Clinical Decision Biological Biomarker and Prognosis Prediction of Hepatocellular Carcinoma by Deep Learning

Recruiting

• Conditions: Contrast-enhanced Ultrasound; Surgery; Hepatocellular Carcinoma; Prognosis; Ablation

• Registration Number: NCT05257694
• Lead Sponsor: Ping Liang

Brief Summary

Developing a deep learning model based on contrast-enhanced ultrasound (CEUS) to predict the prognosis of hepatocellular carcinoma (HCC) and aid choose operation decisions

Detailed Description

Collecting CEUS and clinical data of HCC from different institutions retrospectively.

Developing a deep learning model based on CEUS to predict the prognosis of HCC. Developing a deep learning model based on CEUS to choose a better operation (ablation or surgery) of HCC patients.

Then, validating the deep learning model in the prospective data.

Study Timeline

• Study Start: 2022-01-01
• Study First Submitted: 2022-02-13
• Study First Submitted QC: 2022-02-24
• Study First Posted: 2022-02-25
• Status Verified: 2022-10
• Last Update Submitted: 2022-10-19
• Last Update Posted: 2022-10-21
• Primary Completion: 2025-12-31
• Study Completion: 2025-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1

Inclusion Criteria

• patients with HCC (Ia, Ib, IIa stage) China liver cancer staging who underwent resection or ablation
• without macro-vascular invasion
• Child-Pugh A/B grade
• HCC is proved by pathological examination or two enhanced imaging
• CEUS (Sonovue or Sonozoid) images are performed two weeks before the operation
• Invasive biomarker or prognosis of HCC available
• CEUS images are included in at least three stages (Arterial phase, Portal phase, and Late phase)

Exclusion Criteria

• postop follow-up loss or expired less than 3 months
• patients with co-malignancy
• poor images quality for analyzing

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Recurrence-free survival (RFS)	Immediately after the surgery or ablation	Recurrence-free survival is defined as the time elapsed between a predefined point in time (the date of diagnosis, randomization or the intervention) and any recurrence (local, regional, or distant) or death due to any cause (death is an event).

Secondary Outcome Measures

Name	Time	Method
Recurrence	Immediately after the surgery or ablation	Recurrence included local tumor progression, regional recurrence, or distant recurrence.

Trial Locations

Locations (1)

Chinese PLA General Hospital; 🇨🇳 Beijing, Beijing, China