Clinical trial · Interventional
Two-component Radiology-guided Autonomous Cascade Engine (TRACE)
Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Gastric Cancer (Diagnosis) | Malignant Gastric Neoplasm | CURATED_BROADER | 0.80 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Utilizing the TRACE model to assist radiologists in T-staging | Diagnostic Test | — | UNRESOLVED |
| washout period | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- Standard reading 1
- description
- 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.
- interventionNames
- Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging
- Other: washout period
- type
- EXPERIMENTAL
- label
- Standard Reading 2
- description
- 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.
- interventionNames
- Other: washout period
- Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available