Gastric Cancer (Diagnosis)TRACENCT07651644
Two-component Radiology-guided Autonomous Cascade Engine (TRACE)
A study testing Utilizing the TRACE model to assist radiologists in T-staging and washout period in people with Gastric Cancer (Diagnosis), run by Liaoning Cancer Hospital & Institute at 1 site in China.
Trial summary
This study employed a prospective, randomised crossover trial design to evaluate the clinical utility of the TRACE artificial intelligence system for gastric cancer T-staging. A total of 54 radiologists from tertiary and non-tertiary hospitals, including both senior and junior practitioners, were enrolled. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Gastric Cancer (Diagnosis)
- What is being tested?
- Utilizing the TRACE model to assist radiologists in T-staging and washout period
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 54 participants
- Where
- 1 siteChina
- Study dates
- 18 Jun 2026to 7 Aug 2026
- Phase
- Interventional
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Contrast-enhanced CT (CE-CT) images of gastric cancer patients from the Liaoning Cancer Hospital;
- Patients with a definitive postoperative pathological diagnosis of gastric cancer and a clear T-stage classification (T1-T4, including T4a and T4b);
- Imaging data must be complete and of sufficient quality to meet diagnostic and analytical requirements, with no significant artefacts or missing key data;
- Complete clinical and pathological information must be available to establish a diagnostic gold standard for comparison.
- Physician Inclusion Criteria (Image Readers)
- Radiologists holding a valid medical licence;
Show all 22Show fewer
- From the radiology department of a Grade A tertiary hospital or a non-Grade A tertiary hospital;
- Classified as senior or junior physicians based on clinical experience;
- Voluntarily participating in this study and completing both the non-AI-assisted and AI-assisted image interpretation tasks.
- Case Exclusion Criteria
- Severe missing imaging data or quality failing to meet analysis requirements (e.g., severe motion artefacts);
- Lack of clear postoperative pathological T-staging results;
- Cases not involving gastric cancer or with incomplete pathological information;
- Cases of duplicate enrolment or inconsistent data recording.
- Physician Exclusion Criteria
- Those unable to complete all image review tasks or demonstrating severe non-compliance;
- Those who withdraw during the study period and are unable to provide complete data for both phases of image review;
- Those who fail to complete the AI-assisted and non-AI-assisted interpretation processes as specified.
- Withdrawal Criteria
- Physicians who voluntarily withdraw from the study for personal reasons (e.g., time, health or work commitments);
- Physicians who fail to complete the required image review tasks or have data missing in excess of the specified threshold;
- Cases where critical data errors are identified during subsequent verification or where pathological results cannot be traced; Data found during the study to be non-compliant with ethical or quality control requirements must be excluded.
Who cannot join
None listed.
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
Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
Shenyang, Liaoning, 110024
Guoliang Zheng, doctor, 13322400728, zhengboren1@126.com
About this study
Official title: Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)
This study employed a prospective, randomised crossover trial design to evaluate the clinical utility of the TRACE artificial intelligence system for gastric cancer T-staging. A total of 54 radiologists from tertiary and non-tertiary hospitals, including both senior and junior practitioners, were enrolled. The study aimed to investigate whether AI-assisted diagnosis could improve the diagnostic accuracy of gastric cancer T-staging compared with independent interpretation by radiologists.
All participants were required to interpret 60 contrast-enhanced CT cases sequentially, completing two readings for each case: one without AI assistance and one with AI assistance; The order of the two readings was randomised, and a one-month washout period was observed between readings to eliminate memory bias. All cases were pathologically confirmed gastric cancer cases (stages T1-T4b), and the study simultaneously recorded the physicians' T-staging diagnostic results and the time taken per case. The 60 cases per radiologist were randomly selected from a pool of 1,000 histologically confirmed gastric cancer cases, stratified by pathological T stage T1-T4b. The reference standard was postoperative pathological T stage. The primary outcome was the change in T-staging accuracy between AI-assisted reading and standard (unaided) reading.The term "prospective" in this study refers to the prospective execution of radiologist enrollment, randomization, reading procedures, and data collection.
