drugset / Trial / NCT07282184

Multimodal Deep Learning for Postoperative Liver Cancer Risk Stratification and Intervention

NCT07282184

Phase 1/2 Recruiting 144 enrolled Tongji Hospital
Non-randomizedParallel-groupOpen-labelTreatment

Summary

This study is for patients with early-stage liver cancer who are planning to have surgery. The goal of this research is to see if a personalized treatment plan, guided by a computer model (an artificial intelligence tool), can help prevent the cancer from coming back after surgery. First, the computer model will analyze each patient's medical images and health data to predict their personal risk of the cancer returning. Patients whom the model predicts have a high risk of the cancer coming back will be offered a special treatment plan. This plan involves receiving medication (neoadjuvant therapy) before surgery and additional medication (adjuvant therapy) after surgery. The effectiveness of this plan will be compared to the standard approach of surgery alone. The main goal is to see if this new, personalized plan can better prevent the cancer from returning within 2 years after surgery. The study will also closely monitor the safety of the medications used. All patients in the study will be followed closely for 2 years with regular scans and check-ups to monitor their health.

Timeline

Start
2025-10-26
Primary completion
2027-06-30
Completion
2028-06-30

Drugs

EvaluationDrugModalityDoseRoute
Subject Lenvatinib Small molecule Oral
Subject Leucovorin Small molecule Other
Subject Oxaliplatin Small molecule Other
Subject fluorouracil Small molecule Other