Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data
Joint Authors
El Naqa, Issam
Spencer, Sarah J.
Almiron Bonnin, Damian
Deasy, Joseph O.
Bradley, Jeffrey D.
Source
Journal of Biomedicine and Biotechnology
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-05-28
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Radiotherapy outcomes are determined by complex interactions between physical and biological factors, reflecting both treatment conditions and underlying genetics.
Recent advances in radiotherapy and biotechnology provide new opportunities and challenges for predicting radiation-induced toxicities, particularly radiation pneumonitis (RP), in lung cancer patients.
In this work, we utilize datamining methods based on machine learning to build a predictive model of lung injury by retrospective analysis of treatment planning archives.
In addition, biomarkers for this model are extracted from a prospective clinical trial that collects blood serum samples at multiple time points.
We utilize a 3-way proteomics methodology to screen for differentially expressed proteins that are related to RP.
Our preliminary results demonstrate that kernel methods can capture nonlinear dose-volume interactions, but fail to address missing biological factors.
Our proteomics strategy yielded promising protein candidates, but their role in RP as well as their interactions with dose-volume metrics remain to be determined.
American Psychological Association (APA)
Spencer, Sarah J.& Almiron Bonnin, Damian& Deasy, Joseph O.& Bradley, Jeffrey D.& El Naqa, Issam. 2009. Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data. Journal of Biomedicine and Biotechnology،Vol. 2009, no. 2009, pp.1-14.
https://search.emarefa.net/detail/BIM-505964
Modern Language Association (MLA)
Spencer, Sarah J.…[et al.]. Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data. Journal of Biomedicine and Biotechnology No. 2009 (2009), pp.1-14.
https://search.emarefa.net/detail/BIM-505964
American Medical Association (AMA)
Spencer, Sarah J.& Almiron Bonnin, Damian& Deasy, Joseph O.& Bradley, Jeffrey D.& El Naqa, Issam. Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data. Journal of Biomedicine and Biotechnology. 2009. Vol. 2009, no. 2009, pp.1-14.
https://search.emarefa.net/detail/BIM-505964
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-505964