title: Solitary pulmonary nodules: clinical prediction model versus physicians.
abstract: OBJECTIVE
  To determine whether a clinical prediction model developed to identify malignant lung nodules based on clinical data and radiologic lung nodule characteristics could predict a malignant lung nodule diagnosis with higher accuracy than physicians.
  
  
  METHODS
  One hundred cases were obtained by using a stratified random sample from a retrospective cohort of 629 patients with newly discovered 4- to 30-mm radiologically indeterminate solitary pulmonary nodules (SPNs) on chest radiography. A chest radiologist, pulmonologist, thoracic surgeon, and general internist made predictions of a malignant lesion and recommendations for management (thoracotomy, transthoracic needle aspiration biopsy, or observation) on the basis of radiologic and clinical data used to develop the clinical prediction rule. The predictions of a malignant lung nodule were compared with the probability of malignant involvement from a previously validated clinical prediction model to identify malignant nodules on the basis of three clinical characteristics (age, smoking status, and history of cancer greater than or equal to 5 years previously) and three radiologic characteristics (nodule diameter, spiculation, and upper lobe location).
  
  
  RESULTS
  Receiver operating characteristic analysis showed no significant difference between the logistic model and the physicians' predictions. Calibration curves revealed that physicians overestimated the probability of a malignant lesion in patients with low risk of malignant disease by the prediction rule; this finding suggests a potential for the decision rule to improve the management of patients with SPNs that are likely to be benign.
  
  
  CONCLUSIONS
  The prediction model was not better than physicians' predictions of malignant SPNs. The prediction rule may have potential to improve the management of patients with SPNs that are likely to be benign.
authors.affiliation: Department of Diagnostic Radiology, Mayo Clinic Rochester, Minnesota 55905, USA.
authors.forename: S J
authors.initials: SJ
authors.lastname: Swensen
authors.forename: M D
authors.initials: MD
authors.lastname: Silverstein
authors.forename: E S
authors.initials: ES
authors.lastname: Edell
authors.forename: V F
authors.initials: VF
authors.lastname: Trastek
authors.forename: G L
authors.initials: GL
authors.lastname: Aughenbaugh
authors.forename: D M
authors.initials: DM
authors.lastname: Ilstrup
authors.forename: C D
authors.initials: CD
authors.lastname: Schleck
mesh_terms: D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010820:Physicians; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed