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RECRUITING

NCT07651644

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

Sponsor: Liaoning Cancer Hospital & Institute

View on ClinicalTrials.gov

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.

Official title: Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)

Key Details

Gender

All

Age Range

Any - Any

Study Type

INTERVENTIONAL

Enrollment

Start Date

2026-06-18

Completion Date

2026-08-07

Last Updated

2026-06-16

Healthy Volunteers

Conditions

Gastric Cancer (Diagnosis)

Interventions

DIAGNOSTIC_TEST

Utilizing the TRACE model to assist radiologists in T-staging

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.

OTHER

washout period

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

DIAGNOSTIC_TEST

Utilizing the TRACE model to assist radiologists in T-staging

Inclusion Criteria (Imaging Data) 1. Contrast-enhanced CT (CE-CT) images of gastric cancer patients from the Liaoning Cancer Hospital; 2. Patients with a definitive postoperative pathological diagnosis of gastric cancer and a clear T-stage classification (T1-T4, including T4a and T4b); 3. Imaging data must be complete and of sufficient quality to meet diagnostic and analytical requirements, with no significant artefacts or missing key data; 4. Complete clinical and pathological information must be available to establish a diagnostic gold standard for comparison. Physician Inclusion Criteria (Image Readers) 1. Radiologists holding a valid medical licence; 2. From the radiology department of a Grade A tertiary hospital or a non-Grade A tertiary hospital; 3. Classified as senior or junior physicians based on clinical experience; 4. Voluntarily participating in this study and completing both the non-AI-assisted and AI-assisted image interpretation tasks. Case Exclusion Criteria 1. Severe missing imaging data or quality failing to meet analysis requirements (e.g., severe motion artefacts); 2. Lack of clear postoperative pathological T-staging results; 3. Cases not involving gastric cancer or with incomplete pathological information; 4. Cases of duplicate enrolment or inconsistent data recording. Physician Exclusion Criteria 1. Those unable to complete all image review tasks or demonstrating severe non-compliance; 2. Those who withdraw during the study period and are unable to provide complete data for both phases of image review; 3. Those who fail to complete the AI-assisted and non-AI-assisted interpretation processes as specified. Withdrawal Criteria 1. Physicians who voluntarily withdraw from the study for personal reasons (e.g., time, health or work commitments); 2. Physicians who fail to complete the required image review tasks or have data missing in excess of the specified threshold; 3. 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.

Locations (1)

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

Shenyang, Liaoning, China