Esta página foi traduzida automaticamente e a precisão da tradução não é garantida. Por favor, consulte o versão em inglês para um texto fonte.

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

11 de junho de 2026 atualizado por: Guoliang Zheng, Liaoning Cancer Hospital & Institute

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.

Visão geral do estudo

Status

Recrutamento

Condições

Intervenção / Tratamento

Descrição detalhada

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.

Tipo de estudo

Intervencional

Inscrição (Estimado)

54

Estágio

  • Não aplicável

Contactos e Locais

Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.

Contato de estudo

Locais de estudo

    • Liaoning
      • Shenyang, Liaoning, China, 110024
        • Recrutamento
        • Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
        • Contato:

Critérios de participação

Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.

Critérios de elegibilidade

Idades elegíveis para estudo

  • Filho
  • Adulto
  • Adulto mais velho

Aceita Voluntários Saudáveis

Não

Descrição

Inclusion Criteria (Imaging Data)

  1. Contrast-enhanced CT (CE-CT) images of gastric cancer patients from the Liaoning Cancer Hospital;
  2. Patients with a definitive postoperative pathological diagnosis of gastric cancer and a clear T-stage classification (T1-T4, including T4a and T4b);
  3. Imaging data must be complete and of sufficient quality to meet diagnostic and analytical requirements, with no significant artefacts or missing key data;
  4. Complete clinical and pathological information must be available to establish a diagnostic gold standard for comparison.

Physician Inclusion Criteria (Image Readers)

  1. Radiologists holding a valid medical licence;
  2. From the radiology department of a Grade A tertiary hospital or a non-Grade A tertiary hospital;
  3. Classified as senior or junior physicians based on clinical experience;
  4. Voluntarily participating in this study and completing both the non-AI-assisted and AI-assisted image interpretation tasks.

Case Exclusion Criteria

  1. Severe missing imaging data or quality failing to meet analysis requirements (e.g., severe motion artefacts);
  2. Lack of clear postoperative pathological T-staging results;
  3. Cases not involving gastric cancer or with incomplete pathological information;
  4. Cases of duplicate enrolment or inconsistent data recording.

Physician Exclusion Criteria

  1. Those unable to complete all image review tasks or demonstrating severe non-compliance;
  2. Those who withdraw during the study period and are unable to provide complete data for both phases of image review;
  3. Those who fail to complete the AI-assisted and non-AI-assisted interpretation processes as specified.

Withdrawal Criteria

  1. Physicians who voluntarily withdraw from the study for personal reasons (e.g., time, health or work commitments);
  2. Physicians who fail to complete the required image review tasks or have data missing in excess of the specified threshold;
  3. 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.

Plano de estudo

Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.

Como o estudo é projetado?

Detalhes do projeto

  • Finalidade Principal: Diagnóstico
  • Alocação: Randomizado
  • Modelo Intervencional: Atribuição cruzada
  • Mascaramento: Dobro

Armas e Intervenções

Grupo de Participantes / Braço
Intervenção / Tratamento
Experimental: 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.
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.
Experimental: 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.
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.

O que o estudo está medindo?

Medidas de resultados primários

Medida de resultado
Descrição da medida
Prazo
Accuracy
Prazo: Within 40 days after the first radiologist initiates image reading.
Accuracy of radiologists' interpretation of T staging
Within 40 days after the first radiologist initiates image reading.

Medidas de resultados secundários

Medida de resultado
Descrição da medida
Prazo
Accuracy Change by Physician Experience Level
Prazo: Within 40 days after the first radiologist initiates image reading.
Changes in diagnostic accuracy of radiologists with different experience levels before and after AI assistance.
Within 40 days after the first radiologist initiates image reading.
Stratified diagnostic accuracy of different T-stages
Prazo: Within 40 days after the first radiologist initiates image reading.
Stratified diagnostic accuracy for different T-stages (T1-T4, including T4a and T4b).
Within 40 days after the first radiologist initiates image reading.
Agreement between physician diagnosis and pathological gold standard
Prazo: Within 40 days after the first radiologist initiates image reading.
Agreement between radiologists' diagnostic results and the pathological gold standard (e.g., Kappa value).
Within 40 days after the first radiologist initiates image reading.
Agreement between AI model and physician interpretation
Prazo: Within 40 days after the first radiologist initiates image reading.
Agreement analysis between AI model prediction results and radiologists' interpretations.
Within 40 days after the first radiologist initiates image reading.
Effect of AI assistance on reading efficiency
Prazo: Within 40 days after the first radiologist initiates image reading.
Changes in average reading time for diagnosis with and without AI assistance.
Within 40 days after the first radiologist initiates image reading.

Outras medidas de resultado

Medida de resultado
Descrição da medida
Prazo
Influence of case characteristics on AI assistance effect
Prazo: Within 40 days after the first radiologist initiates image reading.
Influence of different case characteristics (e.g., tumor location, size) on the performance of AI assistance.
Within 40 days after the first radiologist initiates image reading.
Impact of individual physician differences on AI assistance effect
Prazo: Within 40 days after the first radiologist initiates image reading.
Impact of individual differences among physicians on the performance of AI assistance.
Within 40 days after the first radiologist initiates image reading.
Value of AI assistance in reducing diagnostic discrepancy
Prazo: Within 40 days after the first radiologist initiates image reading.
Potential value of AI assistance in reducing diagnostic differences and improving reading agreement.
Within 40 days after the first radiologist initiates image reading.
Impact of model probability information on physician decisions
Prazo: Within 40 days after the first radiologist initiates image reading.
Preliminary analysis of the impact of probability output from AI model on physician decision-making behavior.
Within 40 days after the first radiologist initiates image reading.

Colaboradores e Investigadores

É aqui que você encontrará pessoas e organizações envolvidas com este estudo.

Patrocinador

Investigadores

  • Investigador principal: Guoliang Zheng, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)

Datas de registro do estudo

Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados ​​pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.

Datas Principais do Estudo

Início do estudo (Estimado)

18 de junho de 2026

Conclusão Primária (Estimado)

25 de julho de 2026

Conclusão do estudo (Estimado)

7 de agosto de 2026

Datas de inscrição no estudo

Enviado pela primeira vez

11 de junho de 2026

Enviado pela primeira vez que atendeu aos critérios de CQ

11 de junho de 2026

Primeira postagem (Real)

16 de junho de 2026

Atualizações de registro de estudo

Última Atualização Postada (Real)

16 de junho de 2026

Última atualização enviada que atendeu aos critérios de controle de qualidade

11 de junho de 2026

Última verificação

1 de junho de 2026

Mais Informações

Termos relacionados a este estudo

Outros números de identificação do estudo

  • KY20260512

Plano para dados de participantes individuais (IPD)

Planeja compartilhar dados de participantes individuais (IPD)?

NÃO

Descrição do plano IPD

Due to the restrictions imposed by the ethics committee and the institutional review board regarding the protection of patient privacy, individual participant data will not be shared.

Informações sobre medicamentos e dispositivos, documentos de estudo

Estuda um medicamento regulamentado pela FDA dos EUA

Não

Estuda um produto de dispositivo regulamentado pela FDA dos EUA

Não

Essas informações foram obtidas diretamente do site clinicaltrials.gov sem nenhuma alteração. Se você tiver alguma solicitação para alterar, remover ou atualizar os detalhes do seu estudo, entre em contato com register@clinicaltrials.gov. Assim que uma alteração for implementada em clinicaltrials.gov, ela também será atualizada automaticamente em nosso site .