Read the detailed description
The TRACE trial is a prospective, randomized, crossover, controlled study evaluating an artificial intelligence (AI)-assisted decision system for T staging of gastric cancer based on CT images.
Background and rationale: Accurate preoperative T staging is critical for treatment planning in gastric cancer, but remains challenging due to reader variability and imaging limitations. The AI system was developed using deep learning with a large multi-center dataset to improve staging accuracy.
Study design: Eligible patients with pathologically confirmed gastric cancer will undergo preoperative contrast-enhanced CT. Each participant will be assessed twice in random order: once with AI assistance (AI arm) and once without (standard arm). A washout period will be applied between the two readings to minimize recall bias. Radiologists involved in the study are blinded to clinical and pathological reference standards.
Objective: To compare the T staging accuracy (primary outcome) between AI-assisted and standard reading, with secondary outcomes including inter-reader agreement, reading time, and diagnostic confidence.
Statistical methods: A crossover design will be used with a sample size calculated to detect a prespecified difference in overall accuracy. The primary analysis will employ a paired McNemar test or generalized estimating equation accounting for period and carryover effects. Subgroup analyses by tumor location, T category, and reader experience will be exploratory.
Data monitoring: No independent Data Monitoring Committee is required due to the low-risk nature of the diagnostic device. Adverse events related to the use of the software (e.g., workflow disruption) will be recorded and reported.
Ethics and dissemination: The protocol has been approved by the Ethics Committee of Liaoning Cancer Hospital \& Institute. Written informed consent (online or paper-based) will be obtained from all participants. Results will be submitted for publication in peer-reviewed journals regardless of outcome.
- Conditions
- Gastric Cancer (Diagnosis)
- Being tested
- Utilizing the TRACE model to assist radiologists in T-staging, washout period
- Keywords
- AI, Gastric Cancer, T Stage
- Medical subject headings
- Stomach Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Utilizing the TRACE model to assist radiologists in T-stagingDiagnostic test
AI-assisted reading: Radiologists interpret preoperative contrast-enhanced CT images for gastric cancer T staging with the support of the TRACE artificial intelligence decision system. The AI system provides a suggested T stage and relevant imaging features. The radiologist makes the final staging decision after reviewing the AI output. This intervention is used only during the AI-assisted reading session.
washout periodOther
Participants are required to observe a washout period of at least 30 days between consecutive interventions/assessments.
Utilizing the TRACE model to assist radiologists in T-stagingDiagnostic test
AI-assisted reading: Radiologists interpret preoperative contrast-enhanced CT images for gastric cancer T staging with the support of the TRACE artificial intelligence decision system. The AI system provides a suggested T stage and relevant imaging features. The radiologist makes the final staging decision after reviewing the AI output. This intervention is used only during the AI-assisted reading session.
| Study group | Role | What they receive |
|---|---|---|
| Standard reading 1 | Experimental | Utilizing the TRACE model to assist radiologists in T-staging. In this arm, participants receive TRACE model assistance in the first reading phase (AI-assisted), followed by independent reading without AI after a 1-month washout period. The temporal order of the intervention is early application. Receives: Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging; Other: washout period |
| Standard Reading 2 | Experimental | Utilizing the TRACE model to assist radiologists in T-staging. In this arm, participants first perform independent reading without AI assistance, and after a 1-month washout period, they receive TRACE model assistance in the second reading phase. The temporal order of the same intervention is delayed compared to Arm 1. Receives: Other: washout period; Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Accuracy of radiologists' interpretation of T staging
Measured: Within 40 days after the first radiologist initiates image reading.
Secondary outcomes (5)
Accuracy Change by Physician Experience Level
Changes in diagnostic accuracy of radiologists with different experience levels before and after AI assistance.
Measured: Within 40 days after the first radiologist initiates image reading.
Stratified diagnostic accuracy of different T-stages
Stratified diagnostic accuracy for different T-stages (T1-T4, including T4a and T4b).
Measured: Within 40 days after the first radiologist initiates image reading.
Agreement between physician diagnosis and pathological gold standard
Agreement between radiologists' diagnostic results and the pathological gold standard (e.g., Kappa value).
