661 related articles for article (PubMed ID: 29134348)
1. 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]
2. 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]
3. 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]
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. 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]
6. MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis.
Schieda N; Dilauro M; Moosavi B; Hodgdon T; Cron GO; McInnes MD; Flood TA
Eur Radiol; 2016 Jul; 26(7):2242-51. PubMed ID: 26486936
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
Kocak B; Yardimci AH; Bektas CT; Turkcanoglu MH; Erdim C; Yucetas U; Koca SB; Kilickesmez O
Eur J Radiol; 2018 Oct; 107():149-157. PubMed ID: 30292260
[TBL] [Abstract][Full Text] [Related]
9. [A discrimination model for differentiation of renal cell carcinoma from renal angiomyolipoma without visible fat: based on hierarchical fusion framework of multi-classifier].
Mo T; Wu Y; Yang R; Zhen X
Nan Fang Yi Ke Da Xue Xue Bao; 2022 Aug; 42(8):1174-1181. PubMed ID: 36073216
[TBL] [Abstract][Full Text] [Related]
10. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.
Bektas CT; Kocak B; Yardimci AH; Turkcanoglu MH; Yucetas U; Koca SB; Erdim C; Kilickesmez O
Eur Radiol; 2019 Mar; 29(3):1153-1163. PubMed ID: 30167812
[TBL] [Abstract][Full Text] [Related]
11. Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features.
Cui EM; Lin F; Li Q; Li RG; Chen XM; Liu ZS; Long WS
Acta Radiol; 2019 Nov; 60(11):1543-1552. PubMed ID: 30799634
[No Abstract] [Full Text] [Related]
12. Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma.
Tang Z; Yu D; Ni T; Zhao T; Jin Y; Dong E
AJR Am J Roentgenol; 2020 Feb; 214(2):370-382. PubMed ID: 31799870
[No Abstract] [Full Text] [Related]
13. Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.
Kocak B; Ates E; Durmaz ES; Ulusan MB; Kilickesmez O
Eur Radiol; 2019 Sep; 29(9):4765-4775. PubMed ID: 30747300
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT.
Leng S; Takahashi N; Gomez Cardona D; Kitajima K; McCollough B; Li Z; Kawashima A; Leibovich BC; McCollough CH
Abdom Radiol (NY); 2017 May; 42(5):1485-1492. PubMed ID: 28025654
[TBL] [Abstract][Full Text] [Related]
16. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma.
Zhang Y; Li X; Lv Y; Gu X
Tomography; 2020 Dec; 6(4):325-332. PubMed ID: 33364422
[TBL] [Abstract][Full Text] [Related]
17. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma.
Lin F; Cui EM; Lei Y; Luo LP
Abdom Radiol (NY); 2019 Jul; 44(7):2528-2534. PubMed ID: 30919041
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Zabihollahy F; Schieda N; Krishna S; Ukwatta E
Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
