Radiomics of Hepatocellular Carcinoma

Hepatocellular Carcinoma Clinical Trial

Official title:

Quantitative Imaging for Evaluation of Response to Cancer Therapies

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT02757846
• Other study ID #: 20160427ABCD
• Status: Enrolling by invitation
• Phase: N/A
• First received: April 28, 2016
• Last updated: April 28, 2016
• Start date: April 2017
• Est. completion date: March 2022

Study information

• Verified date: April 2016
• Source: Chinese Academy of Sciences
• Contact: n/a
• Is FDA regulated: No
• Health authority: China: Food and Drug Administration
• Study type: Observational

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 1200
• Est. completion date: March 2022
• Est. primary completion date: March 2022
• Accepts healthy volunteers: No
• Gender: Both
• Age group: N/A and older

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

Study Design

Observational Model: Cohort, Time Perspective: Retrospective

Related Conditions & MeSH terms

• Carcinoma
• Carcinoma, Hepatocellular
• Hepatocellular Carcinoma

Locations

Country	Name	City	State
China	Key Laboratory of Molecular Imaging, Chinese Academy of Sciences	Beijing	Beijing

Sponsors (6)

Lead Sponsor	Collaborator
Chinese Academy of Sciences	Eastern Hepatobiliary Surgery Hospital, Guangdong General Hospital, Henan Province People's Hospital, Peking Union Medical College Hospital, West China Hospital

Country where clinical trial is conducted

China, 

References & Publications (5)

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. Review. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	quantitative image features extracted from CT images can be used as imaging marker for prognosis		five(year)	No

External Links

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