LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases
| Autor: | Nan Hu, Shi-Feng Chen, Zhen-Zhou Yang, Chuan Chen, Yong He, Ding-De Huang, Ming Huang, Yu-Hao Wu, Hualiang Xiao, Ge Wang, Kun-Lin Xiong, Zhong-Shi He, Guo-Dong Liu, Xue-Qin Yang, Dong Wang, Yan Wu |
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| Rok vydání: | 2015 |
| Předmět: |
Adult
Male Oncology China medicine.medical_specialty Pathology Lung Neoplasms Support Vector Machine medicine.drug_class medicine.medical_treatment Observational Study Article Tyrosine-kinase inhibitor Text mining Carcinoma Non-Small-Cell Lung Internal medicine medicine Humans Epidermal growth factor receptor Lung cancer Aged biology Brain Neoplasms business.industry Discriminant Analysis General Medicine Middle Aged Models Theoretical Protein-Tyrosine Kinases medicine.disease respiratory tract diseases ErbB Receptors Radiation therapy Mutation (genetic algorithm) ComputingMethodologies_DOCUMENTANDTEXTPROCESSING biology.protein Female Observational study business Brain metastasis |
| Zdroj: | Medicine |
| ISSN: | 0025-7974 |
| DOI: | 10.1097/md.0000000000000375 |
| Popis: | Supplemental Digital Content is available in the text 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. |
| Databáze: | OpenAIRE |
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