Literature DB >> 16890650

Fat poor renal angiomyolipoma: patient, computerized tomography and histological findings.

John Milner1, Brian McNeil, Joe Alioto, Kevin Proud, Tara Rubinas, Maria Picken, Terrence Demos, Thomas Turk, Kent T Perry.   

Abstract

PURPOSE: We reviewed our experience with fat poor cases of angiomyolipoma.
MATERIALS AND METHODS: The records of patients with angiomyolipoma, as determined by pathological study, from 1998 to 2004 were reviewed by recording patient demographics and outcomes. Fat poor cases were defined as the failure of imaging to demonstrate fat in a lesion. Computerized tomography and histological characteristics were assessed.
RESULTS: Histologically confirmed angiomyolipoma was found in 15 patients. Multiple lesions were found in 3 of 15 cases (20%). Of these 15 patients who underwent surgery 11 (73%) had unsuspected angiomyolipoma due to absent fat on computerized tomography and they underwent intervention for presumed renal cell carcinoma. Mean age +/- SD in this group was 54 +/- 15 years and 8 of 11 patients (73%) were female, of whom 4 (50%) had uterine fibroids. These lesions were found incidentally in 7 of 11 cases (64%). Operative complications developed in 2 of 11 patients (18%). Average maximal diameter on pathological evaluation was 3.2 +/- 1.3 cm (range 1.5 to 6). Nonenhanced computerized tomography was available in 7 of 11 cases, of which 3 of 7 (42%) showed hyperdense lesions and 4 of 7 (57%) showed isodense lesions. The percent of fat identified per high power field was less than 25% in 12 of 13 fat poor angiomyolipoma lesions (92%) compared to 2 of 4 classic lesions (50%) known to be angiomyolipoma before surgery (p = 0.04).
CONCLUSIONS: We suggest that a general definition of fat poor angiomyolipoma should be the failure of imaging to reveal fat within a lesion, thus, making it unsuspected at surgery. A pathological definition should be less than 25% fat per high power field, which to our knowledge is a formerly undefined quantity. Not all cases are hyperdense on nonenhanced computerized tomography. These lesions cannot be reliably identified by imaging and they should be managed like all enhancing renal masses.

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Mesh:

Year:  2006        PMID: 16890650     DOI: 10.1016/j.juro.2006.04.016

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  29 in total

Review 1.  Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI?

Authors:  Robert S Lim; Trevor A Flood; Matthew D F McInnes; Luke T Lavallee; Nicola Schieda
Journal:  Eur Radiol       Date:  2017-08-04       Impact factor: 5.315

2.  MR classification of renal masses with pathologic correlation.

Authors:  Ivan Pedrosa; Mary T Chou; Long Ngo; Ronaldo H Baroni; Elizabeth M Genega; Laura Galaburda; William C DeWolf; Neil M Rofsky
Journal:  Eur Radiol       Date:  2007-09-26       Impact factor: 5.315

3.  Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT.

Authors:  Bino A Varghese; Frank Chen; Darryl H Hwang; Steven Y Cen; Inderbir S Gill; Vinay A Duddalwar
Journal:  Br J Radiol       Date:  2018-06-21       Impact factor: 3.039

4.  Angiomyolipoma (AML) without visible fat: Ultrasound, CT and MR imaging features with pathological correlation.

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

5.  Discordance about the use of the term minimal fat angiomyolipoma.

Authors:  Teresa Pusiol; Irene Piscioli; Alice Morini; Ivan Pedrosa; Neil M Rofsky
Journal:  Radiology       Date:  2013-05       Impact factor: 11.105

Review 6.  Review of renal cell carcinoma and its common subtypes in radiology.

Authors:  Gavin Low; Guan Huang; Winnie Fu; Zaahir Moloo; Safwat Girgis
Journal:  World J Radiol       Date:  2016-05-28

Review 7.  Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis.

Authors:  Ling-Shan Chen; Zheng-Qiu Zhu; Zhi-Tao Wang; Jing Li; Li-Feng Liang; Ji-Yang Jin; Zhong-Qiu Wang
Journal:  Eur Radiol       Date:  2017-11-24       Impact factor: 5.315

8.  Commentary.

Authors:  Leonardo Oliveira Reis; Emerson Luís Zani
Journal:  Urol Ann       Date:  2012-05

Review 9.  Imaging of Solid Renal Masses.

Authors:  Fernando U Kay; Ivan Pedrosa
Journal:  Urol Clin North Am       Date:  2018-06-15       Impact factor: 2.241

10.  Routinely performed multiparametric magnetic resonance imaging helps to differentiate common subtypes of renal tumours.

Authors:  F Cornelis; E Tricaud; A S Lasserre; F Petitpierre; J C Bernhard; Y Le Bras; M Yacoub; M Bouzgarrou; A Ravaud; N Grenier
Journal:  Eur Radiol       Date:  2014-02-21       Impact factor: 5.315

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