2016
Nelofar Kureshi; Syed Sibte Raza Abidi; Christian Blouin
A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer. Journal Article
In: IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 1, pp. 424-431, 2016, ISSN: 2168-2208.
Abstract | Links | BibTeX | Tags: Cancer, Data Mining, Decision Trees, Health Data Analytics, Personalized Medicine, Prediction Model
@article{Kureshi:IeeeJBiomedHealthInform:2014,
title = {A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer.},
author = {Nelofar Kureshi and Syed Sibte Raza Abidi and Christian Blouin},
url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6974996&isnumber=7369993},
doi = {10.1109/JBHI.2014.2377517},
issn = {2168-2208},
year = {2016},
date = {2016-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {20},
number = {1},
pages = {424-431},
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, EGFR mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR-TKIs. Overall, the highest classification accuracy was 76.56% and the AUC 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},
keywords = {Cancer, Data Mining, Decision Trees, Health Data Analytics, Personalized Medicine, Prediction Model},
pubstate = {published},
tppubtype = {article}
}
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, EGFR mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR-TKIs. Overall, the highest classification accuracy was 76.56% and the AUC 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