302 related articles for article (PubMed ID: 32803417)
1. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.
Yap FY; Varghese BA; Cen SY; Hwang DH; Lei X; Desai B; Lau C; Yang LL; Fullenkamp AJ; Hajian S; Rivas M; Gupta MN; Quinn BD; Aron M; Desai MM; Aron M; Oberai AA; Gill IS; Duddalwar VA
Eur Radiol; 2021 Feb; 31(2):1011-1021. PubMed ID: 32803417
[TBL] [Abstract][Full Text] [Related]
2. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
Erdim C; Yardimci AH; Bektas CT; Kocak B; Koca SB; Demir H; Kilickesmez O
Acad Radiol; 2020 Oct; 27(10):1422-1429. PubMed ID: 32014404
[TBL] [Abstract][Full Text] [Related]
3. 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]
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. 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]
6. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.
Demirjian NL; Varghese BA; Cen SY; Hwang DH; Aron M; Siddiqui I; Fields BKK; Lei X; Yap FY; Rivas M; Reddy SS; Zahoor H; Liu DH; Desai M; Rhie SK; Gill IS; Duddalwar V
Eur Radiol; 2022 Apr; 32(4):2552-2563. PubMed ID: 34757449
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach.
Uhlig J; Biggemann L; Nietert MM; Beißbarth T; Lotz J; Kim HS; Trojan L; Uhlig A
Medicine (Baltimore); 2020 Apr; 99(16):e19725. PubMed ID: 32311963
[TBL] [Abstract][Full Text] [Related]
9. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.
Luo S; Wei R; Lu S; Lai S; Wu J; Wu Z; Pang X; Wei X; Jiang X; Zhen X; Yang R
Eur Radiol; 2022 Apr; 32(4):2340-2350. PubMed ID: 34636962
[TBL] [Abstract][Full Text] [Related]
10. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.
Wentland AL; Yamashita R; Kino A; Pandit P; Shen L; Brooke Jeffrey R; Rubin D; Kamaya A
Abdom Radiol (NY); 2023 Feb; 48(2):642-648. PubMed ID: 36370180
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.
Nguyen K; Schieda N; James N; McInnes MDF; Wu M; Thornhill RE
Eur Radiol; 2021 Mar; 31(3):1676-1686. PubMed ID: 32914197
[TBL] [Abstract][Full Text] [Related]
13. Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT.
Schieda N; Nguyen K; Thornhill RE; McInnes MDF; Wu M; James N
Abdom Radiol (NY); 2020 Sep; 45(9):2786-2796. PubMed ID: 32627049
[TBL] [Abstract][Full Text] [Related]
14. An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.
Yang H; Liu H; Lin J; Xiao H; Guo Y; Mei H; Ding Q; Yuan Y; Lai X; Wu K; Wu S
Eur Radiol; 2024 Jan; 34(1):355-366. PubMed ID: 37528301
[TBL] [Abstract][Full Text] [Related]
15. Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms.
Miskin N; Qin L; Silverman SG; Shinagare AB
J Comput Assist Tomogr; 2023 May-Jun 01; 47(3):376-381. PubMed ID: 37184999
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Nazari M; Shiri I; Hajianfar G; Oveisi N; Abdollahi H; Deevband MR; Oveisi M; Zaidi H
Radiol Med; 2020 Aug; 125(8):754-762. PubMed ID: 32193870
[TBL] [Abstract][Full Text] [Related]
18. A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.
Yang L; Gao L; Arefan D; Tan Y; Dan H; Zhang J
BMC Med Imaging; 2022 Jan; 22(1):15. PubMed ID: 35094674
[TBL] [Abstract][Full Text] [Related]
19. Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.
Varghese BA; Chen F; Hwang DH; Cen SY; Desai B; Gill IS; Duddalwar VA
AJR Am J Roentgenol; 2018 Dec; 211(6):W288-W296. PubMed ID: 30240299
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]