title: Establishing assistant diagnosis models of solitary pulmonary nodules based on intelligent algorithms.
abstract: OBJECTIVE
  This study aimed to establish an auxiliary diagnosis model for solitary pulmonary nodules (SPNs) and evaluate its test efficacy.
  
  
  METHODS
  Three hundred thirty-two pathologically diagnosed SPN patients (186 malignant, 146 benign) were collected as subjects. The serum levels of 8 types of markers and 9 computed tomography (CT) imaging features of each patient were treated as independent variables. The corresponding pathological classification results (fungal inflammation, tuberculosis and tuberculoma, inflammatory pseudotumor, tumor middle differentiation, cancer) of quantized patients were treated as dependent variables. A 17-to-1 mathematical auxiliary SPN diagnosis model was established using a back propagation (BP) algorithm and a support vector machine (SVM) algorithm. A 40-case test set was used to estimate the effect.
  
  
  RESULTS
  Two different auxiliary SPN diagnosis models were successfully established. The diagnostic accuracy, sensitivity and specificity of the BP algorithm diagnosis model were 60%, 68% and 46.7%, respectively, and those of the SVM algorithm model were 80%, 85.7% and 66.7%, respectively.
  
  
  CONCLUSIONS
  The accuracy, sensitivity and specificity of the SVM diagnostic model were relatively high, indicating that the model has important reference value for determining the degree of SPN differentiation and is suitable for the auxiliary diagnosis of benign and malignant SPN.
authors.affiliation: Department of Oncology, The First Hospital Affiliated to Henan University, Kaifeng, China.
authors.forename: Zhijun
authors.initials: Z
authors.lastname: Zhao
authors.forename: Jingtao
authors.initials: J
authors.lastname: Chen
authors.forename: Xiaoxiang
authors.initials: X
authors.lastname: Yin
authors.forename: Huayong
authors.initials: H
authors.lastname: Song
authors.forename: Xinchun
authors.initials: X
authors.lastname: Wang
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D002454:Cell Differentiation / Q000502:physiology; D005260:Female; D006801:Humans; D007249:Inflammation / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology; D008175:Lung Neoplasms / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology*; D014057:Tomography, X-Ray Computed / Q000379:methods; D014376:Tuberculosis / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology; D055815:Young Adult
chemical_list: D015415:Biomarkers