title: Pixel distribution analysis: can it be used to distinguish clear cell carcinomas from angiomyolipomas with minimal fat?
abstract: OBJECTIVE
  To retrospectively determine if pixel histogram analysis of unenhanced computed tomographic (CT) images can be used to distinguish angiomyolipomas (AMLs) with minimal fat from clear cell renal cell carcinomas (CCRCCs).
  
  
  METHODS
  The human studies committee approved this HIPAA-complaint study, with waiver of informed consent. Patients with pathologically proved AMLs lacking visible macroscopic fat at CT and patients with pathologically proved CCRCCs were included. Lesions were measured, and a histogram (number of pixels with each attenuation) was calculated electronically within a central region of interest. The percentage of pixels below the attenuation thresholds -20 HU and 10 HU was calculated in both cohorts. The unpaired Student t test was used to compare the average percentage of subthreshold pixels at each threshold. P < .05 indicated a significant difference. The number of lesions with more than the selected percentage of subthreshold pixels was calculated in both groups, and the chi(2) test was used to test the significance of differences between cohorts. The area under the receiver operating characteristic (ROC) curve was used to determine if any percentage of subthreshold pixels could be used to differentiate between the two cohorts.
  
  
  RESULTS
  There were 22 patients with pathologically proved AMLs lacking visible macroscopic fat on CT images. Tuberous sclerosis affected three of these patients. Mean maximal transverse lesion diameter was 20 mm (range, 11-38 mm). There were 28 patients in the CCRCC comparison group. Mean maximal transverse lesion diameter was 26 mm (range, 15-36 mm). Neither the Student t test (P > .2 for all thresholds <0 HU) nor the chi(2) test (P > .15 for all thresholds <0 HU) revealed a significant difference between cohorts. A lesion with more low-attenuation pixels was significantly more likely to be characterized as CCRCC than as AML with ROC curve analysis.
  
  
  CONCLUSIONS
  Once AMLs with visible fat on CT images are excluded, pixel histogram analysis cannot be used to distinguish between AMLs and CCRCCs.
authors.affiliation: Division of Abdominal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WHT 270, Boston, MA 02114, USA.
authors.forename: Onofrio A
authors.initials: OA
authors.lastname: Catalano
authors.forename: Anthony E
authors.initials: AE
authors.lastname: Samir
authors.forename: Dushyant V
authors.initials: DV
authors.lastname: Sahani
authors.forename: Peter F
authors.initials: PF
authors.lastname: Hahn
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D018207:Angiomyolipoma / Q000000981:diagnostic imaging*; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging*; D016009:Chi-Square Distribution; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*