Measured: Within 40 days after the first radiologist initiates image reading.
Agreement between AI model and physician interpretation
Agreement analysis between AI model prediction results and radiologists' interpretations.
Measured: Within 40 days after the first radiologist initiates image reading.
Effect of AI assistance on reading efficiency
Changes in average reading time for diagnosis with and without AI assistance.
Measured: Within 40 days after the first radiologist initiates image reading.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2026
- Study start18 Jun 2026 (estimated)
- Primary completion25 Jul 2026 (estimated)
- Study completion7 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 Cancer (Diagnosis)Studies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 54 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Liaoning Cancer Hospital & Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Guoliang Zheng, Liaoning Cancer Hospital & Institute
- Investigators
- Guoliang Zheng, Principal investigator, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07651644
- Sponsor's protocol ID
- KY20260512
- Acronym
- TRACE
- Status last verified
- Jun 2026
- First submitted
- 11 Jun 2026
- Last updated
- 16 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to the restrictions imposed by the ethics committee and the institutional review board regarding the protection of patient privacy, individual participant data will not be shared.
Study at a glance
From the registry record, last updated by the sponsor on 16 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)
- ClinicalTrials.gov ID
- NCT07651644
- Acronym / sponsor ID
- TRACE / KY20260512
- Conditions
- Gastric Cancer (Diagnosis)
- Keywords
- AI, Gastric Cancer, T Stage
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 54 participants (estimated)
- Lead sponsor
- Liaoning Cancer Hospital & InstituteUniversity, hospital or other
- Study start
- 18 Jun 2026 (estimated)
- Primary completion
- 25 Jul 2026 (estimated)
- Study completion
- 7 Aug 2026 (estimated)
- First posted
- 16 Jun 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 employed a prospective, randomised crossover trial design to evaluate the clinical utility of the TRACE artificial intelligence system for gastric cancer T-staging. A total of 54 radiologists from tertiary and non-tertiary hospitals, including both senior and junior practitioners, were enrolled. The study aimed to investigate whether AI-assisted diagnosis could improve the diagnostic accuracy of gastric cancer T-staging compared with independent interpretation by radiologists.
All participants were required to interpret 60 contrast-enhanced CT cases sequentially, completing two readings for each case: one without AI assistance and one with AI assistance; The order of the two readings was randomised, and a one-month washout period was observed between readings to eliminate memory bias. All cases were pathologically confirmed gastric cancer cases (stages T1-T4b), and the study simultaneously recorded the physicians' T-staging diagnostic results and the time taken per case. The 60 cases per radiologist were randomly selected from a pool of 1,000 histologically confirmed gastric cancer cases, stratified by pathological T stage T1-T4b. The reference standard was postoperative pathological T stage. The primary outcome was the change in T-staging accuracy between AI-assisted reading and standard (unaided) reading.The term "prospective" in this study refers to the prospective execution of radiologist enrollment, randomization, reading procedures, and data collection.
Detailed description
The TRACE trial is a prospective, randomized, crossover, controlled study evaluating an artificial intelligence (AI)-assisted decision system for T staging of gastric cancer based on CT images.
Background and rationale: Accurate preoperative T staging is critical for treatment planning in gastric cancer, but remains challenging due to reader variability and imaging limitations. The AI system was developed using deep learning with a large multi-center dataset to improve staging accuracy.
Study design: Eligible patients with pathologically confirmed gastric cancer will undergo preoperative contrast-enhanced CT. Each participant will be assessed twice in random order: once with AI assistance (AI arm) and once without (standard arm). A washout period will be applied between the two readings to minimize recall bias. Radiologists involved in the study are blinded to clinical and pathological reference standards.
Objective: To compare the T staging accuracy (primary outcome) between AI-assisted and standard reading, with secondary outcomes including inter-reader agreement, reading time, and diagnostic confidence.
Statistical methods: A crossover design will be used with a sample size calculated to detect a prespecified difference in overall accuracy. The primary analysis will employ a paired McNemar test or generalized estimating equation accounting for period and carryover effects. Subgroup analyses by tumor location, T category, and reader experience will be exploratory.
