Two-component Radiology-guided Autonomous Cascade Engine (TRACE) (TRACE)
Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)
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.
연구 개요
상태
정황
상세 설명
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.
연구 유형
등록 (추정된)
단계
- 해당 없음
연락처 및 위치
연구 연락처
- 이름: Guoliang Zheng
- 전화번호: 13322400728
- 이메일: zhengboren1@126.com
연구 장소
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Liaoning
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Shenyang, Liaoning, 중국, 110024
- 모병
- Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
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연락하다:
- Guoliang Zheng, doctor
- 전화번호: 13322400728
- 이메일: zhengboren1@126.com
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참여기준
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
설명
Inclusion Criteria (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.
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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 무작위
- 중재 모델: 크로스오버 할당
- 마스킹: 더블
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
|---|---|
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실험적: 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.
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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.
Participants are required to observe a washout period of at least 30 days between consecutive interventions/assessments.
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실험적: 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.
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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.
Participants are required to observe a washout period of at least 30 days between consecutive interventions/assessments.
|
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Accuracy
기간: Within 40 days after the first radiologist initiates image reading.
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Accuracy of radiologists' interpretation of T staging
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Within 40 days after the first radiologist initiates image reading.
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Accuracy Change by Physician Experience Level
기간: Within 40 days after the first radiologist initiates image reading.
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Changes in diagnostic accuracy of radiologists with different experience levels before and after AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Stratified diagnostic accuracy of different T-stages
기간: Within 40 days after the first radiologist initiates image reading.
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Stratified diagnostic accuracy for different T-stages (T1-T4, including T4a and T4b).
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Within 40 days after the first radiologist initiates image reading.
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Agreement between physician diagnosis and pathological gold standard
기간: Within 40 days after the first radiologist initiates image reading.
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Agreement between radiologists' diagnostic results and the pathological gold standard (e.g., Kappa value).
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Within 40 days after the first radiologist initiates image reading.
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Agreement between AI model and physician interpretation
기간: Within 40 days after the first radiologist initiates image reading.
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Agreement analysis between AI model prediction results and radiologists' interpretations.
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Within 40 days after the first radiologist initiates image reading.
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Effect of AI assistance on reading efficiency
기간: Within 40 days after the first radiologist initiates image reading.
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Changes in average reading time for diagnosis with and without AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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기타 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Influence of case characteristics on AI assistance effect
기간: Within 40 days after the first radiologist initiates image reading.
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Influence of different case characteristics (e.g., tumor location, size) on the performance of AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Impact of individual physician differences on AI assistance effect
기간: Within 40 days after the first radiologist initiates image reading.
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Impact of individual differences among physicians on the performance of AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Value of AI assistance in reducing diagnostic discrepancy
기간: Within 40 days after the first radiologist initiates image reading.
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Potential value of AI assistance in reducing diagnostic differences and improving reading agreement.
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Within 40 days after the first radiologist initiates image reading.
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Impact of model probability information on physician decisions
기간: Within 40 days after the first radiologist initiates image reading.
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Preliminary analysis of the impact of probability output from AI model on physician decision-making behavior.
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Within 40 days after the first radiologist initiates image reading.
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공동 작업자 및 조사자
수사관
- 수석 연구원: Guoliang Zheng, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
연구 기록 날짜
연구 주요 날짜
연구 시작 (추정된)
기본 완료 (추정된)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- KY20260512
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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