Multimodal Deep Learning for Postoperative Liver Cancer Risk Stratification and Intervention

NCT07282184 | Recruiting Phase 1

• 1 other identifier: TJ-IRB202510021
• Study Type: interventional
• Target: 144
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Hepotacellular Carcinoma

Keywords

deep learning; recurrence pattern; Hepatocellular Carcinoma; Neoadjuvant Therapy

Outcome Measures

Primary Outcomes (1)

• 2-year Recurrence-free Survival (RFS) Rate

  The primary outcome is the proportion of patients who remain free of recurrence at 2 years after curative liver resection. Recurrence is defined as radiologically confirmed (via contrast-enhanced CT or MRI according to RECIST 1.1 criteria) appearance of new lesions (intrahepatic or extrahepatic) or death from any cause, whichever occurs first. This outcome will be compared between the intervention arm (AI-guided neoadjuvant therapy + surgery) and the control arm (surgery alone).

  2 years post-surgery

Secondary Outcomes (2)

• Incidence of Grade 3 or Higher Treatment-Related Adverse Events (TRAEs)

  From the start of neoadjuvant therapy until 30 days after the last dose of neoadjuvant drugs

• Overall Survival (OS)

  Up to 3 years post-surgery

Study Arms (2)

Multimodal AI-guided Neoadjuvant Therapy + Surgery

EXPERIMENTAL

Participants identified as high-risk for recurrence by the multimodal deep learning (PRE) model receive neoadjuvant therapy prior to curative liver resection. The neoadjuvant regimen consists of hepatic arterial infusion chemotherapy (HAIC) with the mFOLFOX6 regimen, combined with a PD-1 inhibitor and Lenvatinib. This is followed by standard curative liver resection.

Combination Product: Neoadjuvant HAIC + Lenvatinib + PD-1 Inhibitor; Procedure: Curative Liver Resection; Other: Multimodal AI Risk Stratification

Surgery Alone (High-Risk)

ACTIVE COMPARATOR

Participants identified as high-risk for recurrence by the multimodal deep learning (PRE) model proceed directly to standard curative liver resection without receiving neoadjuvant therapy.

Procedure: Curative Liver Resection; Other: Multimodal AI Risk Stratification

Interventions

Curative Liver Resection PROCEDURE

Standard anatomic or non-anatomic liver resection with the intention of achieving complete tumor removal with negative margins. This is the standard surgical procedure for resectable hepatocellular carcinoma

Multimodal AI-guided Neoadjuvant Therapy + Surgery; Surgery Alone (High-Risk)

Neoadjuvant HAIC + Lenvatinib + PD-1 Inhibitor COMBINATION_PRODUCT

A combination drug regimen used as neoadjuvant therapy. Includes Hepatic Arterial Infusion Chemotherapy (HAIC) with mFOLFOX6 (Oxaliplatin, Leucovorin, Fluorouracil), oral Lenvatinib, and an intravenous PD-1 inhibitor.

Multimodal AI-guided Neoadjuvant Therapy + Surgery

Multimodal AI Risk Stratification OTHER

The use of a pre-established deep learning model (PRE/POST model) to analyze preoperative imaging and clinical data to stratify patients' risk of aggressive recurrence. This stratification is used to determine treatment arm assignment.

Multimodal AI-guided Neoadjuvant Therapy + Surgery; Surgery Alone (High-Risk)

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age and Consent: Patients aged 18-75 years who are able to understand and voluntarily sign an Informed Consent Form.
• Diagnosis: Clinical diagnosis of BCLC stage 0-A hepatocellular carcinoma, confirmed by histopathology or non-invasive imaging criteria per guidelines.
• Surgical Candidacy: Scheduled to undergo curative-intent liver resection.
• Risk Stratification: Predicted as high-risk for aggressive recurrence by the pre-operative multimodal deep learning model (PRE score ≥ 0.5).
• Liver Function: Child-Pugh liver function class A (score ≤ 7).
• Performance Status: ECOG Performance Status of 0 or 1.
• Imaging Requirement: Availability of a standard pre-operative MRI scan (including non-contrast, arterial, portal venous, and delayed phases) performed within 1 month prior to enrollment, with acceptable image quality.
• Follow-up Commitment: Willing and able to comply with the study procedures and scheduled follow-up for at least 2 years.

You may not qualify if:

• Pathology: Postoperative pathological confirmation of non-HCC malignancy (e.g., cholangiocarcinoma, combined hepatocellular-cholangiocarcinoma).
• Other Malignancies: History of other active malignancies within the past 5 years, except for appropriately treated carcinoma in situ of the cervix, non-melanoma skin cancer, or other cancers with a very low risk of recurrence.
• Early Mortality/Loss: Death from any cause or loss to follow-up within 90 days after surgery.
• Contraindications to Protocol Therapy: Known hypersensitivity to any component of the neoadjuvant therapy regimen (e.g., oxaliplatin, fluorouracil, PD-1 inhibitors, lenvatinib).
• Severe, uncontrolled medical conditions including but not limited to: Uncontrolled cardiac disease (e.g., NYHA Class III or IV heart failure), Severe renal dysfunction, Uncontrolled hypertension.
• Inability to Participate: Any condition that, in the opinion of the investigator, would compromise the patient's ability to participate in the study or interfere with the evaluation of the study objectives.

Sponsors & Collaborators

• Tongji Hospital: lead

Study Sites (1)

Tongji Hospital

Wuhan, Hubei, 438700, China

RECRUITING

MeSH Terms

Conditions

Carcinoma, Hepatocellular

Interventions

lenvatinib; Immune Checkpoint Inhibitors

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Liver Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases

Intervention Hierarchy (Ancestors)

Molecular Mechanisms of Pharmacological Action; Pharmacologic Actions; Chemical Actions and Uses; Antineoplastic Agents, Immunological; Antineoplastic Agents; Therapeutic Uses

Central Study Contacts

Yang Wu, M.D.

CONTACT

+8613636076910; 255001907@qq.com

Study Design

• Study Type: interventional
• Phase: phase 1
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: December 2, 2025
• First Posted: December 15, 2025
• Study Start: October 26, 2025
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): June 30, 2028
• Last Updated: December 18, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)