Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer

Yen Chen Chen, Yo-Cheng Chang, Wan Chi Ke, Hung Wen Chiu

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Purpose: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable for ACT (ACT-benefit) and who should avoid ACT (ACT-futile) in NSCLC. Methods: We identified the ACT correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification. Reliability was assessed by cross-data set validation. Results: We obtained 2 probes (2 genes) with T-stage clinical data combination can get good prediction result. These genes included 208893_s_at (DUSP6) and 204891_s_at (LCK). The 10-fold cross validation classification accuracy was 65.71%. The best result of ANN models is MLP14-8-2 with logistic activation function. Conclusions: Using gene signature profiles to predict ACT benefit in NSCLC is feasible. The key to this analysis was identifying the pertinent genes and classification. This study maybe helps reduce the ineffective medical practices to avoid the waste of medical resources.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalJournal of Biomedical Informatics
Volume56
DOIs
Publication statusPublished - Aug 1 2015

Fingerprint

Chemotherapy
Adjuvant Chemotherapy
Gene expression
Non-Small Cell Lung Carcinoma
Cells
Neural networks
Gene Expression
Genes
Neoplasms
Medical Waste
Surgery
Logistics
Chemical activation
Neoplasm Metastasis
Recurrence

Keywords

  • Adjuvant chemotherapy
  • Gene expression
  • Lung cancer
  • Machine learning
  • Microarray
  • Neural network
  • Outcome prediction
  • Survival analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this

Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data : An example for non-small cell lung cancer. / Chen, Yen Chen; Chang, Yo-Cheng; Ke, Wan Chi; Chiu, Hung Wen.

In: Journal of Biomedical Informatics, Vol. 56, 01.08.2015, p. 1-7.

Research output: Contribution to journalArticle

@article{f4fa9dba096b452bb5a2aa21c2736180,
title = "Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer",
abstract = "Purpose: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable for ACT (ACT-benefit) and who should avoid ACT (ACT-futile) in NSCLC. Methods: We identified the ACT correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification. Reliability was assessed by cross-data set validation. Results: We obtained 2 probes (2 genes) with T-stage clinical data combination can get good prediction result. These genes included 208893_s_at (DUSP6) and 204891_s_at (LCK). The 10-fold cross validation classification accuracy was 65.71{\%}. The best result of ANN models is MLP14-8-2 with logistic activation function. Conclusions: Using gene signature profiles to predict ACT benefit in NSCLC is feasible. The key to this analysis was identifying the pertinent genes and classification. This study maybe helps reduce the ineffective medical practices to avoid the waste of medical resources.",
keywords = "Adjuvant chemotherapy, Gene expression, Lung cancer, Machine learning, Microarray, Neural network, Outcome prediction, Survival analysis",
author = "Chen, {Yen Chen} and Yo-Cheng Chang and Ke, {Wan Chi} and Chiu, {Hung Wen}",
year = "2015",
month = "8",
day = "1",
doi = "10.1016/j.jbi.2015.05.006",
language = "English",
volume = "56",
pages = "1--7",
journal = "Journal of Biomedical Informatics",
issn = "1532-0464",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data

T2 - An example for non-small cell lung cancer

AU - Chen, Yen Chen

AU - Chang, Yo-Cheng

AU - Ke, Wan Chi

AU - Chiu, Hung Wen

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Purpose: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable for ACT (ACT-benefit) and who should avoid ACT (ACT-futile) in NSCLC. Methods: We identified the ACT correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification. Reliability was assessed by cross-data set validation. Results: We obtained 2 probes (2 genes) with T-stage clinical data combination can get good prediction result. These genes included 208893_s_at (DUSP6) and 204891_s_at (LCK). The 10-fold cross validation classification accuracy was 65.71%. The best result of ANN models is MLP14-8-2 with logistic activation function. Conclusions: Using gene signature profiles to predict ACT benefit in NSCLC is feasible. The key to this analysis was identifying the pertinent genes and classification. This study maybe helps reduce the ineffective medical practices to avoid the waste of medical resources.

AB - Purpose: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable for ACT (ACT-benefit) and who should avoid ACT (ACT-futile) in NSCLC. Methods: We identified the ACT correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification. Reliability was assessed by cross-data set validation. Results: We obtained 2 probes (2 genes) with T-stage clinical data combination can get good prediction result. These genes included 208893_s_at (DUSP6) and 204891_s_at (LCK). The 10-fold cross validation classification accuracy was 65.71%. The best result of ANN models is MLP14-8-2 with logistic activation function. Conclusions: Using gene signature profiles to predict ACT benefit in NSCLC is feasible. The key to this analysis was identifying the pertinent genes and classification. This study maybe helps reduce the ineffective medical practices to avoid the waste of medical resources.

KW - Adjuvant chemotherapy

KW - Gene expression

KW - Lung cancer

KW - Machine learning

KW - Microarray

KW - Neural network

KW - Outcome prediction

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=84938574067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938574067&partnerID=8YFLogxK

U2 - 10.1016/j.jbi.2015.05.006

DO - 10.1016/j.jbi.2015.05.006

M3 - Article

C2 - 25998519

AN - SCOPUS:84938574067

VL - 56

SP - 1

EP - 7

JO - Journal of Biomedical Informatics

JF - Journal of Biomedical Informatics

SN - 1532-0464

ER -