[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100634376":3},{"organization":4,"outcomesModule":7,"designInfo":24,"detailedDescription":27,"studyPopulation":28,"armGroups":29,"interventions":40,"overallOfficials":50,"centralContacts":55,"locations":61,"responsibleParty":78,"collaborators":82,"id":96,"slug":23,"hasResults":97,"nctId":98,"briefTitle":99,"officialTitle":100,"acronym":23,"eligibilityCriteria":101,"healthyVolunteers":97,"sex":102,"minAge":103,"maxAge":23,"enrollmentInfo":104,"targetDuration":23,"studyType":107,"phases":23,"briefSummary":108,"conditions":109,"keywords":111,"overallStatus":64,"whyStopped":23,"lastUpdateSubmitDate":117,"lastUpdatePostDateStruct":118,"startDateStruct":121,"completionDateStruct":122,"leadSponsor":124,"locationsCount":125},{"fullName":5,"class":6},"Beijing Tsinghua Chang Gung Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":23},[9],{"measure":10,"description":11,"timeFrame":12},"Improvement in Overall Accuracy","The difference in average accuracy across the 4 classification tasks between AI-assisted evaluation (experimental) and independent evaluation (control). Accuracy is determined by comparing physicians' predictions against the reference standard (Ground Truth) established by the independent expert panel","Up to 1 week (Assessed upon completion of all case evaluations)",[14,17,20],{"measure":15,"description":16,"timeFrame":12},"Homogenization Effect on Evaluation Accuracy","Assessment of whether the difference in clinical evaluation accuracy between physicians in the primary\u002Fsecondary hospital group and the tertiary hospital groups is significantly reduced under AI assistance compared to unassisted independent evaluation.",{"measure":18,"description":19,"timeFrame":12},"Evaluation Efficiency (Average Time per Case)","Comparison of the average evaluation time (e.g., measured in minutes) per case required by participating physicians when utilizing AI assistance versus performing unassisted independent evaluation.",{"measure":21,"description":22,"timeFrame":12},"Inter-rater Agreement","Comparison of the consistency of evaluation results (staging and treatment decisions) among all participating physicians, assessed using appropriate statistical measures (e.g., Kappa statistics), under AI-assisted versus unassisted conditions.",null,{"allocation":23,"interventionModel":23,"interventionModelDescription":23,"primaryPurpose":23,"observationalModel":25,"timePerspective":26,"maskingInfo":23},"COHORT","PROSPECTIVE","1. Study Description\n\n   Brief Summary: The precise treatment of primary hepatocellular carcinoma (HCC) highly depends on accurate disease staging (CNLC, TNM, BCLC) and scientific treatment decision-making, which necessitate the integration of both imaging and clinical baseline data. This study prospectively recruits HCC patients and clinical physicians across different hospital tiers to evaluate the clinical value of a self-developed artificial intelligence (AI) model in assisting multi-dimensional comprehensive assessment and treatment decision-making. Utilizing a Multi-Rater Multi-Case (MRMC) crossover balanced design, the study compares the accuracy of clinical evaluations performed by physicians under \"unassisted (without AI)\" versus \"AI-assisted\" conditions. A key focus is to explore whether AI can significantly enhance the comprehensive assessment capabilities of physicians in primary\u002Fsecondary care hospitals, thereby prospectively reducing diagnostic and therapeutic heterogeneity across different institutional levels.\n\n   Gold Standard (Reference Standard): The reference standard (Ground Truth) for all prospectively enrolled cases is established by an independent expert panel consisting of 3 authoritative experts. The panel determines the final standard answers for the four classification tasks through blinded independent evaluation and joint discussion (voting system), incorporating complete prospective imaging data, clinical baseline data, multidisciplinary team (MDT) consensus, and final pathological or clinical follow-up results.