title: Angiomyolipoma with minimal fat and non-clear cell renal cell carcinoma: differentiation on MDCT using classification and regression tree analysis-based algorithm.
abstract: BACKGROUND
  Differentiation between angiomyolipoma with minimal fat (AMLmf) and non-clear cell renal cell carcinoma (nccRCC) may be difficult owing to lack of macroscopic fat in AMLmf. However, the differential points between AMLmf and nccRCC has not been well established in the literature.
  
  
  OBJECTIVE
  To evaluate quantitative triphasic multidetector computed tomography (MDCT) features that differentiate between small AMLmf and nccRCC, and to integrate them to develop a simple and easy diagnostic algorithm.
  
  
  METHODS
  This study was approved by the Institutional Review Board; informed consent was waived. Triphasic MDCT images of pathologically-proven AMLmfs (n = 24) and nccRCCs (n = 55) of 79 patients were retrospectively evaluated. Age, sex, size, long-to-short axis ratio (LSR), attenuation and enhancement degree in all phases, unenhanced tumor-kidney attenuation difference (UTKAD) in Hounsfield units (HU) were compared with Chi-square analysis, independent-samples t-test, and receiver-operating characteristic (ROC) curves. A criterion was formulated with classification and regression tree analysis (CART). Thereafter, CART-based algorithm was tested with additional interpretations from two radiologists. Intra- and inter-observer variability was analyzed with Bland-Altman analysis.
  
  
  RESULTS
  LSR was greater in AMLmf than nccRCC (P < 0.001). AMLmf showed higher attenuation (all phases), CMP enhancement, and wash-out than nccRCC (P ≤ 0.001). UTKAD was greater in AMLmf than nccRCC (P < 0.001). ROC curve analysis yielded area under the curves of 0.936, 0.888, and 0.853 using UTKAD, unenhanced attenuation, and LSR. CART-based algorithm (UTKAD > 7.5 HU, LSR > 1.23) predicted AMLmf with sensitivity, specificity, PPV, and NPV of 87.5%, 96.4%, 91.3%, and 94.6%. Mean intra- and inter-observer difference was -0.1/0.03 HU and -1.0/0.09 HU for UTKAD/LSR, respectively. These interpretations changed the final diagnosis in 1.3% (1/79) and 5.1% (4/79) patients for radiologists 1 and 2.
  
  
  CONCLUSIONS
  Triphasic MDCT was useful for differentiating AMLmf and nccRCC. CART-based algorithm using UTKAD > 7.5 and LSR > 1.23 was simple and accurate in predicting AMLmf.
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
authors.forename: Sungmin
authors.initials: S
authors.lastname: Woo
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea Institute of Radiation Medicine and Kidney Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea radjycho@snu.ac.kr.
authors.forename: Jeong Yeon
authors.initials: JY
authors.lastname: Cho
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea Institute of Radiation Medicine and Kidney Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea.
authors.forename: Seung Hyup
authors.initials: SH
authors.lastname: Kim
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
authors.forename: Sang Youn
authors.initials: SY
authors.lastname: Kim
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging; D000328:Adult; D000368:Aged; D000465:Algorithms; D018207:Angiomyolipoma / Q000000981:diagnostic imaging*; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging*; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007472:Iohexol / Q000031:analogs & derivatives; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography / Q000379:methods*; D015588:Observer Variation; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D007472:Iohexol; C038192:iopromide
keywords: CT; Kidney; angiomyolipoma with minimal fat; classification and regression tree analysis; neoplasms – primary; non-clear cell renal cell carcinoma