Work

How Will Big Data Improve Clinical and Basic Research in Radiation Therapy?

Year: 2015

Type: article

Source: International Journal of Radiation Oncology*Biology*Physics

DOI: 10.1016/j.ijrobp.2015.11.009

Authors: Barry S. Rosenstein, Jacek Capala, Jason A. Efstathiou, Jeff Hammerbacher, Sarah L. Kerns, Feng-Ming (Spring) Kong, Harry Ostrer, Fred W. Prior, Bhadrasain Vikram, John Wong, Ying Xiao

Institutions: New York University, Icahn School of Medicine at Mount Sinai, National Institutes of Health, National Cancer Institute, Harvard University, Massachusetts General Hospital, University of Rochester Medicine, Augusta University, Albert Einstein College of Medicine, University of Arkansas for Medical Sciences, Johns Hopkins University, Johns Hopkins Medicine, University of Pennsylvania

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: U.S. Department of Defense, American Cancer Society, National Institutes of Health, National Cancer Institute

Awards: G1291646684, G1837597684, G2501855449, G2554729840, G316704787, G386607036, Resources for development and validation of Radiomic analyses & Adaptive Therapy, NRG Oncology Network Group Operations Center, G434901823, G5888954722, Risk Prediction for Development of Adverse Effects FOllowing Radiotherapy for Prostate Cancer, G76262943, G8135351443, G9000033067, G9000102198, G9000198389, G9000206809

OpenAlex ID: W2177954057

FWCI: 4.688

Cites: 35

Cited by: 30

Related to: 10