Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients

Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

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자료유형: 학술저널

저자정보: Chen, Jian (Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University) Chen, Jie (Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University) Ding, Hong-Yan (Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University) Pan, Qin-Shi (Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University) Hong, Wan-Dong (Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University) Xu, Gang (Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University) Yu, Fang-You (Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University) Wang, Yu-Min (Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제16권 제12호

발행연도: 2015.1

수록면: 5,095 - 5,099 (5page)

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Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materials and Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (${\geq}65$ years), use of antibiotics, low serum albumin concentrations (${\leq}37.18g/L$), radiotherapy, surgery, low hemoglobin hyperlipidemia (${\leq}93.67g/L$), long time of hospitalization (${\geq}14$days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model($0.829{\pm}0.019$)was higher than that of LR model ($0.756{\pm}0.021$). Conclusions: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

#Artificial neural network #ANN) #predictors #lung cancer #deep fungal infection

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