RecruitingNot Applicable

AI-Based Prediction of HCC Recurrence Patterns After Resection (APAR)

China353 participantsStarted 2025-06-10

Plain-language summary

This observational study aims to validate a deep learning model for predicting aggressive recurrence patterns in patients with early-stage liver cancer (HCC) after surgery. The main question it aims to answer is: Can the AI model accurately identify patients at high risk of cancer recurrence within 2 years after surgery? Participants will provide clinical data and undergo standard surgery, followed by 2-year imaging surveillance. Their data will be used for both AI prediction and validation of recurrence patterns.

Who can participate

Age range18 Years – 75 Years

SexALL

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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

Inclusion Criteria: * Aged 18-75 years, regardless of gender. * BCLC stage 0-A, scheduled for curative liver resection. * Preoperative clinical diagnosis of hepatocellular carcinoma (HCC). * Availability of dynamic contrast-enhanced MRI within 1 month before surgery, with acceptable image quality. * Child-Pugh liver function score ≤7. * ECOG Performance Status (PS) 0-1. * No severe organic diseases of the heart, lungs, brain, or other vital organs. Exclusion Criteria: * Concurrent other malignancies (except cured non-melanoma skin cancer or cervical carcinoma in situ). * Postoperative pathology confirms non-HCC diagnosis. * Pregnant or lactating women. * History of organ transplantation. * Inability to comply with the study protocol or follow-up schedule.

What they're measuring

Accuracy of AI Model in Predicting Aggressive HCC Recurrence (AUC)

Timeframe: 2 years post-surgery

Trial details

NCT IDNCT07062380

SponsorTongji Hospital

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2026-06-10

Contact for this trial

Yang Wu, M.D.

+8613636076910 255001907@qq.com