Gastric Cancer StageNCT07250347
AI-Assisted Detection and Staging of Gastric Cancer Using Contrast-Enhanced CT
An observational study of Gastric Cancer Stage and Gastric Cancer Patients Undergoing Gastrectomy, run by The First Affiliated Hospital with Nanjing Medical University at 1 site in China.
Trial summary
Accurate preoperative assessment of gastric cancer stage guides eligibility for endoscopic resection, extent of gastrectomy and lymphadenectomy, selection for neoadjuvant therapy, and use of staging laparoscopy. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Gastric Cancer Stage, Gastric Cancer Patients Undergoing Gastrectomy
- What is being tested?
- CT scan
- How many people
- 8,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2025to 30 Dec 2028
- 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 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study will enroll adult patients (≥18 years old) with a confirmed diagnosis of gastric cancer (adenocarcinoma) who have undergone contrast-enhanced CT (CECT) as part of their standard preoperative evaluation. Participants will be selected from both internal and external cohorts, with inclusion from multiple centers to assess cross-site reproducibility and generalizability of the AI model.
Who may be able to join
- pathologically confirmed gastric cancer;
- preoperative contrast-enhanced CT performed;
- no evidence of distant metastasis on baseline staging;
- curative-intent management with complete postoperative histopathology.
Who cannot join
- prior treatment before surgery;
- non-diagnostic or poor-quality CT precluding evaluation.
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 Nanjing Medical University
Nanjing, Jiangsu
Yue Wang, 025-68306222, jsphkjwy@163.com
About this study
Official title: Langue and Imaging-integrated Foundation Model for Gastric Cancer Detection and Staging Via Contrast-Enhanced CT: a Multicenter Study
Accurate preoperative assessment of gastric cancer stage guides eligibility for endoscopic resection, extent of gastrectomy and lymphadenectomy, selection for neoadjuvant therapy, and use of staging laparoscopy. Contrast-enhanced CT (CECT) is guideline-endorsed for initial staging, yet performance varies across institutions and readers. This study will evaluate an artificial-intelligence (AI) system that analyzes routine CECT to detect gastric cancer and assign four-class T stage (T1-T4) and N stage (N0-N3) .
Read the detailed description
Adults with confirmed gastric cancer undergoing pre-treatment CECT will be enrolled. The AI analysis will be applied to clinically acquired images. Radiologist interpretations with and without AI support will be collected in a prespecified reader study. The reference standard will include surgical pathology, supplemented by clinical follow-up when applicable. The primary outcome is detection performance, diagnostic performance of the AI for four-class staging (e.g., accuracy and area under the receiver operating characteristic curve). Secondary outcomes include the effect of AI assistance on reader accuracy and interpretation time, inter-reader agreement, and cross-site reproducibility.
- Being tested
- CT scan
- Keywords
- Gastric cancer, stage, artificial-intelligence, detection, contrast-enhanced CT
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT scanDiagnostic test
preoperative contrast-enhanced CT
| Study group | Role | What they receive |
|---|---|---|
| Cohort 1 (Internal Derivation Cohort) | Retrospective case-only cohort of adults with pathologically confirmed gastric cancer who underwent preoperative contrast-enhanced CT at the sponsoring institution. Existing CT images and clinical/pathology records will be used to train and test the AI model and to estimate diagnostic performance for T and N staging. Receives: Diagnostic Test: CT scan | |
| Cohort 2 (External Validation Cohort A) | Independent retrospective case-only cohort from an external hospital with the same inclusion/exclusion criteria. Used solely for external validation to assess reproducibility across sites and scanners. Receives: Diagnostic Test: CT scan | |
| Cohort 3 (External Validation Cohort B) | A second independent retrospective validation cohort from another institution to further test generalizability. Receives: Diagnostic Test: CT scan |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of the AI model for staging
The primary outcome is the diagnostic accuracy of the AI system for four-class T staging (T1-T4) and N staging (N0-3) based on contrast-enhanced CT. The AI performance will be assessed using accuracy, area under the receiver operating characteristic curve (AUC), and micro-AUC for internal and external cohorts.
Measured: 3 years
Secondary outcomes (2)
Reader Accuracy with AI Support
This outcome measures the accuracy of radiologists in classifying gastric cancer stagewhen aided by the AI system compared to manual classification without AI assistance. Accuracy will be compared between different radiologist experience levels.