\n2. Eligibility Criteria\n\n2.1 Evaluator Eligibility:\n\n1. Senior Physicians in Tertiary Hospitals: Employed in the department of hepato-pancreato-biliary surgery, oncology, or related departments in Class III Grade A (tertiary) hospitals, with the professional title of attending physician or above.\n2. Junior Physicians in Tertiary Hospitals: Employed in related departments in Class III Grade A (tertiary) hospitals, with the professional title of resident physician.\n3. Physicians in Primary\u002FSecondary Care Hospitals: Clinical physicians employed in county-level or Class II general hospitals.\n4. Informed Consent: Must voluntarily agree to participate in the assessment and sign the informed consent form.\n\n2.2 Patient\u002FCase Eligibility:\n\nInclusion Criteria:\n\n1. Age \\> 18 years.\n2. Patients prospectively presenting with suspected or newly diagnosed primary hepatocellular carcinoma (HCC) later confirmed by pathology or meeting the China Liver Cancer (CNLC) guidelines.\n3. Complete baseline clinical data acquired during the prospective enrollment period, including complete history of present\u002Fpast illness, ECOG PS score, comprehensive laboratory tests (liver function, coagulation, tumor markers such as AFP, etc.), and baseline abdominal contrast-enhanced CT.\n4. Patients (or their legal representatives) must provide written informed consent for their clinical data to be used in this trial.\n\nExclusion Criteria:\n\n1. Patients with secondary (metastatic) liver cancer or concurrent severe malignancies of other systems.\n2. Patients who fail to complete the required baseline imaging or laboratory tests, preventing accurate staging calculation (e.g., missing data for Child-Pugh score).\n3. Patients who have previously received anti-tumor therapies for liver cancer prior to enrollment.\n\n3\\. Study Design\n\nIntervention Model: Crossover Assignment Masking: Single Blind. Participating evaluators are blinded to the gold standard answers of the cases and to the evaluation results of other participating physicians.\n\nArms and Interventions:\n\nCase Set Partition: 108 prospectively and consecutively enrolled eligible HCC cases are batched and randomly divided into Dataset Set A (54 cases) and Dataset Set B (54 cases). It is ensured that there are no statistically significant differences between the two sets regarding tumor burden, liver function grading, and staging distribution.\n\nEvaluator Grouping: A total of 12 prospectively recruited clinical physicians are included, comprising 4 in the tertiary hospital senior group, 4 in the tertiary hospital junior group, and 4 in the primary\u002Fsecondary hospital group. They are divided into two evaluation groups based on stratified randomization:\n\nGroup A (6 evaluators): 2 tertiary senior, 2 tertiary junior, 2 primary\u002Fsecondary hospital.\n\nGroup B (6 evaluators): 2 tertiary senior, 2 tertiary junior, 2 primary\u002Fsecondary hospital.\n\nArm 1 - Group A Evaluators:\n\nPhase 1 Intervention (Control): Independent evaluation of Set A (54 cases) combining clinical texts and imaging data, recording 4 classification results, without AI assistance.\n\nPhase 2 Intervention (Experimental): Evaluation of Set B (54 cases). The system presents the AI model's 4 prediction results and related evidence; physicians provide the final judgment after comprehensive reference.\n\nArm 2 - Group B Evaluators:\n\nPhase 1 Intervention (Control): Independent evaluation of Set B (54 cases) combining clinical texts and imaging data, recording 4 classification results, without AI assistance.\n\nPhase 2 Intervention (Experimental): Evaluation of Set A (54 cases). Physicians provide the final judgment after referencing the AI model's results.\n\n4\\. Outcome Measures\n\nPrimary Outcome:\n\nImprovement in Overall Accuracy: The difference in average accuracy across the 4 classification tasks between AI-assisted evaluation (experimental group) and independent evaluation (control group).\n\nSecondary Outcomes:\n\nHomogenization Effect: Assessment of whether the difference in clinical evaluation accuracy between physicians in the primary\u002Fsecondary hospital group and the tertiary hospital groups is significantly reduced under AI assistance.\n\nEvaluation Efficiency: Comparison of the average evaluation time per case between physicians with and without AI assistance.