BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

256 related articles for article (PubMed ID: 32627049)

  • 1. Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT.
    Schieda N; Nguyen K; Thornhill RE; McInnes MDF; Wu M; James N
    Abdom Radiol (NY); 2020 Sep; 45(9):2786-2796. PubMed ID: 32627049
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.
    Nguyen K; Schieda N; James N; McInnes MDF; Wu M; Thornhill RE
    Eur Radiol; 2021 Mar; 31(3):1676-1686. PubMed ID: 32914197
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
    Zabihollahy F; Schieda N; Krishna S; Ukwatta E
    Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
    Erdim C; Yardimci AH; Bektas CT; Kocak B; Koca SB; Demir H; Kilickesmez O
    Acad Radiol; 2020 Oct; 27(10):1422-1429. PubMed ID: 32014404
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. Differentiation of Benign From Metastatic Adrenal Masses in Patients With Renal Cell Carcinoma on Contrast-Enhanced CT.
    Sasaguri K; Takahashi N; Takeuchi M; Carter RE; Leibovich BC; Kawashima A
    AJR Am J Roentgenol; 2016 Nov; 207(5):1031-1038. PubMed ID: 27556736
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.
    Wentland AL; Yamashita R; Kino A; Pandit P; Shen L; Brooke Jeffrey R; Rubin D; Kamaya A
    Abdom Radiol (NY); 2023 Feb; 48(2):642-648. PubMed ID: 36370180
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Deep learning and radiomics: the utility of Google TensorFlowâ„¢ 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]  

  • 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. Characterization of clear cell renal cell carcinoma and other renal tumors: evaluation of dual-energy CT using material-specific iodine and fat imaging.
    Udare A; Walker D; Krishna S; Chatelain R; McInnes MD; Flood TA; Schieda N
    Eur Radiol; 2020 Apr; 30(4):2091-2102. PubMed ID: 31858204
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis.
    Schieda N; Dilauro M; Moosavi B; Hodgdon T; Cron GO; McInnes MD; Flood TA
    Eur Radiol; 2016 Jul; 26(7):2242-51. PubMed ID: 26486936
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT.
    Leng S; Takahashi N; Gomez Cardona D; Kitajima K; McCollough B; Li Z; Kawashima A; Leibovich BC; McCollough CH
    Abdom Radiol (NY); 2017 May; 42(5):1485-1492. PubMed ID: 28025654
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Qualitative and quantitative MDCT features for differentiating clear cell renal cell carcinoma from other solid renal cortical masses.
    Lee-Felker SA; Felker ER; Tan N; Margolis DJ; Young JR; Sayre J; Raman SS
    AJR Am J Roentgenol; 2014 Nov; 203(5):W516-24. PubMed ID: 25341166
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 13.