Radiomics of Hepatocellular Carcinoma

Quantitative Imaging for Evaluation of Response to Cancer Therapies

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT02757846

Enrollment

1200

Registered

2016-05-02

Start date

2017-04-30

Completion date

2022-03-31

Last updated

2016-05-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hepatocellular Carcinoma

Keywords

Radiomics, prognosis, medical images, Hepatocellular carcinoma

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

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

Exclusion criteria

Design outcomes

Primary

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

Countries

China

Outcome results