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

Journal: Journal of biomedical informatics

Published Date: May 18, 2015

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.

Authors

Yen-Chen Chen

Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei City, Taiwan.
Yo-Cheng Chang

Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei City, Taiwan.
Wan-Chi Ke

Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei City, Taiwan.
Hung-Wen Chiu

Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei City, Taiwan. Electronic address: hwchiu@tmu.edu.tw.

Keywords

Aged Algorithms Carcinoma, Non-Small-Cell Lung Chemotherapy, Adjuvant Computational Biology Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Kaplan-Meier Estimate Lung Neoplasms Machine Learning Male Middle Aged Neoplasm Metastasis Neoplasm Recurrence, Local Neural Networks, Computer Prognosis Proportional Hazards Models Reproducibility of Results Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (25998519)

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

Abstract

Authors

Keywords

External Resources

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