[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100642236":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":32,"centralContacts":37,"locations":42,"responsibleParty":59,"collaborators":25,"id":61,"slug":62,"hasResults":63,"nctId":64,"briefTitle":65,"officialTitle":66,"acronym":67,"eligibilityCriteria":68,"healthyVolunteers":63,"sex":69,"minAge":25,"maxAge":25,"enrollmentInfo":70,"targetDuration":25,"studyType":73,"phases":74,"briefSummary":76,"conditions":77,"keywords":79,"overallStatus":44,"whyStopped":25,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":87,"completionDateStruct":89,"leadSponsor":91,"locationsCount":92},{"fullName":5,"class":6},"Liaoning Cancer Hospital & Institute","OTHER",[8,15],{"label":9,"type":10,"description":11,"interventionNames":12},"Standard reading 1","EXPERIMENTAL","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.",[13,14],"Diagnostic Test: Utilizing the TRACE model to assist radiologists in T-staging","Other: washout period",{"label":16,"type":10,"description":17,"interventionNames":18},"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.",[14,13],[20,26,30],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"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.",[9],null,{"type":6,"name":27,"description":28,"armGroupLabels":29,"otherNames":25},"washout period","Participants are required to observe a washout period of at least 30 days between consecutive interventions\u002Fassessments.",[16,9],{"type":21,"name":22,"description":23,"armGroupLabels":31,"otherNames":25},[16],[33],{"name":34,"affiliation":35,"role":36},"Guoliang Zheng","Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)","PRINCIPAL_INVESTIGATOR",[38],{"name":34,"role":39,"phone":40,"phoneExt":25,"email":41},"CONTACT","13322400728","zhengboren1@126.com",[43],{"facility":35,"status":44,"city":45,"state":46,"zip":47,"country":48,"countryCode":49,"cosmosGeoPoint":50,"geoPoint":55,"contacts":56},"RECRUITING","Shenyang","Liaoning","110024","China","CN",{"type":51,"coordinates":52},"Point",[53,54],123.43278,41.79222,{"lat":54,"lon":53},[57],{"name":58,"role":39,"phone":40,"phoneExt":25,"email":41},"Guoliang Zheng, doctor",{"type":36,"investigatorFullName":34,"investigatorTitle":60,"investigatorAffiliation":5,"oldNameTitle":25,"oldOrganization":25},"doctor","100642236","two-component-radiology-guided-autonomous-cascade-engine-trace-100642236",false,"NCT07651644","Two-component Radiology-guided Autonomous Cascade Engine (TRACE)","Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)","TRACE","Inclusion Criteria (Imaging Data)\n\n1. Contrast-enhanced CT (CE-CT) images of gastric cancer patients from the Liaoning Cancer Hospital;\n2. Patients with a definitive postoperative pathological diagnosis of gastric cancer and a clear T-stage classification (T1-T4, including T4a and T4b);\n3. Imaging data must be complete and of sufficient quality to meet diagnostic and analytical requirements, with no significant artefacts or missing key data;\n4. Complete clinical and pathological information must be available to establish a diagnostic gold standard for comparison.\n\nPhysician Inclusion Criteria (Image Readers)\n\n1. Radiologists holding a valid medical licence;\n2. From the radiology department of a Grade A tertiary hospital or a non-Grade A tertiary hospital;\n3. Classified as senior or junior physicians based on clinical experience;\n4. Voluntarily participating in this study and completing both the non-AI-assisted and AI-assisted image interpretation tasks.\n\nCase Exclusion Criteria\n\n1. Severe missing imaging data or quality failing to meet analysis requirements (e.g., severe motion artefacts);\n2. Lack of clear postoperative pathological T-staging results;\n3. Cases not involving gastric cancer or with incomplete pathological information;\n4. Cases of duplicate enrolment or inconsistent data recording.\n\nPhysician Exclusion Criteria\n\n1. Those unable to complete all image review tasks or demonstrating severe non-compliance;\n2. Those who withdraw during the study period and are unable to provide complete data for both phases of image review;\n3. Those who fail to complete the AI-assisted and non-AI-assisted interpretation processes as specified.\n\nWithdrawal Criteria\n\n1. Physicians who voluntarily withdraw from the study for personal reasons (e.g., time, health or work commitments);\n2. Physicians who fail to complete the required image review tasks or have data missing in excess of the specified threshold;\n3. 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.","ALL",{"count":71,"type":72},54,"ESTIMATED","INTERVENTIONAL",[75],"NA","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.\n\nAll 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.",[78],"Gastric Cancer (Diagnosis)",[80,81,82],"AI","Gastric Cancer","T Stage","2026-06-11",{"date":85,"type":86},"2026-06-16","ACTUAL",{"date":88,"type":72},"2026-06-18",{"date":90,"type":72},"2026-08-07",{"name":5,"class":6},1]