Two-component Radiology-guided Autonomous Cascade Engine (TRACE)

NCT07651644 | Recruiting

• 1 other identifier: KY20260512
• Study Type: interventional
• Target: 54
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Gastric Cancer (Diagnosis)

Keywords

AI; Gastric Cancer; T Stage

Outcome Measures

Primary Outcomes (1)

• Accuracy

  Accuracy of radiologists' interpretation of T staging

  Within 40 days after the first radiologist initiates image reading.

Secondary Outcomes (5)

• Accuracy Change by Physician Experience Level

  Within 40 days after the first radiologist initiates image reading.

• Stratified diagnostic accuracy of different T-stages

  Within 40 days after the first radiologist initiates image reading.

• Agreement between physician diagnosis and pathological gold standard

  Within 40 days after the first radiologist initiates image reading.

• Agreement between AI model and physician interpretation

  Within 40 days after the first radiologist initiates image reading.

• Effect of AI assistance on reading efficiency

  Within 40 days after the first radiologist initiates image reading.

Other Outcomes (4)

• Influence of case characteristics on AI assistance effect

  Within 40 days after the first radiologist initiates image reading.

• Impact of individual physician differences on AI assistance effect

  Within 40 days after the first radiologist initiates image reading.

• Value of AI assistance in reducing diagnostic discrepancy

  Within 40 days after the first radiologist initiates image reading.

Study Arms (2)

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.

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.

Other: washout period; Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging

Interventions

washout period OTHER

Participants are required to observe a washout period of at least 30 days between consecutive interventions/assessments.

Standard Reading 2; Standard reading 1

Utilizing the TRACE model to assist radiologists in T-staging DIAGNOSTIC_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.

Standard reading 1

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.
• Radiologists holding a valid medical licence;
• 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.

You may not qualify if:

• 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.
• 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.

Sponsors & Collaborators

• Liaoning Cancer Hospital & Institute: lead

Study Sites (1)

Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)

Shenyang, Liaoning, 110024, China

RECRUITING

MeSH Terms

Conditions

Stomach Neoplasms; Disease

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Guoliang Zheng

  Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Guoliang Zheng

CONTACT

13322400728; zhengboren1@126.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: doctor

Study Record Dates

• First Submitted: June 11, 2026
• First Posted: June 16, 2026
• Study Start: June 18, 2026
• Primary Completion: July 25, 2026
• Study Completion: August 7, 2026
• Last Updated: June 16, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)