IMAGING

Angiomyolipoma with minimal fat on MDCT: can counts of negative-attenuation pixels aid diagnosis?
Simpfendorfer C, Herts BR, Motta-Ramirez GA, Lockwood DS, Zhou M, Leiber M, Remer EM
Section of Abdominal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
AJR Am J Roentgenol. 2009; 192: 438-43

• Objective: The purpose of this study was to determine whether counts of pixels with subzero attenuation on CT scans can aid in the diagnosis of renal angiomyolipoma with minimal fat.
• Materials and Methods: Of 33 angiomyolipomas identified among 719 renal masses resected from 702 patients over 4 years, 15 masses in 15 patients were prospectively diagnosed on the basis of the presence of fat at MDCT. The 18 patients with minimal-fat angiomyolipoma and a matched (age, sex, tumor size) cohort of patients with renal cell carcinoma were included in this study. Three radiologists independently counted the number of pixels with attenuation less than -10, -20, and -30 HU. Receiver operating characteristic analysis of the number of pixels at each cutoff was used to calculate sensitivity, specificity, and positive predictive value with the following criteria: 1, more than 10 pixels less than -20 HU; 2, more than 20 pixels less than -20 HU; 3, more than 5 pixels less than -30 HU.
• Results: Using criterion 1, reader A identified six angiomyolipomas; reader B, five; and reader C, two. The combined sensitivity was 24%; specificity, 98%; and positive predictive value, 69%. Using criterion 2, reader A identified three angiomyolipomas; reader B, four; and reader C, two. The combined sensitivity was 17%; specificity, 100%; and positive predictive value, 100%. Using criterion 3, reader A identified four angiomyolipomas; reader B, four; and reader C, two. The combined sensitivity was 18%; specificity, 100%; and positive predictive value, 100%.
• Conclusion: CT findings of more than 20 pixels with attenuation less than -20 HU and more than 5 pixels with attenuation less than -30 HU have a positive predictive value of 100% in detection of angiomyolipoma, but most angiomyolipomas with minimal fat cannot be reliably identified on the basis of an absolute pixel count.
  
  
• Editorial Comment
  Adequate preoperative imaging characterization of small angiomyolipoma (AML) is essential since 3-7% of suspicious renal masses resected are found to be AML. AML is characterized by the presence of variable amount of fat within a renal mass. From the practical point of view (evidence based medicine), all renal mass containing fat are considered AML. The use of thin-section (2-5 mm) unenhanced CT is the best method for detecting even small amounts of fat. Previous reports have been shown that if fat within a mass is not visually obvious, pixel mapping can be performed, which may reveal the fat as clustered pixels with negative CT numbers (defined as at least 3 adjacent pixels with attenuation -20 HU) (1). The drawbacks of these previous reports are lack of pathologic confirmation and absence of a control group. The authors of this manuscript found that in a study with pathologic correlation the CT findings of more than 20 pixels with attenuation less than -20 HU and more than 5 pixels with attenuation less than -30 HU have a positive predictive value of 100% in detection of angiomyolipoma. These AMLs presented at pathologic examination more than 10% of fat.
  AMLs containing less than 10% of fat at pathologic examination could not be characterized on the basis of an absolute pixel count. Perhaps, for the sake of clarity, we should call AMLs with minimal fat those with tiny amount of visible fat and those in which only CT pixel mapping is able to demonstrate negative attenuation. AMLs with less than 10% of fat should be called AMLs without radiologic evidence of fat. The latter category is indistinguishable from renal cell carcinoma and for this reason, imaging guided percutaneous biopsy is indicated.

Reference
1. Takahashi K, Honda M, Okubo RS, Hyodo H, Takakusaki H, Yokoyama H, et al.: CT pixel mapping in the diagnosis of small angiomyolipomas of the kidneys. J Comput Assist Tomogr. 1993; 17: 98-101.

Dr. Adilson Prando
Chief, Department of Radiology and
Diagnostic Imaging, Vera Cruz Hospital
Campinas, São Paulo, Brazil
E-mail: adilson.prando@gmail.com