Radiomics of Hepatocellular Carcinoma

• Conditions: Hepatocellular Carcinoma

• Registration Number: NCT02757846
• Lead Sponsor: Chinese Academy of Sciences

Brief Summary

We propose a radiomics approach to identify prognostic biomarkers of HCC and provide patients with some reasonable advice for their therapies.

Detailed Description

Radiomics is emerging fields that is based on quantitative analysis of medical images. Tri-phasic CT images are currently the standard imaging modality for the management of HCC. Our goal is to improve treatment decisions of HCC patients through better understanding of their prognosis based on radiomics modeling of HCC. Radiomics is defined as the extraction of quantitative image features from medical images. We will use triphasic CT data of at least 200 patients and develop a robust strategy to extract imaging features from CT. We will use deep learning in the form of a Convolutional Neural Network to segment HCC lesions and use image feature extraction algorithms with supervised classification to predict prognosis.

Study Timeline

• Status Verified: 2016-04
• Study First Submitted: 2016-04-28
• Study First Submitted QC: 2016-04-28
• Last Update Submitted: 2016-04-28
• Study First Posted: 2016-05-02
• Last Update Posted: 2016-05-02
• Study Start: 2017-04
• Primary Completion: 2022-03
• Study Completion: 2022-03

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 1200

Inclusion Criteria

• The purpuse of our research is to improve treatment ,therefore we have no creteria.

Exclusion Criteria

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
quantitative image features extracted from CT images can be used as imaging marker for prognosis	five(year)

Trial Locations

Locations (1)

Key Laboratory of Molecular Imaging, Chinese Academy of Sciences; 🇨🇳 Beijing, Beijing, China