254 related articles for article (PubMed ID: 28726505)
1. 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]
2. Ten uncommon and unusual variants of renal angiomyolipoma (AML): radiologic-pathologic correlation.
Schieda N; Kielar AZ; Al Dandan O; McInnes MD; Flood TA
Clin Radiol; 2015 Feb; 70(2):206-20. PubMed ID: 25468637
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. [Renal angiomyolipomas without fat component: tomodensitometric and histologic characteristics, clinical course].
Negre T; Faure A; Andre M; Daniel L; Coulange C; Lechevallier E
Prog Urol; 2011 Nov; 21(12):837-41. PubMed ID: 22035909
[TBL] [Abstract][Full Text] [Related]
6. 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.
Yang R; Wu J; Sun L; Lai S; Xu Y; Liu X; Ma Y; Zhen X
Eur Radiol; 2020 Feb; 30(2):1254-1263. PubMed ID: 31468159
[TBL] [Abstract][Full Text] [Related]
7. New radiologic classification of renal angiomyolipoma: frequently asked questions.
Park SG; Park BK
Clin Imaging; 2019; 55():156-160. PubMed ID: 30825810
[TBL] [Abstract][Full Text] [Related]
8. Renal Angiomyolipoma Based on New Classification: How to Differentiate It From Renal Cell Carcinoma.
Park BK
AJR Am J Roentgenol; 2019 Mar; 212(3):582-588. PubMed ID: 30620673
[TBL] [Abstract][Full Text] [Related]
9. Angiomyolipoma with minimal fat: differentiation of morphological and enhancement features from renal cell carcinoma at CT imaging.
Sung CK; Kim SH; Woo S; Moon MH; Kim SY; Kim SH; Cho JY
Acta Radiol; 2016 Sep; 57(9):1114-22. PubMed ID: 26663389
[TBL] [Abstract][Full Text] [Related]
10. Fat poor angiomyolipoma differentiation from renal cell carcinoma at 320-slice dynamic volume CT perfusion.
Chen C; Kang Q; Xu B; Shi Z; Guo H; Wei Q; Lu Y; Wu X
Abdom Radiol (NY); 2018 May; 43(5):1223-1230. PubMed ID: 28828638
[TBL] [Abstract][Full Text] [Related]
11. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
12. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images.
Yan L; Liu Z; Wang G; Huang Y; Liu Y; Yu Y; Liang C
Acad Radiol; 2015 Sep; 22(9):1115-21. PubMed ID: 26031228
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Renal angiomyolipoma: preoperative identification of atypical fat-poor AML.
Farrell C; Noyes SL; Tourojman M; Lane BR
Curr Urol Rep; 2015 Mar; 16(3):12. PubMed ID: 25677233
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Renal angiomyolipoma with minimal fat.
Hosokawa Y; Kinouchi T; Sawai Y; Mano M; Kiuchi H; Meguro N; Maeda O; Kuroda M; Usami M
Int J Clin Oncol; 2002 Apr; 7(2):120-3. PubMed ID: 12018109
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Are there useful CT features to differentiate renal cell carcinoma from lipid-poor renal angiomyolipoma?
Yang CW; Shen SH; Chang YH; Chung HJ; Wang JH; Lin AT; Chen KK
AJR Am J Roentgenol; 2013 Nov; 201(5):1017-28. PubMed ID: 24147472
[TBL] [Abstract][Full Text] [Related]
20. Renal cell carcinoma containing fat mimicking angiomyolipoma: demonstration with CT scan and histopathology.
Hammadeh MY; Thomas K; Philp T; Singh M
Eur Radiol; 1998; 8(2):228-9. PubMed ID: 9477270
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