\n\nInter-rater Agreement: Comparison of the consistency of evaluation results among physicians (e.g., using Kappa statistics), with and without AI assistance.\n\n5\\. Statistical Analysis Plan \\& Sample Size\n\nSample Size Justification:\n\nThe sample size calculation for this study is based on the expected change in the overall average accuracy across all levels of prospectively recruited physicians. It is estimated that the overall average accuracy without AI assistance is 0.60, and with AI assistance is 0.70.\n\nSetting the significance level for a two-sided test at 0.05 (corresponding to a Z-value of approximately 1.96) and the statistical power at 0.80 (corresponding to a Z-value of approximately 0.84), the sample size was determined using the standard statistical method for comparing two independent proportions. Assuming no clustering effect resulting from multiple case evaluations by the same physician, this calculation indicates that each intervention group requires at least 353 independent evaluations.\n\nPower Verification:\n\nIn the actual configuration of this study, there are 12 physicians in total.\n\nTotal independent evaluations for the control group (without AI) = Group A (6 evaluators) x Set A (54 cases) + Group B (6 evaluators) x Set B (54 cases) = 648 independent evaluations.\n\nTotal independent evaluations for the experimental group (with AI) also = 648 independent evaluations.\n\nSince 648 evaluations is greater than the required base of 353 evaluations, the current configuration of cases and physicians already possesses sufficient statistical power. This sample size provides a conservative margin (approximately 1.8 times the base requirement) to adequately account for any clustering effect (intra-class correlation) resulting from multiple case evaluations by the same physician in this MRMC design.","The study population comprises 108 prospectively and consecutively enrolled adult patients with newly diagnosed primary hepatocellular carcinoma (HCC). Following enrollment and confirmation of complete baseline clinical and imaging data, these 108 patient cases are randomly divided into two equal datasets: Set A (54 cases) and Set B (54 cases). Randomization is stratified to ensure no statistically significant differences between the two sets regarding baseline characteristics such as tumor burden, liver function grading, and staging distribution.\n\nIn the context of this multi-rater multi-case (MRMC) crossover design, these patient cases are allocated to distinct evaluation conditions. Set A cases are assigned to be evaluated by the first group of reviewing physicians without AI assistance (control condition) and by the second group of physicians with AI assistance (experimental condition). Conversely, Set B cases are evaluated by the second group of physicians without AI",[30,36],{"label":31,"type":23,"description":32,"interventionNames":33},"Group A Evaluators","A prospectively recruited group of 6 physicians (2 tertiary senior, 2 tertiary junior, and 2 primary\u002Fsecondary hospital physicians). In Phase 1 (Control), they independently evaluate HCC case Set A without AI assistance. In Phase 2 (Experimental), they evaluate case Set B with the assistance of the AI model.",[34,35],"Diagnostic Test: Unassisted Independent Evaluation","Diagnostic Test: AI-Assisted Evaluation",{"label":37,"type":23,"description":38,"interventionNames":39},"Group B Evaluators","A prospectively recruited group of 6 physicians (2 tertiary senior, 2 tertiary junior, and 2 primary\u002Fsecondary hospital physicians). In Phase 1 (Control), they independently evaluate HCC case Set B without AI assistance. In Phase 2 (Experimental), they evaluate case Set A with the assistance of the AI model.",[34,35],[41,46],{"type":42,"name":43,"description":44,"armGroupLabels":45,"otherNames":23},"DIAGNOSTIC_TEST","Unassisted Independent Evaluation","Physicians independently evaluate the HCC cases and provide staging and treatment decisions using only complete clinical baseline data and imaging data, without any assistance from the AI model.",[31,37],{"type":42,"name":47,"description":48,"armGroupLabels":49,"otherNames":23},"AI-Assisted Evaluation","Physicians evaluate the