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393 related items for PubMed ID: 24384119

  • 1. Glioma: Application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading.
    Jung SC, Yeom JA, Kim JH, Ryoo I, Kim SC, Shin H, Lee AL, Yun TJ, Park CK, Sohn CH, Park SH, Choi SH.
    AJNR Am J Neuroradiol; 2014 Jun; 35(6):1103-10. PubMed ID: 24384119
    [Abstract] [Full Text] [Related]

  • 2. Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: Preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis.
    Santarosa C, Castellano A, Conte GM, Cadioli M, Iadanza A, Terreni MR, Franzin A, Bello L, Caulo M, Falini A, Anzalone N.
    Eur J Radiol; 2016 Jun; 85(6):1147-56. PubMed ID: 27161065
    [Abstract] [Full Text] [Related]

  • 3. Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation.
    Lee J, Choi SH, Kim JH, Sohn CH, Lee S, Jeong J.
    NMR Biomed; 2014 Sep; 27(9):1046-52. PubMed ID: 25042540
    [Abstract] [Full Text] [Related]

  • 4. Gliomas: application of cumulative histogram analysis of normalized cerebral blood volume on 3 T MRI to tumor grading.
    Kim H, Choi SH, Kim JH, Ryoo I, Kim SC, Yeom JA, Shin H, Jung SC, Lee AL, Yun TJ, Park CK, Sohn CH, Park SH.
    PLoS One; 2013 Sep; 8(5):e63462. PubMed ID: 23704910
    [Abstract] [Full Text] [Related]

  • 5. Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading.
    Liu HS, Chiang SW, Chung HW, Tsai PH, Hsu FT, Cho NY, Wang CY, Chou MC, Chen CY.
    Comput Methods Programs Biomed; 2018 Mar; 155():19-27. PubMed ID: 29512499
    [Abstract] [Full Text] [Related]

  • 6. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
    Inano R, Oishi N, Kunieda T, Arakawa Y, Yamao Y, Shibata S, Kikuchi T, Fukuyama H, Miyamoto S.
    Neuroimage Clin; 2014 Mar; 5():396-407. PubMed ID: 25180159
    [Abstract] [Full Text] [Related]

  • 7. Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease.
    Seeger A, Braun C, Skardelly M, Paulsen F, Schittenhelm J, Ernemann U, Bisdas S.
    Acad Radiol; 2013 Dec; 20(12):1557-65. PubMed ID: 24200483
    [Abstract] [Full Text] [Related]

  • 8. Volume-based histogram analysis of dynamic contrast-enhanced MRI for estimation of gliomas IDH1 mutation status.
    Hu Y, Zhang N, Yu MH, Zhou XJ, Ge M, Shen DD, Hua Y, Shi JL, Jia ZZ.
    Eur J Radiol; 2020 Oct; 131():109247. PubMed ID: 32891974
    [Abstract] [Full Text] [Related]

  • 9. Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas.
    Zhang N, Zhang L, Qiu B, Meng L, Wang X, Hou BL.
    J Magn Reson Imaging; 2012 Aug; 36(2):355-63. PubMed ID: 22581762
    [Abstract] [Full Text] [Related]

  • 10. Gliomas: Histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging--correlation with tumor grade.
    Kang Y, Choi SH, Kim YJ, Kim KG, Sohn CH, Kim JH, Yun TJ, Chang KH.
    Radiology; 2011 Dec; 261(3):882-90. PubMed ID: 21969667
    [Abstract] [Full Text] [Related]

  • 11. Low-grade (WHO II) and anaplastic (WHO III) gliomas: differences in morphology and MRI signal intensities.
    Schäfer ML, Maurer MH, Synowitz M, Wüstefeld J, Marnitz T, Streitparth F, Wiener E.
    Eur Radiol; 2013 Oct; 23(10):2846-53. PubMed ID: 23686293
    [Abstract] [Full Text] [Related]

  • 12. Glioma grading capability: comparisons among parameters from dynamic contrast-enhanced MRI and ADC value on DWI.
    Choi HS, Kim AH, Ahn SS, Shin NY, Kim J, Lee SK.
    Korean J Radiol; 2013 Oct; 14(3):487-92. PubMed ID: 23690718
    [Abstract] [Full Text] [Related]

  • 13. Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine positron emission tomography image improves the diagnostic accuracy of cerebral glioma grading.
    Wu R, Watanabe Y, Arisawa A, Takahashi H, Tanaka H, Fujimoto Y, Watabe T, Isohashi K, Hatazawa J, Tomiyama N.
    Jpn J Radiol; 2017 Oct; 35(10):613-621. PubMed ID: 28879406
    [Abstract] [Full Text] [Related]

  • 14. Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high- and low-grade glioma using dynamic contrast-enhanced (DCE) MRI.
    Sahoo P, Rathore RK, Awasthi R, Roy B, Verma S, Rathore D, Behari S, Husain M, Husain N, Pandey CM, Mohakud S, Gupta RK.
    J Magn Reson Imaging; 2013 Sep; 38(3):677-88. PubMed ID: 23390002
    [Abstract] [Full Text] [Related]

  • 15. Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.
    Xie T, Chen X, Fang J, Kang H, Xue W, Tong H, Cao P, Wang S, Yang Y, Zhang W.
    J Magn Reson Imaging; 2018 Apr; 47(4):1099-1111. PubMed ID: 28845594
    [Abstract] [Full Text] [Related]

  • 16. Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors.
    Arisawa A, Watanabe Y, Tanaka H, Takahashi H, Matsuo C, Fujiwara T, Fujiwara M, Fujimoto Y, Tomiyama N.
    Neuroradiology; 2018 Jun; 60(6):599-608. PubMed ID: 29705876
    [Abstract] [Full Text] [Related]

  • 17. Dynamic Contrast-Enhanced Perfusion MRI and Diffusion-Weighted Imaging in Grading of Gliomas.
    Arevalo-Perez J, Peck KK, Young RJ, Holodny AI, Karimi S, Lyo JK.
    J Neuroimaging; 2015 Jun; 25(5):792-8. PubMed ID: 25867683
    [Abstract] [Full Text] [Related]

  • 18. Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images.
    Anzalone N, Castellano A, Cadioli M, Conte GM, Cuccarini V, Bizzi A, Grimaldi M, Costa A, Grillea G, Vitali P, Aquino D, Terreni MR, Torri V, Erickson BJ, Caulo M.
    Radiology; 2018 Jun; 287(3):933-943. PubMed ID: 29361245
    [Abstract] [Full Text] [Related]

  • 19. Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components.
    Sengupta A, Ramaniharan AK, Gupta RK, Agarwal S, Singh A.
    J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
    [Abstract] [Full Text] [Related]

  • 20. Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging.
    Togao O, Hiwatashi A, Yamashita K, Kikuchi K, Keupp J, Yoshimoto K, Kuga D, Yoneyama M, Suzuki SO, Iwaki T, Takahashi M, Iihara K, Honda H.
    Eur Radiol; 2017 Feb; 27(2):578-588. PubMed ID: 27003139
    [Abstract] [Full Text] [Related]

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