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.

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.

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.

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.