title: Predicting lung cancer prior to surgical resection in patients with lung nodules.
abstract: BACKGROUND
  Existing predictive models for lung cancer focus on improving screening or referral for biopsy in general medical populations. A predictive model calibrated for use during preoperative evaluation of suspicious lung lesions is needed to reduce unnecessary operations for a benign disease. A clinical prediction model (Thoracic Research Evaluation And Treatment [TREAT]) is proposed for this purpose.
  
  
  METHODS
  We developed and internally validated a clinical prediction model for lung cancer in a prospective cohort evaluated at our institution. Best statistical practices were used to construct, evaluate, and validate the logistic regression model in the presence of missing covariate data using bootstrap and optimism corrected techniques. The TREAT model was externally validated in a retrospectively collected Veteran Affairs population. The discrimination and calibration of the model was estimated and compared with the Mayo Clinic model in both the populations.
  
  
  RESULTS
  The TREAT model was developed in 492 patients from Vanderbilt whose lung cancer prevalence was 72% and validated among 226 Veteran Affairs patients with a lung cancer prevalence of 93%. In the development cohort, the area under the receiver operating curve (AUC) and Brier score were 0.87 (95% confidence interval [CI], 0.83-0.92) and 0.12, respectively compared with the AUC 0.89 (95% CI, 0.79-0.98) and Brier score 0.13 in the validation dataset. The TREAT model had significantly higher accuracy (p < 0.001) and better calibration than the Mayo Clinic model (AUC = 0.80; 95% CI, 75-85; Brier score = 0.17).
  
  
  CONCLUSIONS
  The validated TREAT model had better diagnostic accuracy than the Mayo Clinic model in preoperative assessment of suspicious lung lesions in a population being evaluated for lung resection.
authors.affiliation: *Department of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, Tennessee; ††Department of Thoracic Surgery, §Department of Medicine, Division of Pulmonary and Critical Care Medicine, ¶Vanderbilt-Ingram Cancer Center, and **School of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; †Department of Biostatistics, Vanderbilt University, Nashville, Tennessee; and ‡Department of Critical Care Medicine, ‖Department of Radiology, and #Geriatric Research Education Clinical Center.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Deppen
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Blume
authors.forename: Melinda C
authors.initials: MC
authors.lastname: Aldrich
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Fletcher
authors.forename: Pierre P
authors.initials: PP
authors.lastname: Massion
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Walker
authors.forename: Heidi C
authors.initials: HC
authors.lastname: Chen
authors.forename: Theodore
authors.initials: T
authors.lastname: Speroff
authors.forename: Catherine A
authors.initials: CA
authors.lastname: Degesys
authors.forename: Rhonda
authors.initials: R
authors.lastname: Pinkerman
authors.forename: Eric S
authors.initials: ES
authors.lastname: Lambright
authors.forename: Jonathan C
authors.initials: JC
authors.lastname: Nesbitt
authors.forename: Joe B
authors.initials: JB
authors.lastname: Putnam
authors.forename: Eric L
authors.initials: EL
authors.lastname: Grogan
mesh_terms: D000368:Aged; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors