NCT02757846

Radiomics of Hepatocellular Carcinoma

Quantitative Imaging for Evaluation of Response to Cancer Therapies

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 1,200 (estimated)
• Sponsor: Chinese Academy of Sciences · Other Government
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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.

Conditions

• Hepatocellular Carcinoma

Timeline

• Start date: 2017-04-01
• Primary completion: 2022-03-01
• Completion: 2022-03-01
• First posted: 2016-05-02
• Last updated: 2016-05-02

Locations

1 site across 1 country: China