Abstract
Non-small cell lung cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Nevertheless, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. To address this problem, we used frequent pattern mining to establish the relationships of patient characteristics and tumor response in advanced NSCLC. Univariate analysis determined that smoking status, histology, epidermal growth factor receptor (EGFR) mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR tyrosine kinase inhibitors. Overall, the highest classification accuracy was 76.56% and the area under the curve was 0.76. The decision tree used a combination of EGFR mutations, histology, and smoking status to predict tumor response and the output was both easily understandable and in keeping with current knowledge. Our findings suggest that support vector machines and decision trees are a promising approach for clinical decision support in the patient selection for targeted therapy in advanced NSCLC.
Original language | English |
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Article number | 6974996 |
Pages (from-to) | 424-431 |
Number of pages | 8 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 20 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2016 |
Bibliographical note
Funding Information:This work was supported in part by NSERC Discovery Grant and CIHR Catalyst Grant.
Publisher Copyright:
© 2014 IEEE.
ASJC Scopus Subject Areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management