Multiomic random forest toxicity modeling of radiation esophagitis

Background and purpose: Radiation esophagitis (RE) is a major dose-limiting toxicity resulting from radiotherapy for non-small-cell lung cancer (NSCLC). Multiomic features may provide additional predictive value for high-grade RE compared to traditionally used clinical and dose-volume histogram (DVH...

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Bibliographic Details

Published in:	Physics and Imaging in Radiation Oncology
Main Authors:	Saurabh S. Nair, Ramon M. Salazar, Ting Xu, Alexandra O. Leone, Zhongxing Liao, Laurence E. Court, Joshua S. Niedzielski
Format:	Article
Language:	English
Published:	Elsevier 2025-10-01
Subjects:	Machine learning Radiomics Outcome prediction modeling Radiation esophagitis Non small cell lung cancer (NSCLC)
Online Access:	http://www.sciencedirect.com/science/article/pii/S2405631625001435