title: Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements--a work in progress.
abstract: OBJECTIVE
  Computed tomography texture analysis (CTTA) is a method of quantifying lesion heterogeneity based on distribution of pixel intensities within a region of interest. This study investigates the ability of CTTA to distinguish different hypervascular liver lesions and compares CTTA parameters by creating a proof-of-concept model to distinguish between different lesions.
  
  
  METHODS
  Following institutional review board approval, CTTA software (TexRAD Ltd) was used to retrospectively analyze 17 cases of focal nodular hyperplasia, 19 hepatic adenomas, 25 hepatocellular carcinomas, and 19 cases of normal liver parenchyma using arterial phase scans. Two radiologists read the same image series used by the CTTA software and reported their best guess diagnosis. Computed tomography texture analysis parameters were computed from regions of interest using spatial band-pass filters to quantify heterogeneity. Random-forest method was used to construct a predictive model from these parameters, and a separate regression model was created using a subset of parameters.
  
  
  RESULTS
  The random-forest model successfully distinguished the 3 lesion types and normal liver with predicted classification performance accuracy for 91.2% for adenoma, 94.4% for focal nodular hyperplasia, and 98.6% for hepatocellular carcinoma. This error prediction was generated using a subset of data points not used in generation of the model, but not on discrete prospective cases. In contrast, the 2 human readers using the same image series data analyzed by the CTTA software had lower accuracies, of 72.2% and 65.6%, respectively. The explicit regression model with a subset of image parameters had intermediate overall accuracy of 84.9%.
  
  
  CONCLUSIONS
  Computed tomography texture analysis may prove valuable in lesion characterization. Differentiation between common hypervascular lesion types could be aided by the judicious incorporation of texture parameters into clinical analysis.
authors.affiliation: From the *Department of Radiology, Johns Hopkins University, †School of Medicine, and ‡Department of Oncology Biostatistics, Johns Hopkins School of Medicine, Baltimore, MD.
authors.forename: Siva P
authors.initials: SP
authors.lastname: Raman
authors.forename: James L
authors.initials: JL
authors.lastname: Schroeder
authors.forename: Peng
authors.initials: P
authors.lastname: Huang
authors.forename: Yifei
authors.initials: Y
authors.lastname: Chen
authors.forename: Stephanie F
authors.initials: SF
authors.lastname: Coquia
authors.forename: Satomi
authors.initials: S
authors.lastname: Kawamoto
authors.forename: Elliot K
authors.initials: EK
authors.lastname: Fishman
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000150:complications / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009389:Neovascularization, Pathologic / Q000150:complications / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*