Quantitative Imaging for Evaluation of Response to Cancer Therapies
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Hepatocellular Carcinoma
- Sponsor
- Chinese Academy of Sciences
- Enrollment
- 1200
- Locations
- 1
- Primary Endpoint
- quantitative image features extracted from CT images can be used as imaging marker for prognosis
- Last Updated
- 10 years ago
Overview
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.
Investigators
Chongwei Chi, Ph.D
Quantitative Imaging for Evaluation of Response to Cancer Therapies
Chinese Academy of Sciences
Eligibility Criteria
Inclusion Criteria
- •The purpuse of our research is to improve treatment ,therefore we have no creteria.
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
quantitative image features extracted from CT images can be used as imaging marker for prognosis
Time Frame: five(year)