Literature DB >> 19155407

Angiomyolipoma with minimal fat on MDCT: can counts of negative-attenuation pixels aid diagnosis?

Claus Simpfendorfer1, Brian R Herts, Gaspar A Motta-Ramirez, Daniel S Lockwood, Ming Zhou, Michael Leiber, Erick M Remer.   

Abstract

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.

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Year:  2009        PMID: 19155407     DOI: 10.2214/AJR.08.1180

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  19 in total

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Review 2.  Imaging renal cell carcinoma with ultrasonography, CT and MRI.

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Journal:  Nat Rev Urol       Date:  2010-05-18       Impact factor: 14.432

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Authors:  Shaheed W Hakim; Nicola Schieda; Taryn Hodgdon; Matthew D F McInnes; Marc Dilauro; Trevor A Flood
Journal:  Eur Radiol       Date:  2015-06-03       Impact factor: 5.315

4.  Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

Authors:  Ruimeng Yang; Jialiang Wu; Lei Sun; Shengsheng Lai; Yikai Xu; Xilong Liu; Ying Ma; Xin Zhen
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Review 5.  Solid renal masses: what the numbers tell us.

Authors:  Stella K Kang; William C Huang; Pari V Pandharipande; Hersh Chandarana
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Review 6.  Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis.

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Review 7.  CT and MRI of small renal masses.

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8.  Angiomyolipoma being surgically excised for presumed kidney carcinoma.

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Journal:  Int Urol Nephrol       Date:  2015-05-05       Impact factor: 2.370

9.  The role of quantitative measurement by acoustic radiation force impulse imaging in differentiating benign renal lesions from malignant renal tumours.

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10.  Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography.

Authors:  Pingkun Xie; Zhihui Yang; Zheng Yuan
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