3 research outputs found
Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data
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