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 r...
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.
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.
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문제 정의
There are no reports concerning the ANN model was used to predict fungal infections and relative dangerous factors of patients with lung cancer at home and abroad. The aim of this work was to develop an ANN to predict deep fungal infection in lung cancer.
제안 방법
Sigmoid transfer functions were performed in the hidden and output layers. Gradient descent was performed to estimate the synaptic weights. In light of the results of the univariate and sensitivity analyses, a final threelayer feed-forward ANN model with a back propagation algorithm was constructed for all 696 patients.
Gradient descent was performed to estimate the synaptic weights. In light of the results of the univariate and sensitivity analyses, a final threelayer feed-forward ANN model with a back propagation algorithm was constructed for all 696 patients. The ANN model was trained with a maximum of 500 iterations and 10 tours.
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 (smoking index=number of cigarettes smoked per day× smoking years), time of hospitalization.
06, respectively. The receiver operating characteristic (ROC) and area under curve (AUC) were performed to evaluate the performance of the ANN model and LR model. Differences were taken into account statistically significant if the two-tailed P value was less than 0.
대상 데이터
Specimens include 454 cases sputum, 121 cases blood, and 121 urine sample. The culture and identification of fungi were used with Kemaijia chromogenic medium (France, Meraux Corporation).
The 696 patients with lung cancer came from the First Affiliated Hospital of Wenzhou Medical University, China, from January April 2005 to December 2014. The above cases were confirmed by the histopathological results.
An exploratory three-layer multiplayer perceptron (MLP) ANN model with a back propagation algorithm was constructed for independent variable importance analysis. The data were randomly divided into a training sample (557 cases, 80%) and a test sample (139 cases, 20%) in the exploratory ANN model. Sigmoid transfer functions were performed in the hidden and output layers.
As manifested in Figure 1, age, and use of antibiotics, serum albumin, radiotherapy, surgery, hemoglobin and time of hospitalization were the most crucial predictors of nosocomial infections by sensitivity analysis (the exploratory ANN model constructed for the sensitivity analysis is not manifested). The final three-layer 5-5-1-feed-forward back propagation ANN model with variables consisting of age, use of antibiotics, serum albumin, radiotherapy, surgery, hemoglobin and time of hospitalization was developed and trained in 696 patients (Figure 2). The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio of the ANN was 58.
The above cases were confirmed by the histopathological results. The lung cancers consisted of 536 male and 160 women, aged between 31 to 90 years and including 54 small cell lung cancer, 642 non-small cell lung cancer (consisting of 400 adenocarcinoma, 240 squamous cell carcinoma, 2 large cell carcinoma). The criterion for the histopathologic diagnosis of lung cancer was the World Health Organization (WHO)/International Study of Lung Cancer (IASLC) lung cancer histological classification standards.
데이터처리
Continuous values were expressed as the means±SD or medians and compared employing Student’s t-test or the Mann-Whitney test.
이론/모형
The lung cancers consisted of 536 male and 160 women, aged between 31 to 90 years and including 54 small cell lung cancer, 642 non-small cell lung cancer (consisting of 400 adenocarcinoma, 240 squamous cell carcinoma, 2 large cell carcinoma). The criterion for the histopathologic diagnosis of lung cancer was the World Health Organization (WHO)/International Study of Lung Cancer (IASLC) lung cancer histological classification standards. 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.
성능/효과
In conclusion, an artificial neural network model with variables consisting of age, employ of antibiotics, serum albumin concentrations, radiotherapy, surgery, hemoglobin, time of hospitalization may be useful for predicting the fungal infection in patients with lung cancer.
65% (2/121). The ratio of Candida albicans was 86.99% (194/223) in the total deep fungal, Candida glabrata accounted for 7.62% (17/223), that of Candida tropicalis was 4.94% (9/223) and that of krusei was 1.35% (3/223).
The results of this work demonstrate that older, use of antibiotics, low serum albumin concentrations, received radiotherapy, received surgery, low hemoglobin hyperlipidemia; long time of hospitalization was the most crucial predictors of nosocomial infections. According to ROC analysis, the diagnostic performance of the ANN model was superior to that of the LR model.
The final three-layer 5-5-1-feed-forward back propagation ANN model with variables consisting of age, use of antibiotics, serum albumin, radiotherapy, surgery, hemoglobin and time of hospitalization was developed and trained in 696 patients (Figure 2). The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio of the ANN was 58.56%, 80.39%, 2.99 and 0.52, respectively. The ROC curves for the ANN model and LR model for predicting nosocomial infections in lung cancer patients are manifested in Figure 3.
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