NDLI: Cancer adjuvant chemotherapy prediction model for non-small cell lung cancer

Content Provider	IET Digital Library
Author	Alanni, Russul Hou, Jingyu Azzawi, Hasseeb Xiang, Yong
Abstract	Non-small cell lung cancer (NSCLC) is the most popular and dangerous type of lung cancer. Adjuvant chemotherapy (ACT) is the main treatment after surgery resection to prevent the patient from cancer recurrence. However, ACT could be toxic and unhelpful in some cases. Therefore, it is highly desired in clinical applications to predict the treatment outcomes of chemotherapy. Conventional methods of predicting cancer treatment rely solely on histopathology and the results are not reliable in some cases. This study aims at building a predictive model to identify who needs ACT treatment and who should avoid it. To this end, the authors propose an innovative method to identify NSCLC-related prognostic genes from microarray gene-expression datasets. They also propose a new model using gene-expression programming algorithm for ACT classification. The proposed model was evaluated on integrated microarray datasets from four institutes and compared with four representative methods: general regression neural network, decision tree, support vector machine and naive Bayes. Evaluation results demonstrated the effectiveness of the proposed model with accuracy 89.8% which is higher than other representative models. They obtained four probes (four genes) that can get good prediction results. These genes are 204891_s_at (LCK), 208893_s_at (DUSP6), 202454_s_at (ERBB3) and 201076_at (MMD).
Starting Page	129
Ending Page	135
Page Count	7
ISSN	17518849
Volume Number	13
e-ISSN	17518857
Issue Number	Issue 3, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-syb/13/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-syb.2018.5060
Journal	IET Systems Biology
Publisher Date	2019-02-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ACT Classification ACT Information ACT Treatment Adjuvant Chemotherapy Bayes Method Biochemistry Biology And Medical Computing Cancer Cancer ACT Prediction Model Cancer Recurrence Cancer Treatment Cellular Biophysics Conventional Method Decision Tree Gene-expression Programming Algorithm General Regression Neural Network Genetics Integrated Microarray Datasets Knowledge Engineering Technique Lung Medical Computing Microarray Gene-expression Datasets Microarray Gene-expression Technology Naive Bayes Neural Computing Technique Neural Nets Nonsmall Cell Lung Cancer NSCLC Treatment NSCLC-related Prognostic Genes Patient Care And Treatment Physics of Subcellular Structure Regression Analysis Representative Model Support Vector Machine Surfactant Surgery Resection Survival Time
Content Type	Text
Resource Type	Article
Subject	Cell Biology Genetics Molecular Biology Modeling and Simulation Biotechnology

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