Radiomics of Hepatocellular Carcinoma

NCT ID: NCT02757846

Last Updated: 2016-05-02

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

1200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2017-04-30

Study Completion Date

2022-03-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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

See the medical conditions and disease areas that this research is targeting or investigating.

Hepatocellular Carcinoma

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

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

Exclusion Criteria

\-
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Eastern Hepatobiliary Surgery Hospital

OTHER

Sponsor Role collaborator

Guangdong Provincial People's Hospital

OTHER

Sponsor Role collaborator

Henan Provincial People's Hospital

OTHER

Sponsor Role collaborator

West China Hospital

OTHER

Sponsor Role collaborator

Peking Union Medical College Hospital

OTHER

Sponsor Role collaborator

Chinese Academy of Sciences

OTHER_GOV

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Chongwei Chi, Ph.D

Quantitative Imaging for Evaluation of Response to Cancer Therapies

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

di dong, PhD

Role: STUDY_DIRECTOR

Chinese Academy of Sciences

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Key Laboratory of Molecular Imaging, Chinese Academy of Sciences

Beijing, Beijing Municipality, China

Site Status

Countries

Review the countries where the study has at least one active or historical site.

China

References

Explore related publications, articles, or registry entries linked to this study.

Shen W, Zhou M, Yang F, Yang C, Tian J. Multi-scale Convolutional Neural Networks for Lung Nodule Classification. Inf Process Med Imaging. 2015;24:588-99. doi: 10.1007/978-3-319-19992-4_46.

Reference Type BACKGROUND
PMID: 26221705 (View on PubMed)

Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010 Jun 15;26(12):1572-3. doi: 10.1093/bioinformatics/btq170. Epub 2010 Apr 28.

Reference Type BACKGROUND
PMID: 20427518 (View on PubMed)

Sanson M, Marie Y, Paris S, Idbaih A, Laffaire J, Ducray F, El Hallani S, Boisselier B, Mokhtari K, Hoang-Xuan K, Delattre JY. Isocitrate dehydrogenase 1 codon 132 mutation is an important prognostic biomarker in gliomas. J Clin Oncol. 2009 Sep 1;27(25):4150-4. doi: 10.1200/JCO.2009.21.9832. Epub 2009 Jul 27.

Reference Type BACKGROUND
PMID: 19636000 (View on PubMed)

Cui Y, Jia J. Update on epidemiology of hepatitis B and C in China. J Gastroenterol Hepatol. 2013 Aug;28 Suppl 1:7-10. doi: 10.1111/jgh.12220.

Reference Type BACKGROUND
PMID: 23855289 (View on PubMed)

Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J, Han B, Zhang P, Chen Y. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res. 2012 Jan;40(Database issue):D1128-36. doi: 10.1093/nar/gkr797. Epub 2011 Sep 24.

Reference Type BACKGROUND
PMID: 21948793 (View on PubMed)

Related Links

Access external resources that provide additional context or updates about the study.

http://www.mitk.net/

A Website from Key Laboratory of Molecular Imaging, Chinese Academy of Sciences

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

20160427ABCD

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.