title: CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT.
abstract: The purpose of the paper is to evaluate if CT pixel distribution and texture analysis can identify fat in angiomyolipoma (AML) on unenhanced CT.
  
  
  
  Thirty-seven patients with 38 AMLs and 75 patients with 83 renal cell carcinomas (RCCs) were evaluated. Region of interest (ROI) was manually placed over renal mass on unenhanced CT. In-house software generated multiple overlapping small-ROIs of various sizes within whole-lesion-ROI. Maximal number of pixels under cutoff attenuation values in the multiple small-ROIs was calculated. Skewness of CT attenuation histogram was calculated from whole-lesion-ROI. Presence of fat in renal mass was also evaluated subjectively. Performance of subjective evaluation and objective methods for identifying fat was compared using McNemar test.
  
  
  
  Macroscopic fat was identified in 15/38 AMLs and 1/83 RCCs by both subjective evaluation and by CT negative pixel distribution analysis (p = 1.0). Optimal threshold was ≥6 pixels below -30 HU within 13-pixel-ROI. Skewness of < -0.4 in whole-lesion-ROI identified fat in 10/38 AMLs and 0/83 RCCs. By combining CT negative pixel distribution analysis and skewness, fat was identified in 20/38 AMLs and 1/83 RCCs, but the difference to the subjective method was not statistically significant (p = 0.07).
  
  
  
  CT negative attenuation pixel distribution analysis does not identify fat in AML beyond subjective evaluation. Addition of skewness by texture analysis may help improve identifying fat in AML.
authors.affiliation: Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. takahashi.naoki@mayo.edu.
authors.forename: Naoki
authors.initials: N
authors.lastname: Takahashi
authors.affiliation: Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
authors.forename: Mitsuru
authors.initials: M
authors.lastname: Takeuchi
authors.affiliation: Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
authors.forename: Kohei
authors.initials: K
authors.lastname: Sasaguri
authors.affiliation: Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
authors.forename: Shuai
authors.initials: S
authors.lastname: Leng
authors.affiliation: Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Froemming
authors.affiliation: Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
authors.forename: Akira
authors.initials: A
authors.lastname: Kawashima
mesh_terms: D018249:Adenoma, Oxyphilic / Q000000981:diagnostic imaging*; D000368:Aged; 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; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Angiomyolipoma; Computed tomography; Kidney; Renal cell carcinoma