title: CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.
abstract: OBJECTIVE
  Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model.
  
  
  METHODS
  CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases.
  
  
  RESULTS
  The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%).
  
  
  CONCLUSIONS
  CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.
authors.affiliation: Department of Radiology, JHOC 3251, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21287. Electronic address: sraman3@gmail.com.
authors.forename: Siva P
authors.initials: SP
authors.lastname: Raman
authors.affiliation: Department of Radiology, JHOC 3251, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21287.
authors.forename: Yifei
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Radiology, JHOC 3251, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21287.
authors.forename: James L
authors.initials: JL
authors.lastname: Schroeder
authors.affiliation: Biostatistics and Bioinformatics Division, Department of Oncology, Johns Hopkins University, Baltimore, Maryland.
authors.forename: Peng
authors.initials: P
authors.lastname: Huang
authors.affiliation: Department of Radiology, JHOC 3251, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21287.
authors.forename: Elliot K
authors.initials: EK
authors.lastname: Fishman
mesh_terms: D018249:Adenoma, Oxyphilic / Q000000981:diagnostic imaging*; D000368:Aged; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007472:Iohexol; D052177:Kidney Diseases, Cystic / Q000000981:diagnostic imaging*; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014283:Triiodobenzoic Acids
chemical_list: D003287:Contrast Media; D014283:Triiodobenzoic Acids; D007472:Iohexol; C044834:iodixanol
keywords: Texture analysis; clear cell renal cell carcinoma; multidetector computed tomography; oncocytoma; papillary renal cell carcinoma