Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation

Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation

Background and Purpose: Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to ide...

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Bibliographic Details
Main Authors: José Marcio Luna, Hann-Hsiang Chao, Russel T. Shinohara, Lyle H. Ungar, Keith A. Cengel, Daniel A. Pryma, Chidambaram Chinniah, Abigail T. Berman, Sharyn I. Katz, Despina Kontos, Charles B. Simone, II, Eric S. Diffenderfer
Format: Article
Language:English
Published: Elsevier 2020-05-01
Series:Clinical and Translational Radiation Oncology
Online Access:http://www.sciencedirect.com/science/article/pii/S2405630820300203

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http://www.sciencedirect.com/science/article/pii/S2405630820300203

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