Data monitoring: No independent Data Monitoring Committee is required due to the low-risk nature of the diagnostic device. Adverse events related to the use of the software (e.g., workflow disruption) will be recorded and reported.
Ethics and dissemination: The protocol has been approved by the Ethics Committee of Liaoning Cancer Hospital \& Institute. Written informed consent (online or paper-based) will be obtained from all participants. Results will be submitted for publication in peer-reviewed journals regardless of outcome.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Standard reading 1 | Experimental | Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging, Other: washout periodUtilizing the TRACE model to assist radiologists in T-staging. In this arm, participants receive TRACE model assistance in the first reading phase (AI-assisted), followed by independent reading without AI after a 1-month washout period. The temporal order of the intervention is early application. |
| Standard Reading 2 | Experimental | Other: washout period, Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-stagingUtilizing the TRACE model to assist radiologists in T-staging. In this arm, participants first perform independent reading without AI assistance, and after a 1-month washout period, they receive TRACE model assistance in the second reading phase. The temporal order of the same intervention is delayed compared to Arm 1. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (22 listed)
- Contrast-enhanced CT (CE-CT) images of gastric cancer patients from the Liaoning Cancer Hospital;
- Patients with a definitive postoperative pathological diagnosis of gastric cancer and a clear T-stage classification (T1-T4, including T4a and T4b);
- Imaging data must be complete and of sufficient quality to meet diagnostic and analytical requirements, with no significant artefacts or missing key data;
- Complete clinical and pathological information must be available to establish a diagnostic gold standard for comparison.
- Physician Inclusion Criteria (Image Readers)
Key exclusion criteria (0 listed)
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 |
|---|---|---|
| Utilizing the TRACE model to assist radiologists in T-stagingAI-assisted reading: Radiologists interpret preoperative contrast-enhanced CT images for gastric cancer T staging with the support of the TRACE artificial intelligence decision system. The AI system provides a suggested T stage and relevant imaging features. The radiologist makes the final staging decision after reviewing the AI output. This intervention is used only during the AI-assisted reading session. | Diagnostic test | Standard reading 1 |
| washout periodParticipants are required to observe a washout period of at least 30 days between consecutive interventions/assessments. | Other | Standard Reading 2, Standard reading 1 |
| Utilizing the TRACE model to assist radiologists in T-stagingAI-assisted reading: Radiologists interpret preoperative contrast-enhanced CT images for gastric cancer T staging with the support of the TRACE artificial intelligence decision system. The AI system provides a suggested T stage and relevant imaging features. The radiologist makes the final staging decision after reviewing the AI output. This intervention is used only during the AI-assisted reading session. | Diagnostic test | Standard Reading 2 |
Primary outcome
Accuracy
Time frame: Within 40 days after the first radiologist initiates image reading.
Accuracy of radiologists' interpretation of T staging
5 secondary outcomes
Accuracy Change by Physician Experience Level
Time frame: Within 40 days after the first radiologist initiates image reading.
Stratified diagnostic accuracy of different T-stages
Time frame: Within 40 days after the first radiologist initiates image reading.
Agreement between physician diagnosis and pathological gold standard
Time frame: Within 40 days after the first radiologist initiates image reading.
Agreement between AI model and physician interpretation
Time frame: Within 40 days after the first radiologist initiates image reading.
Effect of AI assistance on reading efficiency
Time frame: Within 40 days after the first radiologist initiates image reading.
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 16 Jun 2026.
- Individual participant dataSharing planned: No. Due to the restrictions imposed by the ethics committee and the institutional review board regarding the protection of patient privacy, individual participant data will not be shared.
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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.
Guoliang Zheng principal investigator
Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Utilizing the TRACE model to assist radiologists in T-staging and washout period in people with Gastric Cancer (Diagnosis), run by Liaoning Cancer Hospital & Institute 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 22 inclusion and 0 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 16 Jun 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 Guoliang Zheng (13322400728, zhengboren1@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: Within 40 days after the first radiologist initiates image reading.). 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 16 Jun 2026; AzTrials last refreshed its copy on 26 Oct 2026.