Abstract
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. 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. 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 (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). 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.
Highlights
The mortality of lung cancer mortality is the highest in all tumors and its incidence is gradually growing (Moore et al, 2010)
It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67g /L), long time of hospitalization (≥14days) were apt to deep fungal infection and the artificial neural network (ANN) model consisted of the seven factors
TNM clinical stage was in the light of TNM clinical stage from American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC) in 2002.The following information was collected for each patient on admission: age, sex, clinical stage, histological classification, invasive procedures, mechanical ventilation, surgery, radiotherapy, chemotherapy, hemoglobin, serum albumin, white blood cell count, use of antibiotics, use of hormone, non-neoplastic lung disease, concurrent diabetes or renal insufficiency, smoking, time of hospitalization
Summary
The mortality of lung cancer mortality is the highest in all tumors and its incidence is gradually growing (Moore et al, 2010). 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. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67g /L), long time of hospitalization (≥14days) were apt to deep fungal infection and the ANN model consisted of the seven factors. 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
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