BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

115 related articles for article (PubMed ID: 38512539)

  • 1. Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.
    Bai Y; An ZC; Li F; Du LF; Xie TW; Zhang XP; Cai YY
    World J Urol; 2024 Mar; 42(1):184. PubMed ID: 38512539
    [TBL] [Abstract][Full Text] [Related]  

  • 2. High-frame-rate contrast-enhanced ultrasound to differentiate between clear cell renal cell carcinoma and angiomyolipoma.
    Wang J; Shi J; Gao L; Hu W; Chen M; Zhang W
    BMC Cancer; 2024 May; 24(1):659. PubMed ID: 38816725
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 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. 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]  

  • 6. Papillary renal cell carcinoma and clear cell renal cell carcinoma: Differentiation of distinct histological types with contrast - enhanced ultrasonography.
    Xue LY; Lu Q; Huang BJ; Li Z; Li CX; Wen JX; Wang WP
    Eur J Radiol; 2015 Oct; 84(10):1849-56. PubMed ID: 26149528
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Comparative diagnostic performance of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging for differentiating clear cell and non-clear cell renal cell carcinoma.
    Zhao P; Zhu J; Wang L; Li N; Zhang X; Li J; Luo Y; Li Q
    Eur Radiol; 2023 May; 33(5):3766-3774. PubMed ID: 36725722
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma.
    Xi Y; Yuan Q; Zhang Y; Madhuranthakam AJ; Fulkerson M; Margulis V; Brugarolas J; Kapur P; Cadeddu JA; Pedrosa I
    Eur Radiol; 2018 Jan; 28(1):124-132. PubMed ID: 28681074
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.
    Shu J; Tang Y; Cui J; Yang R; Meng X; Cai Z; Zhang J; Xu W; Wen D; Yin H
    Eur J Radiol; 2018 Dec; 109():8-12. PubMed ID: 30527316
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram.
    Cheng D; Abudikeranmu Y; Tuerdi B
    Curr Med Imaging; 2023; 19(9):1005-1017. PubMed ID: 36411581
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.
    Nie P; Liu S; Zhou R; Li X; Zhi K; Wang Y; Dai Z; Zhao L; Wang N; Zhao X; Li X; Cheng N; Wang Y; Chen C; Xu Y; Yang G
    Eur J Radiol; 2023 Sep; 166():111018. PubMed ID: 37562222
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma.
    Sun J; Pan L; Zha T; Xing W; Chen J; Duan S
    Acta Radiol; 2021 Aug; 62(8):1104-1111. PubMed ID: 32867506
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.
    Zhao Y; Chang M; Wang R; Xi IL; Chang K; Huang RY; Vallières M; Habibollahi P; Dagli MS; Palmer M; Zhang PJ; Silva AC; Yang L; Soulen MC; Zhang Z; Bai HX; Stavropoulos SW
    J Magn Reson Imaging; 2020 Nov; 52(5):1542-1549. PubMed ID: 32222054
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The value of real-time contrast-enhanced ultrasound combined with CT enhancement in the differentiation of subtypes of renal cell carcinoma.
    Liang RX; Wang H; Zhang HP; Ye Q; Zhang Y; Zheng MJ; Xue ES; Zhu YF
    Urol Oncol; 2021 Dec; 39(12):837.e19-837.e28. PubMed ID: 34654644
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Use of specific contrast-enhanced CT regions of interest to differentiate renal oncocytomas from small clear cell and chromophobe renal cell carcinomas.
    Qu JY; Jiang H; Wang XF; Song XH; Hao CJ
    Diagn Interv Radiol; 2022 Nov; 28(6):555-562. PubMed ID: 36550755
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparison of Biexponential and Monoexponential Model of Diffusion-Weighted Imaging for Distinguishing between Common Renal Cell Carcinoma and Fat Poor Angiomyolipoma.
    Ding Y; Zeng M; Rao S; Chen C; Fu C; Zhou J
    Korean J Radiol; 2016; 17(6):853-863. PubMed ID: 27833401
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.