AI-guided treatment choices for advanced liver cancer
Official title: Treatment STRATification in Patients With Advanced HEPatocellular Carcinoma (STRATHEP)
Run by Universitaire Ziekenhuizen KU Leuven · NCT07863258 · Data updated 2026-11-12
Researchers are testing an AI tool to help predict which patients with advanced hepatocellular carcinoma will respond to modern systemic therapies.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 280 (estimated) |
| Sponsor | Universitaire Ziekenhuizen KU Leuven Academic / other |
| Locations | 5 sites (Anderlecht and 4 other locations) |
| Status | Recruiting |
| Started | October 15, 2026 |
| Main results expected | December 2027 |
| Study ends | December 2031 |
| Registry record updated | October 7, 2026 |
| Trial ID | NCT07863258 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults with hepatocellular carcinoma who have been treated with systemic therapy and have appropriate tissue or MRI data available for analysis.
✕Probably not a fit
Patients without archival tissue or suitable imaging data, or those not receiving systemic therapy, may not benefit from this predictive approach.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study develops and validates a multimodal artificial intelligence model that combines digital pathology and MRI radiomics to predict treatment response in advanced hepatocellular carcinoma. The model uses RECIST 1.1 as the primary benchmark for assessing responses to systemic therapies. The study leverages archival tissue and imaging data, aiming to identify patients most likely to benefit from current systemic options. Participating sites include several major hospitals in Belgium, with data collected under informed consent for secondary use. No interventional treatment is described within the protocol itself; the focus is on predictive modeling and validation.
Why it matters
Potential benefit: If successful, the model could help tailor systemic therapy choices for patients with advanced HCC by predicting who is most likely to respond.
How similar studies have gone: Past work in similar AI-based predictive approaches for cancer treatment responses has shown promise, but integration of digital pathology with MRI radiomics for HCC is an emerging area.
Who can join: the full rules
Show full inclusion / exclusion criteria
Participants eligible for inclusion in this Study must meet all of the following criteria: * Male or female, age \> 18 years * Hepatocellular carcinoma treated with systemic treatment during the disease course * Histological confirmation of HCC within one year of the start of systemic treatment OR the availability of an MRI of the liver within 6 months of the start of systemic treatment * Left-over tissue from previous diagnostic biopsy available * Ability to sign informed consent for secondary use of archival tissue and data collection for study-specific research for patients who are alive and that have not yet reached a 5-year follow-up period. Participants eligible for this Study must not meet any of the following criteria: * Hepatocellular carcinoma not treated systemically * Other malignancies of the liver, including cholangiocarcinoma
Where it's running
-
Hôpital Universitaire de Bruxelles
Anderlecht, Belgium Recruiting -
Imelda Ziekenhuis
Bonheiden, Belgium Recruiting -
AZ Sint-Lucas Brugge
Bruges, Belgium Recruiting -
Universitair Ziekenhuis Antwerpen
Edegem, Belgium Recruiting -
UZ Leuven
Leuven, Belgium Recruiting
Study contacts
- Study coordinator: Jeroen Dekervel, MD PhD
- Email: jeroen.dekervel@uzleuven.be
- Phone: +3216344225
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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.