Neoadjuvant therapy for patients with resectable hepatocellular carcinoma using deep learning.
Official title: Efficacy and Safety of Neoadjuvant HAIC Combined With Tislelizumab and Lenvatinib in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model: a Multicenter Randomized Controlled Trial.
Run by Tongji Hospital · NCT06420440 · Data updated 2026-11-12
This study is testing a new treatment plan using a combination of chemotherapy and other medications for patients with early-stage liver cancer to see if it helps them live longer and reduces the chances of the cancer coming back after surgery.
Quick facts
| Phase | Phase 2 |
|---|---|
| Study type | Interventional |
| Ages | 18 Years to 75 Years |
| Sex | All |
| Enrollment | 160 (estimated) |
| Sponsor | Tongji Hospital Academic / other |
| Drugs / interventions | Lenvatinib |
| Locations | 2 sites (Xiangyang, Hubei and 1 other locations) |
| Status | Recruiting |
| Started | June 1, 2024 |
| Main results expected | May 31, 2026 |
| Study ends | May 31, 2027 |
| Registry record updated | May 1, 2025 |
| Trial ID | NCT06420440 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are adults aged 18-75 with resectable HCC at high risk for recurrence, as determined by imaging and genetic data.
✕Probably not a fit
Patients with distant metastasis or those who have previously received treatment for HCC may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This clinical trial investigates the use of neoadjuvant therapy in patients with resectable hepatocellular carcinoma (HCC) identified through a multimodal deep learning model. The approach combines hepatic arterial infusion chemotherapy, lenvatinib, and PD-1 monoclonal antibodies to reduce the risk of postoperative recurrence and improve overall survival. The study utilizes pre-treatment genetic testing, digital pathology, and enhanced MRI data to create an artificial intelligence model that predicts which patients will benefit from this treatment regimen. The trial aims to validate the effectiveness of this model in guiding treatment decisions for high-risk HCC patients.
Why it matters
Potential benefit: If successful, this approach could significantly reduce recurrence rates and improve survival outcomes for patients with high-risk hepatocellular carcinoma.
How similar studies have gone: While the use of deep learning in oncology is emerging, this specific approach to neoadjuvant therapy in HCC is novel and has not been extensively tested in prior studies.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Aged 18-75. 2. No previous local or systemic treatment for hepatocellular carcinoma. 3. Child-Pugh liver function score ≤ 7. 4. ECOG PS 0-1. 5. No serious organic diseases of the heart, lungs, brain, kidneys, etc. 6. Enhanced MRI determines that the tumor is technically resectable but at high risk for recurrence(BCLC-A tumor diameter more than or equal to 5cm; BCLC-B; BCLC-C) ; without distant metastasis. 7. Pathologic type of hepatocellular carcinoma confirmed by puncture biopsy. 8. Multimodal Deep Learning Model Screening Based on Pathology, Imaging, and Genetic Data Suggests Benefit from HAIC in Combination with Lenvatinib and PD-1 inhibitors. Exclusion Criteria: 1. Pregnant and lactating women. 2. Suffering from a condition that interferes with the absorption, distribution, metabolism, or clearance of the study drug (e.g., severe vomiting, chronic diarrhea, intestinal obstruction, impaired absorption, etc.). 3. A history of gastrointestinal bleeding within the previous 4 weeks or a definite predisposition to gastrointestinal bleeding (e.g., known locally active ulcer lesions, fecal occult blood ++ or more, or gastroscopy if persistent fecal occult blood +) that has not been targeted, or other conditions that may have caused gastrointestinal bleeding (e.g., severe fundoplication/esophageal varices), as determined by the investigator. 4. Active infection. 5. Other significant clinical and laboratory abnormalities that affect the safety evaluation. 6. Inability to follow the study protocol for treatment or follow up as scheduled.
Where it's running
-
Huapeng Sun
Xiangyang, Hubei, China Recruiting -
Enyu Liu
Jinan, Shandong, China Recruiting
Study contacts
- Study coordinator: WanGuang Zhang
- Email: wgzhang@tjh.tjmu.edu.cn
- Phone: 13886195965
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
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QL1706 (iparomlimab + tuvonralimab) with TACE and lenvatinib for second-line treatment of unresectable intermediate-to-advanced liver cancer
This test will try whether adding QL1706 (iparomlimab plus tuvonralimab) to TACE and lenvatinib helps people with unresectable intermediate-to-advanced hepatocellular carcinoma who progressed after PD-1/PD-L1 plus bevacizumab.
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Combining sintilimab, lenvatinib, and HAIC for treating borderline resectable liver cancer before surgery
This study is testing if a combination of three treatments can help people with borderline resectable liver cancer do better in surgery compared to having surgery right away.
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This study tests whether adding SBRT to standard lenvatinib plus pembrolizumab helps people with metastatic kidney cancer who have stable disease after initial treatment.
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This study tests whether Cadonilimab plus Lenvatinib can help adults with unresectable, advanced renal cell carcinoma, especially clear cell type, after prior treatments.
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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.