A Prospective, Randomized, Controlled, Crossover Study of Artificial Intelligence-Assisted Multi-Dimensional Staging and Treatment Decision-Making for Hepatocellular Carcinoma
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 108
- 试验地点
- 1
- 主要终点
- Improvement in Overall Accuracy
研究概览
简要总结
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/secondary care hospitals, thereby prospectively reducing diagnostic and therapeutic heterogeneity across different institutional levels.
详细描述
- Study Description
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/secondary care hospitals, thereby prospectively reducing diagnostic and therapeutic heterogeneity across different institutional levels.
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. 2. Eligibility Criteria
2.1 Evaluator Eligibility:
- 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.
- Junior Physicians in Tertiary Hospitals: Employed in related departments in Class III Grade A (tertiary) hospitals, with the professional title of resident physician.
- Physicians in Primary/Secondary Care Hospitals: Clinical physicians employed in county-level or Class II general hospitals.
- Informed Consent: Must voluntarily agree to participate in the assessment and sign the informed consent form.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age >= 18 years.
- •Patients prospectively presenting with suspected or newly diagnosed primary hepatocellular carcinoma (HCC) later confirmed by pathology or meeting the China Liver Cancer (CNLC) guidelines.
- •Complete baseline clinical data acquired during the prospective enrollment period, including complete history of present/past illness, ECOG PS score, comprehensive laboratory tests (liver function, coagulation, tumor markers such as AFP, etc.), and baseline abdominal contrast-enhanced CT.
- •Patients (or their legal representatives) must provide written informed consent for their clinical data to be used in this trial.
排除标准
- •Patients with secondary (metastatic) liver cancer or concurrent severe malignancies of other systems.
- •Patients who fail to complete the required baseline imaging or laboratory tests, preventing accurate staging calculation (e.g., missing data for Child-Pugh score).
- •Patients who have previously received anti-tumor therapies for liver cancer prior to enrollment.
研究组 & 干预措施
Group A Evaluators
A prospectively recruited group of 6 physicians (2 tertiary senior, 2 tertiary junior, and 2 primary/secondary 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.
干预措施: Unassisted Independent Evaluation (Diagnostic Test)
Group A Evaluators
A prospectively recruited group of 6 physicians (2 tertiary senior, 2 tertiary junior, and 2 primary/secondary 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.
干预措施: AI-Assisted Evaluation (Diagnostic Test)
Group B Evaluators
A prospectively recruited group of 6 physicians (2 tertiary senior, 2 tertiary junior, and 2 primary/secondary 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.
干预措施: Unassisted Independent Evaluation (Diagnostic Test)
Group B Evaluators
A prospectively recruited group of 6 physicians (2 tertiary senior, 2 tertiary junior, and 2 primary/secondary 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.
干预措施: AI-Assisted Evaluation (Diagnostic Test)
结局指标
主要结局
Improvement in Overall Accuracy
时间窗: Up to 1 week (Assessed upon completion of all case evaluations)
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
次要结局
- Homogenization Effect on Evaluation Accuracy(Up to 1 week (Assessed upon completion of all case evaluations))
- Evaluation Efficiency (Average Time per Case)(Up to 1 week (Assessed upon completion of all case evaluations))
- Inter-rater Agreement(Up to 1 week (Assessed upon completion of all case evaluations))
研究者
Jiahong Dong，MD
Hospital President
Beijing Tsinghua Chang Gung Hospital