Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning—An Artificial Intelligence Concept-Reference-Cited by-同舟云学术

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning—An Artificial Intelligence Concept

Published:2018-06 Issue:6 Volume:29 Page:850-857.e1
ISSN:1051-0443
Container-title:Journal of Vascular and Interventional Radiology
language:en
Short-container-title:Journal of Vascular and Interventional Radiology

Author:

Abajian Aaron,Murali Nikitha^ORCID,Savic Lynn Jeanette,Laage-Gaupp Fabian Max,Nezami Nariman,Duncan James S.,Schlachter Todd,Lin MingDe,Geschwind Jean-François,Chapiro Julius

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine,Radiology, Nuclear Medicine and imaging

Reference18 articles.

1. NCCN clinical practice guidelines in oncology: hepatobiliary cancers;Benson;J Natl Compr Canc Netw,2009

2. Cancer statistics 2016;Siegel;CA Cancer J Clin,2016

3. EASL and mRECIST responses are independent prognostic factors for survival in hepatocellular cancer patients treated with transarterial embolization;Gillmore;J Hepatol,2011

4. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1);Eisenhauer;Eur J Cancer,2009

5. Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver;Bruix;J Hepatol,2001

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