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NCT Number: NCT07282184

Multimodal Deep Learning for Postoperative Liver Cancer Risk Stratification and Intervention

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.

Recruiting

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Key information

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Phase 1 / Phase 2

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Treatment and study plan

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.

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 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.

Primary outcomes

  1. 2-year Recurrence-free Survival (RFS) Rate

    Time frame: 2 years post-surgery

    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).

Secondary outcomes

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

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

    The proportion of patients in the intervention arm who experience at least one Grade 3 or higher adverse event that is judged by the investigator to be related to the neoadjuvant therapy regimen (HAIC + Lenvatinib + PD-1 inhibitor). Severity will be graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) version 5.0.

  2. Overall Survival (OS)

    Time frame: Up to 3 years post-surgery

    The time from the date of randomization (or enrollment for non-randomized studies) to the date of death from any cause. Patients who are still alive at the time of analysis will be censored at the last known date they were alive.

Study contacts

Contact information is provided by the study sponsor or research team.

Yang Wu, M.D.

CONTACT

[email protected]

+8613636076910

Sponsors and collaborators

Lead sponsor

Tongji Hospital

Other

Registry information

Official study title

A Multimodal Deep Learning-Driven Study for Perioperative Risk Stratification and Precision Intervention in Hepatocellular Carcinoma Recurrence

Important dates

Study start
2025
Primary completion
2027
Study completion
2028
First posted
Dec 15, 2025
Registry last updated
Dec 18, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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