title: Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution computed tomography.
abstract: OBJECTIVE
  We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP).
  
  
  METHODS
  Thirty-three patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of 2 thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence-based guidelines for computed tomography diagnosis of UIP. Two cardiothoracic fellows with 1 year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple-detector computed tomography images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multidirectional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes.
  
  
  RESULTS
  We compared the predictions on the basis of regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the 2 fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic.
  
  
  CONCLUSIONS
  Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its associated risks.
authors.affiliation: From the *Department of Radiology, School of Medicine, Stanford University, Stanford, CA; †Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; and ‡Section on Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA.
authors.forename: Adrien
authors.initials: A
authors.lastname: Depeursinge
authors.forename: Anne S
authors.initials: AS
authors.lastname: Chin
authors.forename: Ann N
authors.initials: AN
authors.lastname: Leung
authors.forename: Donato
authors.initials: D
authors.lastname: Terrone
authors.forename: Michael
authors.initials: M
authors.lastname: Bristow
authors.forename: Glenn
authors.initials: G
authors.lastname: Rosen
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000000981:diagnostic imaging; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed