AI-assisted staging and treatment decisions for hepatocellular carcinoma
Official title: A Prospective, Randomized, Controlled, Crossover Study of Artificial Intelligence-Assisted Multi-Dimensional Staging and Treatment Decision-Making for Hepatocellular Carcinoma
Run by Beijing Tsinghua Chang Gung Hospital · NCT07538882 · Data updated 2026-11-12
This project will test whether an AI tool helps doctors more accurately stage HCC and choose treatments for adult patients with new or suspected primary liver cancer.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 108 (estimated) |
| Sponsor | Beijing Tsinghua Chang Gung Hospital Academic / other |
| Locations | 1 site (Beijing, Changping) |
| Status | Recruiting |
| Started | April 13, 2026 |
| Main results expected | May 13, 2026 |
| Study ends | May 20, 2026 |
| Registry record updated | April 20, 2026 |
| Trial ID | NCT07538882 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (≥18 years) with suspected or newly diagnosed primary HCC who can provide consent and have complete baseline clinical data and contrast-enhanced abdominal CT are ideal candidates.
✕Probably not a fit
Patients with secondary (metastatic) liver cancer, concurrent other active malignancies, missing required imaging or lab data, or prior anti-tumor treatment are unlikely to benefit or be eligible.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This prospective, multi-center study enrolls adults with suspected or newly diagnosed primary hepatocellular carcinoma and compares physician decisions made with and without an AI assistance tool using a balanced multi-rater, multi-case crossover design. Physicians from different hospital tiers review the same imaging and baseline clinical data under unassisted and AI-assisted conditions, and their staging (CNLC, TNM, BCLC) and treatment recommendations are recorded. An independent three-expert panel establishes the reference standard using full imaging, clinical data, and multidisciplinary discussion. The design specifically examines whether AI reduces diagnostic and therapeutic variability between primary/secondary hospitals and higher-tier centers.
Why it matters
Potential benefit: If successful, the AI could improve staging accuracy and lead to more consistent, evidence-aligned treatment choices across hospitals, especially benefiting patients at lower-tier centers.
How similar studies have gone: Prior AI work in liver imaging has shown promise for lesion detection and staging, but prospective multi-center MRMC comparisons of AI-assisted clinical decision-making remain limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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 Criteria: * 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.
Where it's running
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Beijing Tsinghua Changgung Hospital
Beijing, Changping, China Recruiting
Study contacts
- Study coordinator: Jitao Wang
- Email: wjta05045@btch.edu.cn
- Phone: +8618632957579
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.