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

Overview

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.