Measured: 3 years
Survival time
Calculate the survival time of gastric cancer patients from the point of diagnosis and treatment initiation.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Nov 2025
- Study start1 Aug 2025
- Primary completion30 Dec 2028 (estimated)
- Study completion30 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric Cancer StageStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital with Nanjing Medical 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 8,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Jiangsu Cancer Institute & Hospital, Zhengzhou University, Peking University First Hospital
- Responsible party
- Sponsor
- Investigators
- Zhang Yudong, Principal investigator, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07250347
- Sponsor's protocol ID
- 2025-SR-842
- Status last verified
- Sep 2025
- First submitted
- 29 Sep 2025
- Last updated
- 26 Nov 2025
- Results posted
- No
- Data monitoring committee
- Yes
- 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 26 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Langue and Imaging-integrated Foundation Model for Gastric Cancer Detection and Staging Via Contrast-Enhanced CT: a Multicenter Study
- ClinicalTrials.gov ID
- NCT07250347
- Acronym / sponsor ID
- 2025-SR-842
- Keywords
- Gastric cancer, stage, artificial-intelligence, detection, contrast-enhanced CT
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 8,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital with Nanjing Medical UniversityUniversity, hospital or other
- Collaborators
- Jiangsu Cancer Institute & Hospital, Zhengzhou University, Peking University First Hospital
- Study start
- 1 Aug 2025 (actual)
- Primary completion
- 30 Dec 2028 (estimated)
- Study completion
- 30 Dec 2028 (estimated)
- First posted
- 26 Nov 2025 (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.
Accurate preoperative assessment of gastric cancer stage guides eligibility for endoscopic resection, extent of gastrectomy and lymphadenectomy, selection for neoadjuvant therapy, and use of staging laparoscopy. Contrast-enhanced CT (CECT) is guideline-endorsed for initial staging, yet performance varies across institutions and readers. This study will evaluate an artificial-intelligence (AI) system that analyzes routine CECT to detect gastric cancer and assign four-class T stage (T1-T4) and N stage (N0-N3) .
Detailed description
Adults with confirmed gastric cancer undergoing pre-treatment CECT will be enrolled. The AI analysis will be applied to clinically acquired images. Radiologist interpretations with and without AI support will be collected in a prespecified reader study. The reference standard will include surgical pathology, supplemented by clinical follow-up when applicable. The primary outcome is detection performance, diagnostic performance of the AI for four-class staging (e.g., accuracy and area under the receiver operating characteristic curve). Secondary outcomes include the effect of AI assistance on reader accuracy and interpretation time, inter-reader agreement, and cross-site reproducibility.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Cohort 1 (Internal Derivation Cohort) | – | Diagnostic Test: CT scanRetrospective case-only cohort of adults with pathologically confirmed gastric cancer who underwent preoperative contrast-enhanced CT at the sponsoring institution. Existing CT images and clinical/pathology records will be used to train and test the AI model and to estimate diagnostic performance for T and N staging. |
| Cohort 2 (External Validation Cohort A) | – | Diagnostic Test: CT scanIndependent retrospective case-only cohort from an external hospital with the same inclusion/exclusion criteria. Used solely for external validation to assess reproducibility across sites and scanners. |
| Cohort 3 (External Validation Cohort B) | – | Diagnostic Test: CT scanA second independent retrospective validation cohort from another institution to further test generalizability. |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study will enroll adult patients (≥18 years old) with a confirmed diagnosis of gastric cancer (adenocarcinoma) who have undergone contrast-enhanced CT (CECT) as part of their standard preoperative evaluation. Participants will be selected from both internal and external cohorts, with inclusion from multiple centers to assess cross-site reproducibility and generalizability of the AI model.
Key inclusion criteria (4 listed)
- pathologically confirmed gastric cancer;
- preoperative contrast-enhanced CT performed;
- no evidence of distant metastasis on baseline staging;
- curative-intent management with complete postoperative histopathology.
Key exclusion criteria (2 listed)
- prior treatment before surgery;
- non-diagnostic or poor-quality CT precluding evaluation.
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 |
|---|---|---|
| CT scanpreoperative contrast-enhanced CT | Diagnostic test | Cohort 1 (Internal Derivation Cohort), Cohort 2 (External Validation Cohort A), Cohort 3 (External Validation Cohort B) |
Primary outcome
Diagnostic performance of the AI model for staging
Time frame: 3 years
The primary outcome is the diagnostic accuracy of the AI system for four-class T staging (T1-T4) and N staging (N0-3) based on contrast-enhanced CT. The AI performance will be assessed using accuracy, area under the receiver operating characteristic curve (AUC), and micro-AUC for internal and external cohorts.
2 secondary outcomes
Reader Accuracy with AI Support
Time frame: 3 years
Survival time
Time frame: 3 years
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 26 Nov 2025.
- 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.
Zhang Yudong principal investigator
The First Affiliated Hospital with Nanjing Medical University
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 Gastric Cancer Stage and Gastric Cancer Patients Undergoing Gastrectomy, run by The First Affiliated Hospital with Nanjing Medical 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 2 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 26 Nov 2025. 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 Zhang Yudong, PHD, MD (+8618251966069, zhangyd3895@njmu.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 AI model for staging (time frame: 3 years). 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 26 Nov 2025.