Multimodal Deep Learning for Postoperative Liver Cancer Risk Stratification and Intervention
A Multimodal Deep Learning-Driven Study for Perioperative Risk Stratification and Precision Intervention in Hepatocellular Carcinoma Recurrence
Tongji Hospital
144 participants
Oct 26, 2025
INTERVENTIONAL
Conditions
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.
Eligibility
Inclusion Criteria8
- 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 Criteria6
- 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.
Interventions
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.
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
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.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07282184