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LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study

Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non–small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with...

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Autores principales: Hu, Nan, Wang, Ge, Wu, Yu-Hao, Chen, Shi-Feng, Liu, Guo-Dong, Chen, Chuan, Wang, Dong, He, Zhong-Shi, Yang, Xue-Qin, He, Yong, Xiao, Hua-Liang, Huang, Ding-De, Xiong, Kun-Lin, Wu, Yan, Huang, Ming, Yang, Zhen-Zhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4602717/
https://www.ncbi.nlm.nih.gov/pubmed/25654374
http://dx.doi.org/10.1097/MD.0000000000000375
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author Hu, Nan
Wang, Ge
Wu, Yu-Hao
Chen, Shi-Feng
Liu, Guo-Dong
Chen, Chuan
Wang, Dong
He, Zhong-Shi
Yang, Xue-Qin
He, Yong
Xiao, Hua-Liang
Huang, Ding-De
Xiong, Kun-Lin
Wu, Yan
Huang, Ming
Yang, Zhen-Zhou
author_facet Hu, Nan
Wang, Ge
Wu, Yu-Hao
Chen, Shi-Feng
Liu, Guo-Dong
Chen, Chuan
Wang, Dong
He, Zhong-Shi
Yang, Xue-Qin
He, Yong
Xiao, Hua-Liang
Huang, Ding-De
Xiong, Kun-Lin
Wu, Yan
Huang, Ming
Yang, Zhen-Zhou
author_sort Hu, Nan
collection PubMed
description Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non–small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this small cohort of patients with NSCLC. Further studies with larger cohorts should be carried out to validate our findings in the clinical setting.
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spelling pubmed-46027172015-10-27 LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study Hu, Nan Wang, Ge Wu, Yu-Hao Chen, Shi-Feng Liu, Guo-Dong Chen, Chuan Wang, Dong He, Zhong-Shi Yang, Xue-Qin He, Yong Xiao, Hua-Liang Huang, Ding-De Xiong, Kun-Lin Wu, Yan Huang, Ming Yang, Zhen-Zhou Medicine (Baltimore) 5700 Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non–small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this small cohort of patients with NSCLC. Further studies with larger cohorts should be carried out to validate our findings in the clinical setting. Wolters Kluwer Health 2015-02-06 /pmc/articles/PMC4602717/ /pubmed/25654374 http://dx.doi.org/10.1097/MD.0000000000000375 Text en Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0, where it is permissible to download, share and reproduce the work in any medium, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0
spellingShingle 5700
Hu, Nan
Wang, Ge
Wu, Yu-Hao
Chen, Shi-Feng
Liu, Guo-Dong
Chen, Chuan
Wang, Dong
He, Zhong-Shi
Yang, Xue-Qin
He, Yong
Xiao, Hua-Liang
Huang, Ding-De
Xiong, Kun-Lin
Wu, Yan
Huang, Ming
Yang, Zhen-Zhou
LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study
title LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study
title_full LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study
title_fullStr LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study
title_full_unstemmed LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study
title_short LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases: An Observational Study
title_sort lda-svm-based egfr mutation model for nsclc brain metastases: an observational study
topic 5700
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4602717/
https://www.ncbi.nlm.nih.gov/pubmed/25654374
http://dx.doi.org/10.1097/MD.0000000000000375
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