168 related articles for article (PubMed ID: 37672075)
1. Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.
Yao H; Tian L; Liu X; Li S; Chen Y; Cao J; Zhang Z; Chen Z; Feng Z; Xu Q; Zhu J; Wang Y; Guo Y; Chen W; Li C; Li P; Wang H; Luo J
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15827-15838. PubMed ID: 37672075
[TBL] [Abstract][Full Text] [Related]
2. Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?
Hodgdon T; McInnes MD; Schieda N; Flood TA; Lamb L; Thornhill RE
Radiology; 2015 Sep; 276(3):787-96. PubMed ID: 25906183
[TBL] [Abstract][Full Text] [Related]
3. Alkaline phosphatase combines with CT factors for differentiating small (≤ 4 cm) fat-poor angiomyolipoma from renal cell carcinoma: a multiple quantitative tool.
Peng T; Fan J; Xie B; Wang Q; Chen Y; Li Y; Wu K; Feng C; Li T; Chen H; Pu X; Liu J
World J Urol; 2023 May; 41(5):1345-1351. PubMed ID: 37093317
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis.
Dehghani Firouzabadi F; Gopal N; Hasani A; Homayounieh F; Li X; Jones EC; Yazdian Anari P; Turkbey E; Malayeri AA
PLoS One; 2023; 18(7):e0287299. PubMed ID: 37498830
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
Lee H; Hong H; Kim J; Jung DC
Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
[TBL] [Abstract][Full Text] [Related]
9. The value of quantitative CT texture analysis in differentiation of angiomyolipoma without visible fat from clear cell renal cell carcinoma on four-phase contrast-enhanced CT images.
You MW; Kim N; Choi HJ
Clin Radiol; 2019 Jul; 74(7):547-554. PubMed ID: 31010583
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.
Nie P; Yang G; Wang Z; Yan L; Miao W; Hao D; Wu J; Zhao Y; Gong A; Cui J; Jia Y; Niu H
Eur Radiol; 2020 Feb; 30(2):1274-1284. PubMed ID: 31506816
[TBL] [Abstract][Full Text] [Related]
13. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.
Feng Z; Rong P; Cao P; Zhou Q; Zhu W; Yan Z; Liu Q; Wang W
Eur Radiol; 2018 Apr; 28(4):1625-1633. PubMed ID: 29134348
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Differentiation of Clear Cell Renal Cell Carcinoma From Other Subtypes and Fat-Poor Angiomyolipoma by Use of Quantitative Enhancement Measurement During Three-Phase MDCT.
Kim SH; Kim CS; Kim MJ; Cho JY; Cho SH
AJR Am J Roentgenol; 2016 Jan; 206(1):W21-8. PubMed ID: 26700359
[TBL] [Abstract][Full Text] [Related]
16. Early dark cortical band sign on CT for differentiating clear cell renal cell carcinoma from fat poor angiomyolipoma and detecting peritumoral pseudocapsule.
Ogawa Y; Morita S; Takagi T; Yoshida K; Tanabe K; Nagashima Y; Nishina Y; Sakai S
Eur Radiol; 2021 Aug; 31(8):5990-5997. PubMed ID: 33559699
[TBL] [Abstract][Full Text] [Related]
17. Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?
Ma Y; Cao F; Xu X; Ma W
Abdom Radiol (NY); 2020 Aug; 45(8):2500-2507. PubMed ID: 31980867
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. CT histogram analysis: differentiation of angiomyolipoma without visible fat from renal cell carcinoma at CT imaging.
Kim JY; Kim JK; Kim N; Cho KS
Radiology; 2008 Feb; 246(2):472-9. PubMed ID: 18094264
[TBL] [Abstract][Full Text] [Related]
20. Use of Iodine Concentration in the Lipid-Poor Portion of the Renal Mass for Differentiation of Angiomyolipoma from Renal Cell Carcinoma.
Sun J; Zhang XY; Li XT; Li YL; Wang ZL
Cancer Biother Radiopharm; 2019 May; 34(4):224-230. PubMed ID: 31070481
[No Abstract] [Full Text] [Related]
[Next] [New Search]
