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

• Published in: IET systems biology Vol. 13; no. 3; pp. 129 - 135
• Main Authors: Alanni, Russul, Hou, Jingyu, Azzawi, Hasseeb, Xiang, Yong
• Format: Journal Article
• Language: English
• Published: England The Institution of Engineering and Technology 01.06.2019
• Subjects: ACT classification; ACT information; ACT treatment; adjuvant chemotherapy; Algorithms; Bayes methods; Bayes Theorem; biochemistry; cancer; cancer ACT prediction model; cancer recurrence; cancer treatment; Carcinoma, Non-Small-Cell Lung - drug therapy; Carcinoma, Non-Small-Cell Lung - genetics; cellular biophysics; Chemotherapy, Adjuvant; conventional methods; decision tree; decision trees; Gene Expression Regulation, Neoplastic - drug effects; general regression neural network; genetics; gene‐expression programming algorithm; Humans; integrated microarray datasets; lung; Lung Neoplasms - drug therapy; Lung Neoplasms - genetics; medical computing; microarray gene‐expression datasets; microarray gene‐expression technology; Models, Statistical; naive Bayes; neural nets; nonsmall cell lung cancer; NSCLC treatment; NSCLC‐related prognostic genes; regression analysis; representative models; Research Article; support vector machine; support vector machines; surgery; surgery resection; survival time; Treatment Outcome; adjuvant chemotherapy; cancer treatment; cellular biophysics; integrated microarray datasets; regression analysis; ACT classification; representative models; NSCLC-related prognostic genes; decision tree; genetics; surgery resection; microarray gene-expression datasets; microarray gene-expression technology; NSCLC treatment; support vector machines; nonsmall cell lung cancer; cancer ACT prediction model; lung; support vector machine; survival time; conventional methods; ACT information; cancer recurrence; decision trees; biochemistry; naive Bayes; cancer; Bayes methods; general regression neural network; gene-expression programming algorithm; medical computing; neural nets; surgery; ACT treatment
• ISSN: 1751-8849; 1751-8857; 1751-8857
• DOI: 10.1049/iet-syb.2018.5060

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).