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 ther

Trial Details

NCT ID
NCT07282184
Phase
PHASE1 / PHASE2
Sponsor
Tongji Hospital
Status
RECRUITING
Cancer Type
Liver Cancer
Interventions
  • Neoadjuvant HAIC + Lenvatinib + PD-1 Inhibitor
  • Curative Liver Resection
  • Multimodal AI Risk Stratification
Locations (sample)
  • Wuhan, Hubei, China|30.58333,114.26667

Key Eligibility 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).

For full eligibility, visit ClinicalTrials.gov.

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