229 related articles for article (PubMed ID: 27666625)
1. New radiologic classification of renal angiomyolipomas.
Song S; Park BK; Park JJ
Eur J Radiol; 2016 Oct; 85(10):1835-1842. PubMed ID: 27666625
[TBL] [Abstract][Full Text] [Related]
2. Unenhanced CT and MRI Parameters That Can Be Used to Reliably Predict Fat-Invisible Angiomyolipoma.
Jeong CJ; Park BK; Park JJ; Kim CK
AJR Am J Roentgenol; 2016 Feb; 206(2):340-7. PubMed ID: 26797361
[TBL] [Abstract][Full Text] [Related]
3. Sonographic Features of Small (< 4 cm) Renal Tumors With Low Signal Intensity on T2-Weighted MR Images: Differentiating Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma.
Park KJ; Kim MH; Kim JK; Cho KS
AJR Am J Roentgenol; 2018 Sep; 211(3):605-613. PubMed ID: 30040467
[TBL] [Abstract][Full Text] [Related]
4. Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT.
Kim JK; Park SY; Shon JH; Cho KS
Radiology; 2004 Mar; 230(3):677-84. PubMed ID: 14990834
[TBL] [Abstract][Full Text] [Related]
5. Computed Tomography and Magnetic Resonance Findings of Fat-Poor Angiomyolipomas.
Potretzke AM; Potretzke TA; Bauman TM; Knight BA; Park AM; Mobley JM; Figenshau RS; Siegel CL
J Endourol; 2017 Feb; 31(2):119-128. PubMed ID: 27897036
[TBL] [Abstract][Full Text] [Related]
6. CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT.
Takahashi N; Takeuchi M; Sasaguri K; Leng S; Froemming A; Kawashima A
Abdom Radiol (NY); 2016 Jun; 41(6):1142-51. PubMed ID: 27015866
[TBL] [Abstract][Full Text] [Related]
7. Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography.
Coy H; Young JR; Douek ML; Brown MS; Sayre J; Raman SS
Abdom Radiol (NY); 2017 Jul; 42(7):1919-1928. PubMed ID: 28280876
[TBL] [Abstract][Full Text] [Related]
8. Diagnosis of angiomyolipoma using computed tomography-region of interest < or =-10 HU or 4 adjacent pixels < or =-10 HU are recommended as the diagnostic thresholds.
Simpson E; Patel U
Clin Radiol; 2006 May; 61(5):410-6. PubMed ID: 16679114
[TBL] [Abstract][Full Text] [Related]
9. Small (<4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging.
Sasiwimonphan K; Takahashi N; Leibovich BC; Carter RE; Atwell TD; Kawashima A
Radiology; 2012 Apr; 263(1):160-8. PubMed ID: 22344404
[TBL] [Abstract][Full Text] [Related]
10. Intensity ratio curve analysis of small renal masses on T2-weighted magnetic resonance imaging: Differentiation of fat-poor angiomyolipoma from renal cell carcinoma.
Moriyama S; Yoshida S; Tanaka H; Tanaka H; Yokoyama M; Ishioka J; Matsuoka Y; Saito K; Kihara K; Fujii Y
Int J Urol; 2018 Jun; 25(6):554-560. PubMed ID: 29577440
[TBL] [Abstract][Full Text] [Related]
11. Angiomyolipoma with minimal fat: differentiation from papillary renal cell carcinoma by helical CT.
Zhang YY; Luo S; Liu Y; Xu RT
Clin Radiol; 2013 Apr; 68(4):365-70. PubMed ID: 23321146
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of T1-Weighted MRI to Detect Intratumoral Hemorrhage Within Papillary Renal Cell Carcinoma as a Feature Differentiating From Angiomyolipoma Without Visible Fat.
Murray CA; Quon M; McInnes MD; van der Pol CB; Hakim SW; Flood TA; Schieda N
AJR Am J Roentgenol; 2016 Sep; 207(3):585-91. PubMed ID: 27275530
[TBL] [Abstract][Full Text] [Related]
13. Pixel distribution analysis: can it be used to distinguish clear cell carcinomas from angiomyolipomas with minimal fat?
Catalano OA; Samir AE; Sahani DV; Hahn PF
Radiology; 2008 Jun; 247(3):738-46. PubMed ID: 18413886
[TBL] [Abstract][Full Text] [Related]
14. Value of T2-weighted MR imaging in differentiating low-fat renal angiomyolipomas from other renal tumors.
Choi HJ; Kim JK; Ahn H; Kim CS; Kim MH; Cho KS
Acta Radiol; 2011 Apr; 52(3):349-53. PubMed ID: 21498374
[TBL] [Abstract][Full Text] [Related]
15. Renal Angiomyolipoma: Radiologic Classification and Imaging Features According to the Amount of Fat.
Park BK
AJR Am J Roentgenol; 2017 Oct; 209(4):826-835. PubMed ID: 28726505
[TBL] [Abstract][Full Text] [Related]
16. Double-echo gradient chemical shift MR imaging fails to differentiate minimal fat renal angiomyolipomas from other homogeneous solid renal tumors.
Ferré R; Cornelis F; Verkarre V; Eiss D; Correas JM; Grenier N; Hélénon O
Eur J Radiol; 2015 Mar; 84(3):360-365. PubMed ID: 25547327
[TBL] [Abstract][Full Text] [Related]
17. Circularity Index on Contrast-Enhanced Computed Tomography Helps Distinguish Fat-Poor Angiomyolipoma from Renal Cell Carcinoma: Retrospective Analyses of Histologically Proven 257 Small Renal Tumors Less Than 4 cm.
Kang HS; Park JJ
Korean J Radiol; 2021 May; 22(5):735-741. PubMed ID: 33660463
[TBL] [Abstract][Full Text] [Related]
18. Small (< 4 cm) Renal Tumors With Predominantly Low Signal Intensity on T2-Weighted Images: Differentiation of Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma.
Park JJ; Kim CK
AJR Am J Roentgenol; 2017 Jan; 208(1):124-130. PubMed ID: 27824487
[TBL] [Abstract][Full Text] [Related]
19. Histogram analysis of small solid renal masses: differentiating minimal fat angiomyolipoma from renal cell carcinoma.
Chaudhry HS; Davenport MS; Nieman CM; Ho LM; Neville AM
AJR Am J Roentgenol; 2012 Feb; 198(2):377-83. PubMed ID: 22268181
[TBL] [Abstract][Full Text] [Related]
20. Diagnosis of renal angiomyolipoma with hounsfield unit thresholds: effect of size of region of interest and nephrographic phase imaging.
Davenport MS; Neville AM; Ellis JH; Cohan RH; Chaudhry HS; Leder RA
Radiology; 2011 Jul; 260(1):158-65. PubMed ID: 21555349
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]