title: Noninvasive risk stratification of lung adenocarcinoma using quantitative computed tomography.
abstract: BACKGROUND
  Lung cancer remains the leading cause of cancer-related deaths in the United States and worldwide. Adenocarcinoma is the most common type of lung cancer and encompasses lesions with widely variable clinical outcomes. In the absence of noninvasive risk stratification, individualized patient management remains challenging. Consequently a subgroup of pulmonary nodules of the lung adenocarcinoma spectrum is likely treated more aggressively than necessary.
  
  
  METHODS
  Consecutive patients with surgically resected pulmonary nodules of the lung adenocarcinoma spectrum (lesion size ≤3 cm, 2006-2009) and available presurgical high-resolution computed tomography (HRCT) imaging were identified at Mayo Clinic Rochester. All cases were classified using an unbiased Computer-Aided Nodule Assessment and Risk Yield (CANARY) approach based on the quantification of presurgical HRCT characteristics. CANARY-based classification was independently correlated to postsurgical progression-free survival.
  
  
  RESULTS
  CANARY analysis of 264 consecutive patients identified three distinct subgroups. Independent comparisons of 5-year disease-free survival (DFS) between these subgroups demonstrated statistically significant differences in 5-year DFS, 100%, 72.7%, and 51.4%, respectively (p = 0.0005).
  
  
  CONCLUSIONS
  Noninvasive CANARY-based risk stratification identifies subgroups of patients with pulmonary nodules of the adenocarcinoma spectrum characterized by distinct clinical outcomes. This technique may ultimately improve the current expert opinion-based approach to the management of these lesions by facilitating individualized patient management.
authors.affiliation: *Department of Physiology and Biomedical Engineering, †Department of Radiology, and ‡Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN.
authors.forename: Sushravya
authors.initials: S
authors.lastname: Raghunath
authors.forename: Fabien
authors.initials: F
authors.lastname: Maldonado
authors.forename: Srinivasan
authors.initials: S
authors.lastname: Rajagopalan
authors.forename: Ronald A
authors.initials: RA
authors.lastname: Karwoski
authors.forename: Zackary S
authors.initials: ZS
authors.lastname: DePew
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Bartholmai
authors.forename: Tobias
authors.initials: T
authors.lastname: Peikert
authors.forename: Richard A
authors.initials: RA
authors.lastname: Robb
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis* / Q000473:pathology; D000077192:Adenocarcinoma of Lung; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D018570:Risk Assessment; D016019:Survival Analysis; D014057:Tomography, X-Ray Computed / Q000379:methods*