Precision treatment for advanced liver cancer using advanced data models

Precision Treatment of Unresectable Liver Cancer Based on Multi-omics Deep Learning Model: a Multi-center Prospective Single-arm Study

This study is testing a new way to treat advanced liver cancer by using a special computer model to help decide if a combination of treatments will work better for patients who can’t have surgery.

Quick facts

• Phase: Phase 1
• Study type: Interventional
• Enrollment: 30 (estimated)
• Ages: 18 Years to 75 Years
• Sex: All
• Sponsor: Tongji Hospital (Academic / other)
• Drugs / interventions: Lenvatinib, immunotherapy
• Locations: 1 site (Wuhan, Hubei)
• Trial ID: NCT06463444 on ClinicalTrials.gov

What this trial studies

This clinical trial focuses on patients with unresectable hepatocellular carcinoma (HCC) who cannot undergo surgery. It aims to improve treatment outcomes by utilizing a multi-omics deep learning model that integrates various biological data, including radiomics, pathology, genomics, and immunomics. The study will evaluate the effectiveness of a combination therapy involving hepatic arterial infusion chemotherapy (HAIC), Tislelizumab, and Lenvatinib, guided by predictions from the deep learning model. By accurately predicting which patients will benefit from this therapy, the trial seeks to enhance individualized treatment approaches for HCC.

Who should consider this trial

Good fit: Ideal candidates are adults aged 18-75 with unresectable HCC who have not received prior local or systemic treatments.

Not a fit: Patients with serious organic diseases or those who have had recent gastrointestinal bleeding may not benefit from this study.

Why it matters

Potential benefit: If successful, this approach could significantly improve treatment responses and outcomes for patients with advanced liver cancer.

How similar studies have performed: Other studies have shown promising results with similar multi-omics approaches, indicating potential for success in this novel application.

Eligibility 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 unresectable.
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 this trial is running

Wuhan, Hubei

• Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan, Hubei, China (Recruiting)

Study contacts

• Principal investigator: WanGuang Zhang — Tongji Hospital
• Study coordinator: WanGuang Zhang

Conditions HCC; Precision Therapy