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250 related items for PubMed ID: 30946334

  • 1. Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.
    Sun X, Liu L, Xu K, Li W, Huo Z, Liu H, Shen T, Pan F, Jiang Y, Zhang M.
    Medicine (Baltimore); 2019 Apr; 98(14):e15022. PubMed ID: 30946334
    [Abstract] [Full Text] [Related]

  • 2. Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.
    Lu C, Xia Y, Han J, Chen W, Qiao X, Gao R, Jiang X.
    Sci Rep; 2024 May 27; 14(1):12043. PubMed ID: 38802547
    [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 27; 121():108738. PubMed ID: 31756634
    [Abstract] [Full Text] [Related]

  • 4. CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.
    Zhang H, Yin F, Chen M, Qi A, Yang L, Wen G.
    Br J Radiol; 2024 May 29; 97(1158):1169-1179. PubMed ID: 38688660
    [Abstract] [Full Text] [Related]

  • 5. 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 29; 103():51-56. PubMed ID: 29803385
    [Abstract] [Full Text] [Related]

  • 6. 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 29; 109():8-12. PubMed ID: 30527316
    [Abstract] [Full Text] [Related]

  • 7. Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT.
    Han D, Yu Y, Yu N, Dang S, Wu H, Jialiang R, He T.
    Br J Radiol; 2020 Oct 01; 93(1114):20200131. PubMed ID: 32706977
    [Abstract] [Full Text] [Related]

  • 8. Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study.
    Li S, Zhou Z, Gao M, Liao Z, He K, Qu W, Li J, Kamel IR, Chu Q, Zhang Q, Li Z.
    Int J Surg; 2024 Jul 01; 110(7):4221-4230. PubMed ID: 38573065
    [Abstract] [Full Text] [Related]

  • 9. Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study.
    Xv Y, Lv F, Guo H, Zhou X, Tan H, Xiao M, Zheng Y.
    Insights Imaging; 2021 Nov 20; 12(1):170. PubMed ID: 34800179
    [Abstract] [Full Text] [Related]

  • 10. Ultrasound-Based Radiomics for Predicting the WHO/ISUP Grading of Clear-Cell Renal Cell Carcinoma.
    Chen YF, Fu F, Zhuang JJ, Zheng WT, Zhu YF, Lian GT, Fan XQ, Zhang HP, Ye Q.
    Ultrasound Med Biol; 2024 Nov 20; 50(11):1619-1627. PubMed ID: 39097493
    [Abstract] [Full Text] [Related]

  • 11. 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 20; 125(8):754-762. PubMed ID: 32193870
    [Abstract] [Full Text] [Related]

  • 12. Prediction of World Health Organization /International Society of Urological Pathology (WHO/ISUP) Pathological Grading of Clear Cell Renal Cell Carcinoma by Dual-Layer Spectral CT.
    Zhang X, Zhang G, Xu L, Bai X, Zhang J, Chen L, Lu X, Yu S, Jin Z, Sun H.
    Acad Radiol; 2023 Oct 20; 30(10):2321-2328. PubMed ID: 36543688
    [Abstract] [Full Text] [Related]

  • 13. 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 20; 30(5):2912-2921. PubMed ID: 32002635
    [Abstract] [Full Text] [Related]

  • 14. Tumor grade estımatıon of clear cell and papıllary renal cell carcınomas usıng contrast-enhanced MDCT and FSE T2 weıghted MR ımagıng: radıology-pathology correlatıon.
    Halefoglu AM, Ozagari AA.
    Radiol Med; 2021 Sep 20; 126(9):1139-1148. PubMed ID: 34100169
    [Abstract] [Full Text] [Related]

  • 15. 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 20; 32(4):2552-2563. PubMed ID: 34757449
    [Abstract] [Full Text] [Related]

  • 16. Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma?
    Wang X, Song G, Jiang H, Zheng L, Pang P, Xu J.
    Abdom Radiol (NY); 2021 Sep 20; 46(9):4289-4300. PubMed ID: 33909090
    [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 20; 44(7):2528-2534. PubMed ID: 30919041
    [Abstract] [Full Text] [Related]

  • 18. Computed Tomography-Based Radiomics Model for Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Preoperatively: A Multicenter Study.
    Wang R, Hu Z, Shen X, Wang Q, Zhang L, Wang M, Feng Z, Chen F.
    Front Oncol; 2021 Jul 20; 11():543854. PubMed ID: 33718124
    [Abstract] [Full Text] [Related]

  • 19. Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas.
    Wang P, Pei X, Yin XP, Ren JL, Wang Y, Ma LY, Du XG, Gao BL.
    Sci Rep; 2021 Jul 02; 11(1):13729. PubMed ID: 34215760
    [Abstract] [Full Text] [Related]

  • 20. Multi-sequence MRI-based radiomics model to preoperatively predict the WHO/ISUP grade of clear Cell Renal Cell Carcinoma: a two-center study.
    Chen R, Su Q, Li Y, Shen P, Zhang J, Tan Y.
    BMC Cancer; 2024 Sep 27; 24(1):1176. PubMed ID: 39333970
    [Abstract] [Full Text] [Related]

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