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

NCT07651644 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 54

Last updated 2026-06-16

No results posted yet for this study

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)

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

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.

Sponsors & Collaborators

  • Liaoning Cancer Hospital & Institute

    lead OTHER

Principal Investigators

  • Guoliang Zheng · Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Model
CROSSOVER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-18
Primary Completion
2026-07-25
Completion
2026-08-07

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07651644 on ClinicalTrials.gov