title: Predicting EGFR mutation status in lung cancer:Proposal for a scoring model using imaging and demographic characteristics.
abstract: OBJECTIVE
  To determine if a combination of CT and demographic features can predict EGFR mutation status in bronchogenic carcinoma.
  
  
  METHODS
  We reviewed demographic and CT features for patients with molecular profiling for resected non-small cell lung carcinoma. Using multivariate logistic regression, we identified features predictive of EGFR mutation. Prognostic factors identified from the logistic regression model were then used to build a more practical scoring system.
  
  
  RESULTS
  A scoring system awarding 5 points for no or minimal smoking history, 3 points for tumours with ground glass component, 3 points for airbronchograms, 2 points for absence of preoperative evidence of nodal enlargement or metastases and 1 point for doubling time of more than a year, resulted in an AUROC of 0.861. A total score of at least 8 yielded a specificity of 95 %. On multivariate analysis sex was not found to be predictor of EGFR status.
  
  
  CONCLUSIONS
  A weighted scoring system combining imaging and demographic data holds promise as a predictor of EGFR status. Further studies are necessary to determine reproducibility in other patient groups. A predictive score may help determine which patients would benefit from molecular profiling and may help inform treatment decisions when molecular profiling is not possible.
  
  
  CONCLUSIONS
  • EGFR mutation-targeted chemotherapy for bronchogenic carcinoma has a high success rate. • Mutation testing is not possible in all patients. • EGFR associations include subsolid density, slow tumour growth and minimal/no smoking history. • Demographic or imaging features alone are weak predictors of EGFR status. • A scoring system, using imaging and demographic features, is more predictive.
authors.affiliation: Department of Diagnostic Radiology, Dalhousie University, Victoria Building, room 307, 1276 South Park Street, PO BOX 9000, Halifax, Nova Scotia, B3H 2Y9, Canada. sabri.ali@gmail.com.|Halifax Infirmary, Room 3510, 1796 Summer Street, Halifax, NS, Canada. sabri.ali@gmail.com.
authors.forename: Ali
authors.identifier: http://orcid.org/0000-0002-4335-1770
authors.initials: A
authors.lastname: Sabri
authors.affiliation: Department of Diagnostic Radiology, Dalhousie University, Victoria Building, room 307, 1276 South Park Street, PO BOX 9000, Halifax, Nova Scotia, B3H 2Y9, Canada.
authors.forename: Madiha
authors.initials: M
authors.lastname: Batool
authors.affiliation: Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: Zhaolin
authors.initials: Z
authors.lastname: Xu
authors.affiliation: Department of Surgery, Dalhousie University, QEII Health Sciences center, Halifax, Nova Scotia, Canada.
authors.forename: Drew
authors.initials: D
authors.lastname: Bethune
authors.affiliation: Department of Diagnostic Radiology, Dalhousie University, Victoria Building, room 307, 1276 South Park Street, PO BOX 9000, Halifax, Nova Scotia, B3H 2Y9, Canada.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdolell
authors.affiliation: Department of Diagnostic Radiology, Dalhousie University, Victoria Building, room 307, 1276 South Park Street, PO BOX 9000, Halifax, Nova Scotia, B3H 2Y9, Canada.
authors.forename: Daria
authors.initials: D
authors.lastname: Manos
mesh_terms: D000328:Adult; D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics* / Q000473:pathology; D003710:Demography; D066246:ErbB Receptors / Q000235:genetics*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009154:Mutation / Q000235:genetics*; D015203:Reproducibility of Results; D012189:Retrospective Studies
chemical_list: D066246:ErbB Receptors
keywords: Adenocarcinoma; EGFR; Gene mutation; Lung cancer; Scoring model