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.
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.
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.
Beijing Tsinghua Chang Gung Hospital
Lead sponsor
Affiliated Hospital of Hebei University
Collaborator
Meng Chao Hepatobiliary Hospital of Fujian Medical University
Collaborator
Zhongnan Hospital
Collaborator
Xingtai People's Hospital
Collaborator
Second Affiliated Hospital of Xi'an Jiaotong University
Collaborator
Xinan hospital of Army Medical University
Collaborator