title: Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules.
abstract: BACKGROUND
  Texture analysis is a computer tool that enables quantification of gray-level patterns, pixel interrelationships, and spectral properties of an image. It can enhance visual methods of image analysis. Primary lung cancer and granulomatous nodules have identical CT imaging features. The purpose of this study was to assess the sensitivity and specificity of CT texture analysis in differentiating lung cancer and granulomas.
  
  
  METHODS
  This retrospective study evaluated 55 patients with primary lung cancer and granulomatous nodules who had contrast-enhanced (CE) and/or non-contrast-enhanced (NCE) CT within 3 months of biopsy. Textural features were extracted from 61 nodules. Mann-Whitney U tests were used to compare values for nodules. Receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with histopathology as outcome. Combinations of features were entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.
  
  
  RESULTS
  The model generated by sum of squares, sum difference, and sum entropy features for NCE CT yielded 88% sensitivity and 92% specificity (AUC =0.90±0.06, P<0.0001). For nodules with fluorodeoxyglucose positron emission tomography (FDG-PET)/CT, sensitivity for detection of lung cancer was 79.2% (CI: 57.8-92.9%), specificity was 38.5% (CI: 13.9-68.4%) and accuracy was 64.8%.
  
  
  CONCLUSIONS
  Quantitative CT texture analysis has the potential to differentiate primary lung cancer and granulomatous lesions.
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Carole
authors.initials: C
authors.lastname: Dennie
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Thornhill
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Vineeta
authors.initials: V
authors.lastname: Sethi-Virmani
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Carolina A
authors.initials: CA
authors.lastname: Souza
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Hamid
authors.initials: H
authors.lastname: Bayanati
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Ashish
authors.initials: A
authors.lastname: Gupta
authors.affiliation: 1 Department of Medical Imaging, 2 Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
authors.forename: Donna
authors.initials: D
authors.lastname: Maziak
keywords: Applied imaging technology; chest imaging; nuclear imaging; oncologic imaging