180 related articles for article (PubMed ID: 34935652)
1. Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database.
Toda N; Hashimoto M; Arita Y; Haque H; Akita H; Akashi T; Gobara H; Nishie A; Yakami M; Nakamoto A; Watadani T; Oya M; Jinzaki M
Invest Radiol; 2022 May; 57(5):327-333. PubMed ID: 34935652
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
5. Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.
Zhou T; Guan J; Feng B; Xue H; Cui J; Kuang Q; Chen Y; Xu K; Lin F; Cui E; Long W
Eur Radiol; 2023 Jun; 33(6):4323-4332. PubMed ID: 36645455
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.
Kocak B; Durmaz ES; Ates E; Ulusan MB
AJR Am J Roentgenol; 2019 Mar; 212(3):W55-W63. PubMed ID: 30601030
[TBL] [Abstract][Full Text] [Related]
8. Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.
Dai C; Xiong Y; Zhu P; Yao L; Lin J; Yao J; Zhang X; Huang R; Wang R; Hou J; Wang K; Shi Z; Chen F; Guo J; Zeng M; Zhou J; Wang S
Radiology; 2024 May; 311(2):e232178. PubMed ID: 38742970
[TBL] [Abstract][Full Text] [Related]
9. Differential diagnosis and prognosis of small renal masses: association with collateral vessels detected using contrast-enhanced computed tomography.
Yanagi M; Kiriyama T; Akatsuka J; Endo Y; Takeda H; Katsu A; Honda Y; Suzuki K; Nishikawa Y; Ikuma S; Mikami H; Toyama Y; Kimura G; Kondo Y
BMC Cancer; 2022 Aug; 22(1):856. PubMed ID: 35932010
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.
Lin F; Ma C; Xu J; Lei Y; Li Q; Lan Y; Sun M; Long W; Cui E
Eur J Radiol; 2020 Aug; 129():109079. PubMed ID: 32526669
[TBL] [Abstract][Full Text] [Related]
12. Clinico-radio-pathologic features of a solitary solid renal mass at MDCT examination.
Kim EY; Park BK; Kim CK; Lee HM
Acta Radiol; 2010 Dec; 51(10):1143-8. PubMed ID: 20849320
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Small renal masses (≤ 4 cm): differentiation of oncocytoma from renal clear cell carcinoma using ratio of lesion to cortex attenuation and aorta-lesion attenuation difference (ALAD) on contrast-enhanced CT.
Gentili F; Bronico I; Maestroni U; Ziglioli F; Silini EM; Buti S; de Filippo M
Radiol Med; 2020 Dec; 125(12):1280-1287. PubMed ID: 32385827
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Small (< 4 cm) Renal Mass: Differentiation of Oncocytoma From Renal Cell Carcinoma on Biphasic Contrast-Enhanced CT.
Sasaguri K; Takahashi N; Gomez-Cardona D; Leng S; Schmit GD; Carter RE; Leibovich BC; Kawashima A
AJR Am J Roentgenol; 2015 Nov; 205(5):999-1007. PubMed ID: 26496547
[TBL] [Abstract][Full Text] [Related]
17. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.
Cui E; Li Z; Ma C; Li Q; Lei Y; Lan Y; Yu J; Zhou Z; Li R; Long W; Lin F
Eur Radiol; 2020 May; 30(5):2912-2921. PubMed ID: 32002635
[TBL] [Abstract][Full Text] [Related]
18. Renal cell carcinoma: value of multiphase MDCT with multiplanar reformations in the detection of pseudocapsule.
Tsili AC; Argyropoulou MI; Gousia A; Kalef-Ezra J; Sofikitis N; Malamou-Mitsi V; Tsampoulas K
AJR Am J Roentgenol; 2012 Aug; 199(2):379-86. PubMed ID: 22826400
[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. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma.
Yang H; Wu K; Liu H; Wu P; Yuan Y; Wang L; Liu Y; Zeng H; Li J; Liu W; Wu S
Eur Radiol; 2023 Nov; 33(11):7532-7541. PubMed ID: 37289245
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]