NCT07651644Toboroz
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)
Forrás: ClinicalTrials.gov · Utoljára frissítve: 2026. jún. 16.
Rövid összefoglaló (regiszter, angolul)
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.
- Állapot (regiszter)
- Toboroz
- Vizsgálattípus
- Beavatkozással járó
- Résztvevők száma
- 54 (tervezett)
- Első benyújtás
- 2026. jún. 11.
- Kezdés
- 2026. jún. 18.
- Elsődleges befejezés
- 2026. júl. 25.
- Fő szponzor
- Liaoning Cancer Hospital & Institute
- Betegségek
- Gyomor- és nyelőcsőrák, Gyermekkori daganatok
- NCT-szám
- NCT07651644
Mit vállalsz
- Vizsgálat típusa: Beavatkozással járó
- Tervezett résztvevők: 54
- 2026. jún. 18. és az elsődleges befejezés (2026. júl. 25.) között
- 1 helyszín, 1 országban
- Vizitek és vizsgálatok: kérdezd a vizsgálat csapatát, a regiszter nem sorolja fel őket
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Kiket keres a vizsgálat
- 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;
- 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.
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Helyszínek (1)
Helyszínek a regiszter sorrendjében. Ha a profilodba mented a városodat, távolság szerint rendeződnek.
| Helyszín | Város | Ország | Helyszín állapota |
|---|---|---|---|
| Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute) Ennek a helyszínnek vagy a koordinátora? Erősítsd meg az állapotát | Shenyang | Kína | Toboroz |
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