title: Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.
abstract: 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.
  
  
  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 (≥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).
  
  
  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.
authors.affiliation: Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China E-mail : wzjxyfy@163.com, wym0577@163.com.
authors.forename: Jian
authors.initials: J
authors.lastname: Chen
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Ding
authors.forename: Qin-Shi
authors.initials: QS
authors.lastname: Pan
authors.forename: Wan-Dong
authors.initials: WD
authors.lastname: Hong
authors.forename: Gang
authors.initials: G
authors.lastname: Xu
authors.forename: Fang-You
authors.initials: FY
authors.lastname: Yu
authors.forename: Yu-Min
authors.initials: YM
authors.lastname: Wang
mesh_terms: D000230:Adenocarcinoma / Q000382:microbiology / Q000473:pathology; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D018287:Carcinoma, Large Cell / Q000382:microbiology / Q000473:pathology; D002289:Carcinoma, Non-Small-Cell Lung / Q000382:microbiology / Q000473:pathology; D002294:Carcinoma, Squamous Cell / Q000382:microbiology / Q000473:pathology; D003428:Cross Infection / Q000453:epidemiology* / Q000382:microbiology / Q000473:pathology; D005260:Female; D005500:Follow-Up Studies; D005658:Fungi / Q000302:isolation & purification*; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008175:Lung Neoplasms / Q000382:microbiology* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009181:Mycoses / Q000453:epidemiology* / Q000382:microbiology / Q000473:pathology; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D012307:Risk Factors; D055752:Small Cell Lung Carcinoma / Q000382:microbiology / Q000473:pathology