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...
Main Authors: | , , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2020-05-01
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Series: | Clinical and Translational Radiation Oncology |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2405630820300203 |