These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
196 related articles for article (PubMed ID: 30506142)
1. 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]
2. Voxel-based whole-lesion enhancement parameters: a study of its clinical value in differentiating clear cell renal cell carcinoma from renal oncocytoma. Chen F; Gulati M; Hwang D; Cen S; Yap F; Ugwueze C; Varghese B; Desai M; Aron M; Gill I; Duddalwar V Abdom Radiol (NY); 2017 Feb; 42(2):552-560. PubMed ID: 27595574 [TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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; 46(9):4289-4300. PubMed ID: 33909090 [TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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; 93(1114):20200131. PubMed ID: 32706977 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis. Sun R; Zhao S; Jiang H; Jiang H; Dai Y; Zhang C; Wang S Biomed Res Int; 2021; 2021():1821876. PubMed ID: 34977234 [TBL] [Abstract][Full Text] [Related]
11. 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; 110(7):4221-4230. PubMed ID: 38573065 [TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. 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]
15. 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]
16. Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Differentiation of Minimal Fat Angiomyolipoma from Clear Cell Renal Cell Carcinoma. Li H; Li A; Zhu H; Hu Y; Li J; Xia L; Hu D; Kamel IR; Li Z Acad Radiol; 2019 May; 26(5):632-639. PubMed ID: 30087067 [TBL] [Abstract][Full Text] [Related]
17. 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; 126(9):1139-1148. PubMed ID: 34100169 [TBL] [Abstract][Full Text] [Related]
18. 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]