HCC cases and provide final staging and treatment decisions after reviewing the initial predictions and related evidence generated by the self-developed artificial intelligence (AI) model, alongside the clinical baseline and imaging data.",[31,37],[51],{"name":52,"affiliation":53,"role":54},"Jiahong Dong","Beijing Tsinghua Changgeng Hospital","STUDY_CHAIR",[56],{"name":57,"role":58,"phone":59,"phoneExt":23,"email":60},"Jitao Wang","CONTACT","+8618632957579","wjta05045@btch.edu.cn",[62],{"facility":63,"status":64,"city":65,"state":66,"zip":67,"country":68,"countryCode":69,"cosmosGeoPoint":70,"geoPoint":75,"contacts":76},"Beijing Tsinghua Changgung Hospital","RECRUITING","Beijing","Changping","102218","China","CN",{"type":71,"coordinates":72},"Point",[73,74],116.39723,39.9075,{"lat":74,"lon":73},[77],{"name":57,"role":58,"phone":59,"phoneExt":23,"email":60},{"type":79,"investigatorFullName":80,"investigatorTitle":81,"investigatorAffiliation":5,"oldNameTitle":23,"oldOrganization":23},"PRINCIPAL_INVESTIGATOR","Jiahong Dong，MD","Hospital President",[83,85,87,89,91,93],{"name":84,"class":6},"Affiliated Hospital of Hebei University",{"name":86,"class":6},"Meng Chao Hepatobiliary Hospital of Fujian Medical University",{"name":88,"class":6},"Zhongnan Hospital",{"name":90,"class":6},"Xingtai People's Hospital",{"name":92,"class":6},"Second Affiliated Hospital of Xi'an Jiaotong University",{"name":94,"class":95},"Xinan hospital of Army Medical University","UNKNOWN","100634376",false,"NCT07538882","AI-Assisted Staging and Treatment Decision-Making for Hepatocellular Carcinoma","A Prospective, Randomized, Controlled, Crossover Study of Artificial Intelligence-Assisted Multi-Dimensional Staging and Treatment Decision-Making for Hepatocellular Carcinoma","Inclusion Criteria:\n\n* Age \\>= 18 years.\n* Patients prospectively presenting with suspected or newly diagnosed primary hepatocellular carcinoma (HCC) later confirmed by pathology or meeting the China Liver Cancer (CNLC) guidelines.\n* Complete baseline clinical data acquired during the prospective enrollment period, including complete history of present\u002Fpast illness, ECOG PS score, comprehensive laboratory tests (liver function, coagulation, tumor markers such as AFP, etc.), and baseline abdominal contrast-enhanced CT.\n* Patients (or their legal representatives) must provide written informed consent for their clinical data to be used in this trial.\n\nExclusion Criteria:\n\n* Patients with secondary (metastatic) liver cancer or concurrent severe malignancies of other systems.\n* Patients who fail to complete the required baseline imaging or laboratory tests, preventing accurate staging calculation (e.g., missing data for Child-Pugh score).\n* Patients who have previously received anti-tumor therapies for liver cancer prior to enrollment.","ALL","18 Years",{"count":105,"type":106},108,"ESTIMATED","OBSERVATIONAL","The precise treatment of primary hepatocellular carcinoma (HCC) highly depends on accurate disease staging (CNLC, TNM, BCLC) and scientific treatment decision-making, which necessitate the integration of both imaging and clinical baseline data. This study prospectively recruits HCC patients and clinical physicians across different hospital tiers to evaluate the clinical value of a self-developed artificial intelligence (AI) model in assisting multi-dimensional comprehensive assessment and treatment decision-making. Utilizing a Multi-Rater Multi-Case (MRMC) crossover balanced design, the study compares the accuracy of clinical evaluations performed by physicians under \"unassisted (without AI)\" versus \"AI-assisted\" conditions. A key focus is to explore whether AI can significantly enhance the comprehensive assessment capabilities of physicians in primary\u002Fsecondary care hospitals, thereby prospectively reducing diagnostic and therapeutic heterogeneity across different institutional levels.",[110],"Hepatocellular Carcinoma (HCC)",[112,113,114,115,116],"Hepatocellular Carcinoma","Artificial Intelligence","Staging","Clinical Decision-Making","Diagnostic Accuracy","2026-04-13",{"date":119,"type":120},"2026-04-20","ACTUAL",{"date":117,"type":106},{"date":123,"type":106},"2026-05-20",{"name":5,"class":6},1]