Gastric CancerLAGCNCT07683195
Research on Early Recurrence of Locally Advanced Gastric Cancer Based on CT Radiomics Prediction
An observational study of Gastric Cancer, run by Liu Yang at 1 site in China.
Trial summary
This study aims to develop a model for predicting postoperative recurrence in patients with LAGC using artificial intelligence (AI) technology based on preoperative computed tomography (CT) images Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Gastric Cancer
- How many people
- 900 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 31 Aug 2026
- 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: All patients with locally advanced gastric cancer
Who may be able to join
- pathology diagnosis of LAGC (pT2NxM0-pT4NxM0);
- radical gastrectomy with D2 lymph node dissection (>15 lymph nodes);
- available clinicopathological data;
- patients underwent contrast-enhanced abdominal CT scans within 4 weeks before surgery.
Who cannot join
- preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy);
- previous malignancies;
- unsatisfactory gastric distention or inability to identify the primary tumor;
- image artifacts.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
This study aims to develop a model for predicting postoperative recurrence in patients with LAGC using artificial intelligence (AI) technology based on preoperative computed tomography (CT) images
- Conditions
- Gastric Cancer
- Keywords
- Locally Advanced Gastric Cancer, Early Recurrence, CT, Radiomics
- Medical subject headings
- Stomach Neoplasms, Recurrence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| No recurrence | Patients with locally advanced gastric cancer who have experienced no recurrence within 1 year after radical gastrectomy | |
| Recurrence | Patients with locally advanced gastric cancer who experienced recurrence within 1 year after radical gastrectomy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of early recurrence models
In this study, clinical data and contrast-enhanced CT imaging data of 550 patients with locally advanced gastric cancer from our hospital were collected. Machine learning and deep learning algorithms were applied to assess the early recurrence of patients within one year after surgery. The performance of the artificial intelligence model was evaluated from two dimensions: diagnostic accuracy and stability, and quantitative analysis of its performance was conducted using indicators including the area under the curve (AUC) and the precision-recall curve (PR curve).
Measured: Immediately evaluated after the early recurrence model was built
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study start1 Jan 2020
- Primary completion31 Dec 2024
- Study completion31 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric CancerStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Liu Yang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Liu Yang, Qianfoshan Hospital
- Investigators
- Guangyong Zhang, Principal investigator, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07683195
- Sponsor's protocol ID
- YXYXLL-KY-2025[168]
- Acronym
- LAGC
- Status last verified
- Jul 2026
- First submitted
- 26 Jun 2026
- Last updated
- 7 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets used and analyzed in this study are not publicly available due to patient privacy requirements and ethical restrictions
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Early Recurrence of Locally Advanced Gastric Cancer Based on CT Radiomics Prediction
- ClinicalTrials.gov ID
- NCT07683195
- Acronym / sponsor ID
- LAGC / YXYXLL-KY-2025[168]
- Conditions
- Gastric Cancer
- Keywords
- Locally Advanced Gastric Cancer, Early Recurrence, CT, Radiomics
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Liu YangUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Aug 2026 (estimated)
- First posted
- 6 Jul 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.
This study aims to develop a model for predicting postoperative recurrence in patients with LAGC using artificial intelligence (AI) technology based on preoperative computed tomography (CT) images
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| No recurrence | – | –Patients with locally advanced gastric cancer who have experienced no recurrence within 1 year after radical gastrectomy |
| Recurrence | – | –Patients with locally advanced gastric cancer who experienced recurrence within 1 year after radical gastrectomy |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All patients with locally advanced gastric cancer
Key inclusion criteria (4 listed)
- pathology diagnosis of LAGC (pT2NxM0-pT4NxM0);
- radical gastrectomy with D2 lymph node dissection (>15 lymph nodes);
- available clinicopathological data;
- patients underwent contrast-enhanced abdominal CT scans within 4 weeks before surgery.
Key exclusion criteria (4 listed)
- preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy);
- previous malignancies;
- unsatisfactory gastric distention or inability to identify the primary tumor;
- image artifacts.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of early recurrence models
Time frame: Immediately evaluated after the early recurrence model was built
In this study, clinical data and contrast-enhanced CT imaging data of 550 patients with locally advanced gastric cancer from our hospital were collected. Machine learning and deep learning algorithms were applied to assess the early recurrence of patients within one year after surgery. The performance of the artificial intelligence model was evaluated from two dimensions: diagnostic accuracy and stability, and quantitative analysis of its performance was conducted using indicators including the area under the curve (AUC) and the precision-recall curve (PR curve).
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 7 Jul 2026.
- Individual participant dataSharing planned: No. The datasets used and analyzed in this study are not publicly available due to patient privacy requirements and ethical restrictions
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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.
Guangyong Zhang principal investigator
Qianfoshan Hospital
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, run by Liu Yang 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: enrolling by invitation, as of the sponsor's last update on 7 Jul 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of early recurrence models (time frame: Immediately evaluated after the early recurrence model was built). 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 7 Jul 2026.