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
Beijing Tsinghua Chang Gung Hospital
108 participants
Apr 13, 2026
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria4
- 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.
Exclusion Criteria3
- 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.
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Interventions
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.
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.
Locations(1)
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NCT07538882