title: Biomarkers to help guide management of patients with pulmonary nodules.
abstract: BACKGROUND
  Indeterminate pulmonary nodules are a common radiographic finding and require further evaluation because of the concern for lung cancer.
  
  
  OBJECTIVE
  We developed an algorithm to assign patients to a low- or high-risk category for lung cancer, based on a combination of serum biomarker levels and nodule size.
  
  
  METHODS
  For the serum biomarker assay, we determined levels of carcinoembryonic antigen, α1-antitrypsin, and squamous cell carcinoma antigen. Serum data and nodule size from a training set of 509 patients with (n = 298) and without (n = 211) lung cancer were subjected to classification and regression tree and logistic regression analyses. Multiple models were developed and tested in an independent, masked validation set for their ability to categorize patients with (n = 203) or without (n = 196) lung cancer as being low- or high-risk for lung cancer.
  
  
  RESULTS
  In all models, a large percentage of individuals in the validation study with small nodules (<1 cm) were assigned to the low-risk group, and a large percentage of individuals with large nodules (≥3 cm) were assigned to the high-risk group. In the validation study, the classification and regression tree algorithm had overall sensitivity, specificity, and positive and negative predictive values for determining lung cancer of 88%, 82%, 84%, and 87%, respectively. The logistic regression model had overall sensitivity, specificity, and positive and negative predictive values of 80%, 89%, 89%, and 81%, respectively.
  
  
  CONCLUSIONS
  Integration of biomarkers with lung nodule size has the potential to help guide the management of patients with indeterminate pulmonary nodules.
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA. patz0002@mc.duke.edu
authors.forename: Edward F
authors.initials: EF
authors.lastname: Patz
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Campa
authors.forename: Elizabeth B
authors.initials: EB
authors.lastname: Gottlin
authors.forename: Priscilla R
authors.initials: PR
authors.lastname: Trotter
authors.forename: James E
authors.initials: JE
authors.lastname: Herndon
authors.forename: Don
authors.initials: D
authors.lastname: Kafader
authors.forename: Russell P
authors.initials: RP
authors.lastname: Grant
authors.forename: Marcia
authors.initials: M
authors.lastname: Eisenberg
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000951:Antigens, Neoplasm / Q000097:blood; D015415:Biomarkers / Q000097:blood*; D002272:Carcinoembryonic Antigen / Q000097:blood; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008171:Lung Diseases / Q000175:diagnosis*; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity; D015843:Serpins / Q000097:blood; D003074:Solitary Pulmonary Nodule / Q000097:blood* / Q000628:therapy*; D000515:alpha 1-Antitrypsin / Q000097:blood
chemical_list: D000951:Antigens, Neoplasm; D015415:Biomarkers; D002272:Carcinoembryonic Antigen; D015843:Serpins; D000515:alpha 1-Antitrypsin; C051072:squamous cell carcinoma-related antigen