312 related articles for article (PubMed ID: 31822969)
1. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.
Haji-Momenian S; Lin Z; Patel B; Law N; Michalak A; Nayak A; Earls J; Loew M
Abdom Radiol (NY); 2020 Mar; 45(3):789-798. PubMed ID: 31822969
[TBL] [Abstract][Full Text] [Related]
2. Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study.
Haji-Momenian S; Ricker R; Chen Z; Houser M; Adusumilli N; Yang M; Toubaji A; Loew M
Abdom Radiol (NY); 2021 Sep; 46(9):4266-4277. PubMed ID: 33813624
[TBL] [Abstract][Full Text] [Related]
3. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
Shu J; Wen D; Xi Y; Xia Y; Cai Z; Xu W; Meng X; Liu B; Yin H
Eur J Radiol; 2019 Dec; 121():108738. PubMed ID: 31756634
[TBL] [Abstract][Full Text] [Related]
4. CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma.
Feng Z; Shen Q; Li Y; Hu Z
Cancer Imaging; 2019 Feb; 19(1):6. PubMed ID: 30728073
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study.
Goyal A; Razik A; Kandasamy D; Seth A; Das P; Ganeshan B; Sharma R
Abdom Radiol (NY); 2019 Oct; 44(10):3336-3349. PubMed ID: 31300850
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Texture analysis as a radiomic marker for differentiating renal tumors.
Yu H; Scalera J; Khalid M; Touret AS; Bloch N; Li B; Qureshi MM; Soto JA; Anderson SW
Abdom Radiol (NY); 2017 Oct; 42(10):2470-2478. PubMed ID: 28421244
[TBL] [Abstract][Full Text] [Related]
10. Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers.
Scrima AT; Lubner MG; Abel EJ; Havighurst TC; Shapiro DD; Huang W; Pickhardt PJ
Abdom Radiol (NY); 2019 Jun; 44(6):1999-2008. PubMed ID: 29804215
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.
Kocak B; Durmaz ES; Kaya OK; Kilickesmez O
Acta Radiol; 2020 Jun; 61(6):856-864. PubMed ID: 31635476
[TBL] [Abstract][Full Text] [Related]
14. CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes.
Lubner MG; Stabo N; Abel EJ; Del Rio AM; Pickhardt PJ
AJR Am J Roentgenol; 2016 Jul; 207(1):96-105. PubMed ID: 27145377
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma.
Ding J; Xing Z; Jiang Z; Chen J; Pan L; Qiu J; Xing W
Eur J Radiol; 2018 Jun; 103():51-56. PubMed ID: 29803385
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Juxtatumoral perinephric fat analysis in clear cell renal cell carcinoma.
Gill TS; Varghese BA; Hwang DH; Cen SY; Aron M; Aron M; Duddalwar VA
Abdom Radiol (NY); 2019 Apr; 44(4):1470-1480. PubMed ID: 30506142
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
