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

BioMed Research International

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

Medicine

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. BioMed Research International،Vol. 2009, no. 2009, pp.1-14.
https://search.emarefa.net/detail/BIM-988467

Modern Language Association (MLA)

Spencer, Sarah J.…[et al.]. Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data. BioMed Research International No. 2009 (2009), pp.1-14.
https://search.emarefa.net/detail/BIM-988467

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. BioMed Research International. 2009. Vol. 2009, no. 2009, pp.1-14.
https://search.emarefa.net/detail/BIM-988467

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-988467