Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 54
- 试验地点
- 1
- 主要终点
- Accuracy
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Diagnostic
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •(Imaging Data)
- •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.
排除标准
- •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.
研究组 & 干预措施
Standard reading 1
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.
干预措施: Utilizing the TRACE model to assist radiologists in T-staging (Diagnostic Test)
Standard reading 1
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.
干预措施: washout period (Other)
Standard Reading 2
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.
干预措施: washout period (Other)
Standard Reading 2
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.
干预措施: Utilizing the TRACE model to assist radiologists in T-staging (Diagnostic Test)
结局指标
主要结局
Accuracy
时间窗: Within 40 days after the first radiologist initiates image reading.
Accuracy of radiologists' interpretation of T staging
次要结局
- Accuracy Change by Physician Experience Level(Within 40 days after the first radiologist initiates image reading.)
- Stratified diagnostic accuracy of different T-stages(Within 40 days after the first radiologist initiates image reading.)
- Agreement between physician diagnosis and pathological gold standard(Within 40 days after the first radiologist initiates image reading.)
- Agreement between AI model and physician interpretation(Within 40 days after the first radiologist initiates image reading.)
- Effect of AI assistance on reading efficiency(Within 40 days after the first radiologist initiates image reading.)
研究者
Guoliang Zheng
doctor
Liaoning Cancer Hospital & Institute