Literature DB >> 22344404

Small (<4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging.

Kewalee Sasiwimonphan1, Naoki Takahashi, Bradley C Leibovich, Rickey E Carter, Thomas D Atwell, Akira Kawashima.   

Abstract

PURPOSE: To determine whether a combination of magnetic resonance (MR) parameters can help differentiate small angiomyolipomas (AMLs) without visible fat from renal cell carcinomas (RCCs).
MATERIALS AND METHODS: This HIPAA-compliant retrospective study received institutional review board approval; 69 men and 42 women (mean age, 59.7 years) with 15 AMLs without visible fat and 104 RCCs underwent MR. The development set consisted of 10 AMLs and 71 RCCs; the validation set consisted of five AMLs and 33 RCCs. T1-weighted fast spin-echo (SE), fat-suppressed T2-weighted fast SE, in- and opposed-phase gradient-echo (GRE), and fat-suppressed three-dimensional T1-weighted spoiled GRE sequences were performed before and after contrast material administration. Tumor signal intensity (SI) was measured. T1 and T2 SI ratio (ratio of tumor to renal cortex SI on T1- and T2-weighted images, respectively), SI index (SII) ([SI(in) 2 SI(opp)]/[SI(in)] × 100; SI(in) and SI(opp) are tumor SI on in- and opposed-phase images, respectively), and arterial-to-delayed enhancement ratio ([SI(art) 2 SI(pre)]/[SI(del) 2 SI(pre)]; SI(pre), SI(art), and SI(del) are tumor SI on unenhanced, arterial phase, and delayed phase three-dimensional T1-weighted spoiled GRE images, respectively) were compared. Combinations of MR parameter threshold levels were constructed from development set and validated with validation set. Sensitivity, specificity, and accuracy for differentiating between AML and RCC were calculated for combinations of MR parameter threshold levels.
RESULTS: AML had significantly higher T1 SI ratio (P = .04), lower T2 SI ratio (P = .001), higher SII (P = .02), and higher arterial-to-delayed enhancement ratio (P < .001) than RCC. Sensitivity, specificity, and accuracy for combination of T2 SI ratio less than 0.9 and ([SII greater than 20% and T1 SI ratio greater than 1.2] or arterial-to-delayed enhancement ratio greater than 1.5) were 73% (11 of 15), 99% (103 of 104), and 96% (114 of 119), respectively, for differentiating AML from RCC.
CONCLUSION: A combination of T2 SI ratio less than 0.9 and ([SII greater than 20% and T1 SI ratio greater than 1.2] or arterial-to-delayed enhancement ratio greater than 1.5) was accurate in differentiating AML from RCC. © RSNA, 2012.

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Year:  2012        PMID: 22344404     DOI: 10.1148/radiol.12111205

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  52 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.  MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis.

Authors:  Nicola Schieda; Marc Dilauro; Bardia Moosavi; Taryn Hodgdon; Gregory O Cron; Matthew D F McInnes; Trevor A Flood
Journal:  Eur Radiol       Date:  2015-10-20       Impact factor: 5.315

3.  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

Review 4.  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

5.  Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics.

Authors:  Camila Lopes Vendrami; Robert J McCarthy; Carolina Parada Villavicencio; Frank H Miller
Journal:  Abdom Radiol (NY)       Date:  2020-07-14

Review 6.  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

7.  Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology.

Authors:  Fernando U Kay; Noah E Canvasser; Yin Xi; Daniella F Pinho; Daniel N Costa; Alberto Diaz de Leon; Gaurav Khatri; John R Leyendecker; Takeshi Yokoo; Aaron H Lay; Nicholas Kavoussi; Ersin Koseoglu; Jeffrey A Cadeddu; Ivan Pedrosa
Journal:  Radiology       Date:  2018-02-01       Impact factor: 11.105

8.  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

9.  Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging.

Authors:  Brett A Johnson; Sandy Kim; Ryan L Steinberg; Alberto Diaz de Leon; Ivan Pedrosa; Jeffrey A Cadeddu
Journal:  Urol Oncol       Date:  2019-09-17       Impact factor: 3.498

Review 10.  Role of Multiparametric MR Imaging in Malignancies of the Urogenital Tract.

Authors:  Alberto Diaz de Leon; Daniel Costa; Ivan Pedrosa
Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

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