BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

393 related articles for article (PubMed ID: 28421244)

  • 1. 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]  

  • 2. Small (< 4 cm) Renal Mass: Differentiation of Oncocytoma From Renal Cell Carcinoma on Biphasic Contrast-Enhanced CT.
    Sasaguri K; Takahashi N; Gomez-Cardona D; Leng S; Schmit GD; Carter RE; Leibovich BC; Kawashima A
    AJR Am J Roentgenol; 2015 Nov; 205(5):999-1007. PubMed ID: 26496547
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 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. 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]  

  • 6. Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography.
    Coy H; Young JR; Douek ML; Brown MS; Sayre J; Raman SS
    Abdom Radiol (NY); 2017 Jul; 42(7):1919-1928. PubMed ID: 28280876
    [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. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. Subtype Differentiation of Small (≤ 4 cm) Solid Renal Mass Using Volumetric Histogram Analysis of DWI at 3-T MRI.
    Li A; Xing W; Li H; Hu Y; Hu D; Li Z; Kamel IR
    AJR Am J Roentgenol; 2018 Sep; 211(3):614-623. PubMed ID: 29812980
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Pretreatment differentiation of renal cell carcinoma subtypes by CT: the influence of different tumor enhancement measurement approaches.
    Zokalj I; Marotti M; Kolarić B
    Int Urol Nephrol; 2014 Jun; 46(6):1089-100. PubMed ID: 24381132
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.
    Raman SP; Chen Y; Schroeder JL; Huang P; Fishman EK
    Acad Radiol; 2014 Dec; 21(12):1587-96. PubMed ID: 25239842
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT.
    Young JR; Margolis D; Sauk S; Pantuck AJ; Sayre J; Raman SS
    Radiology; 2013 May; 267(2):444-53. PubMed ID: 23382290
    [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 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]  

  • 20. Aorta-Lesion-Attenuation-Difference (ALAD) on contrast-enhanced CT: a potential imaging biomarker for differentiating malignant from benign oncocytic neoplasms.
    Dhyani M; Grajo JR; Rodriguez D; Chen Z; Feldman A; Tambouret R; Gervais DA; Arellano RS; Hahn PF; Samir AE
    Abdom Radiol (NY); 2017 Jun; 42(6):1734-1743. PubMed ID: 28197683
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.