acronym
- XAI-EGC
- Status last verified
- Apr 2026
- First submitted
- 14 Apr 2026
- Last updated
- 24 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Explainable Artificial Intelligence Model for Early Gastric Cancer Diagnosis Using Multimodal Endoscopic Imaging
- ClinicalTrials.gov ID
- NCT07551466
- Acronym / sponsor ID
- XAI-EGC / 2022-098
- Conditions
- Early Gastric Cancer
- Keywords
- Early Gastric Cancer, Artificial Intelligence, Multimodal Imaging, Endoscopic Imaging, Explainable AI, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Soochow UniversityUniversity, hospital or other
- Study start
- 1 May 2026 (estimated)
- Primary completion
- 1 Dec 2026 (estimated)
- Study completion
- 1 Feb 2027 (estimated)
- First posted
- 24 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging.
This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support.
The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.
Detailed description
This study is designed to develop and evaluate an explainable multimodal artificial intelligence (AI) model for the diagnosis of early gastric cancer (EGC) based on endoscopic imaging.
Patients who underwent endoscopic submucosal dissection (ESD) will be collected from one or more medical centers. Eligible patients will include those with histopathologically confirmed early gastric cancer or non-cancerous lesions who have undergone both white-light imaging (WLI) and image-enhanced endoscopy. For each lesion, representative endoscopic images will be selected according to predefined quality criteria.
The study will be conducted in several steps. First, a lesion detection model will be developed to identify regions of interest in endoscopic images. Second, quantitative image features, including color, texture, morphological, and mucosal structural features, will be extracted from the detected regions. Third, deep learning models will be used to derive predictive features from both WLI and image-enhanced endoscopic images. These features will be integrated with clinical variables to construct a multimodal prediction model.
The dataset will be divided into training, validation, and testing subsets. Model performance will be evaluated using standard diagnostic metrics, including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. Calibration performance and clinical utility may also be assessed using calibration curves and decision curve analysis.
To enhance model interpretability, feature contribution will be analyzed using Shapley additive explanations (SHAP), and visualization techniques such as gradient-weighted class activation mapping (Grad-CAM) will be applied to highlight regions of interest contributing to model predictions.
This study aims to develop an accurate and interpretable AI-based diagnostic tool for early gastric cancer and to provide evidence supporting its potential application in clinical endoscopic practice.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Early Gastric Cancer | – | Other: No intervention (observational study)Participants with histopathologically confirmed early gastric cancer who underwent endoscopic examination, including white-light imaging and image-enhanced endoscopy. |
| Non-Early Gastric Lesions | – | Other: No intervention (observational study)Participants with non-cancerous gastric lesions or non-early gastric cancer confirmed by histopathology who underwent endoscopic examination, including white-light imaging and image-enhanced endoscopy. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with suspected gastric lesions who underwent endoscopic examination and endoscopic submucosal dissection (ESD), with available white-light imaging (WLI), magnifying endoscopy with narrow-band imaging (ME-NBI), and histopathological diagnosis at participating centers.
Key inclusion criteria (4 listed)
- Age ≥18 years
- Suspicious gastric lesions identified on white-light imaging (WLI)
- Preoperative biopsy indicating precancerous lesions (dysplasia or intraepithelial neoplasia) or adenocarcinoma, with preoperative magnifying endoscopy with narrow-band imaging (ME-NBI) performed
- Patients meeting the absolute indications for endoscopic submucosal dissection (ESD) and who underwent ESD
Key exclusion criteria (4 listed)
- Non-adenocarcinoma histological types (e.g., lymphoma)
- Patients who did not undergo ME-NBI examination or did not receive ESD
- Lesions invading the muscularis propria or deeper layers
- Missing or indeterminate postoperative histopathological results
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention (observational study)This is an observational study with no intervention assigned to participants. | Other | Early Gastric Cancer, Non-Early Gastric Lesions |
Primary outcome
Diagnostic performance of the artificial intelligence model for detecting early gastric cancer
Time frame: Up to 14 days after endoscopy, when histopathological results are available
The primary outcome is the diagnostic performance of the artificial intelligence model for identifying early gastric cancer, evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and F1 score, using histopathological diagnosis as the reference standard.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Apr 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Early Gastric Cancer, run by The First Affiliated Hospital of Soochow University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 24 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Li he Liu (+8615943593759, lhliu2024@stu.suda.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of the artificial intelligence model for detecting early gastric cancer (time frame: Up to 14 days after endoscopy, when histopathological results are available). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Apr 2026.