title: CT histogram analysis: differentiation of angiomyolipoma without visible fat from renal cell carcinoma at CT imaging.
abstract: OBJECTIVE
  To retrospectively evaluate the diagnostic performance of computed tomographic (CT) histogram analysis for differentiating angiomyolipoma (AML) without visible fat from renal cell carcinoma (RCC) at CT, by using pathologic analysis and clinical diagnosis as the reference standard.
  
  
  METHODS
  This retrospective study was approved by the institutional review board; informed consent was waived. The authors reviewed the medical records of 144 patients with pathologic confirmation of RCC or AML (105 men, 39 women; mean age, 52 years). Analysis of unenhanced CT histograms was performed on 34 AMLs without visible fat at CT and 110 size-matched RCCs. The percentages of voxels and pixels were compared in the two groups according to the CT number categories. The diagnostic performance of CT histogram analysis in differentiating AML from RCC was determined by using receiver operating characteristic (ROC) analysis.
  
  
  RESULTS
  The percentages of voxels and pixels with a CT number less than -30 HU (2.7% and 3.4% vs 0.1% and 0.0%), less than -20 HU (4.3% and 5.1% vs 0.2% and 0.1%), less than -10 HU (7.0% and 8.1% vs 0.6% and 0.4%), and less than 0 HU (12.0% and 13.9% vs 2.0% and 2.0%) were significantly greater in the AML group than in the RCC group (P < .01), respectively. The area under the ROC curve was as high as 0.706 when a pixel percentage with a CT number less than -10 HU was used as a differentiating parameter. Corresponding to the specificity of 100% for differentiating AML from RCC, the sensitivity was as high as 20% when a pixel percentage of 6% with a CT number less than -10 HU was used as a criterion.
  
  
  CONCLUSIONS
  CT histogram analysis may be useful for differentiating AML without visible fat from RCC at CT.
authors: Department of Radiology, Asan Medical Center, University of Ulsan, 388-1 Poongnap-dong, Songpa-gu, Seoul 138-736, Korea.
authors: Ji Yeon
authors: JY
authors: Kim
authors: Jeong Kon
authors: JK
authors: Kim
authors: Namkug
authors: N
authors: Kim
authors: Kyoung-Sik
authors: KS
authors: Cho
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000465:Algorithms; D018207:Angiomyolipoma / Q000000981:diagnostic imaging*; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; 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*