Title
Predicting risk of radiation-induced lung injury Predicting risk of radiation-induced lung injury
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Hagerstown, Md :Lippincott Williams & Wilkins ,
Subject
Human medicine
Source (journal)
Journal of thoracic oncology / International Association for the Study of Lung Cancer [Aurora, Colo.] - Hagerstown, Md, 2006, currens
Volume/pages
2(2007) :9 , p. 864-874
ISSN
1556-0864
ISI
000249470800016
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Radiation-induced lung injury (RILI) is the most common, dose-limiting complication of thoracic radio- and radiochemotherapy. Unfortunately, predicting which patients will suffer from this complication is extremely difficult. Ideally, individual phenotype- and genotype-based risk profiles should be able to identify patients who are resistant to RILI and who could benefit from dose escalation in chemoradiotherapy. This could result in better local control and overall survival. We review the risk predictors that are currently in clinical use-dosimetric parameters of radiotherapy such as normal tissue complication probability, mean lung dose, V20 and V30-as well as biomarkers that might individualize risk profiles. These biomarkers comprise a variety of proinflammatory and profibrotic cytokines and molecules including transforming growth factor beta 1 that are implicated in development and persistence of RILI. Dosimetric parameters of radiotherapy show a low negative predictive value of 60% to 80%. Depending on the studied molecule, negative predictive value of biomarkers is approximately 50%. The predictive power of biomarkers might be increased if they are coupled with radiogenomics, e.g., genotyping analysis of single nucleotide polymorphisms in transforming growth factor beta 1, transforming growth factor beta 1 pathway genes, and other cytokines. Genetic variability and the complexity of RILI and its underlying molecular mechanisms make identification of biological risk predictors challenging. Further investigations are needed to develop more effective risk predictors of RILI.
E-info
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