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Journal Abstract Search

932 related items for PubMed ID: 29626238

  • 1. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
    Suh HB, Choi YS, Bae S, Ahn SS, Chang JH, Kang SG, Kim EH, Kim SH, Lee SK.
    Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
    [Abstract] [Full Text] [Related]

  • 2. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation.
    Kang D, Park JE, Kim YH, Kim JH, Oh JY, Kim J, Kim Y, Kim ST, Kim HS.
    Neuro Oncol; 2018 Aug 02; 20(9):1251-1261. PubMed ID: 29438500
    [Abstract] [Full Text] [Related]

  • 3. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient.
    Choi YS, Lee HJ, Ahn SS, Chang JH, Kang SG, Kim EH, Kim SH, Lee SK.
    Eur Radiol; 2017 Apr 02; 27(4):1344-1351. PubMed ID: 27436023
    [Abstract] [Full Text] [Related]

  • 4. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
    Kim Y, Cho HH, Kim ST, Park H, Nam D, Kong DS.
    Neuroradiology; 2018 Dec 02; 60(12):1297-1305. PubMed ID: 30232517
    [Abstract] [Full Text] [Related]

  • 5. Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.
    Xia W, Hu B, Li H, Geng C, Wu Q, Yang L, Yin B, Gao X, Li Y, Geng D.
    J Magn Reson Imaging; 2021 Jan 02; 53(1):242-250. PubMed ID: 32864825
    [Abstract] [Full Text] [Related]

  • 6. Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging.
    You SH, Yun TJ, Choi HJ, Yoo RE, Kang KM, Choi SH, Kim JH, Sohn CH.
    Eur Radiol; 2018 Sep 02; 28(9):3801-3810. PubMed ID: 29619520
    [Abstract] [Full Text] [Related]

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  • 8. Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.
    Kunimatsu A, Kunimatsu N, Yasaka K, Akai H, Kamiya K, Watadani T, Mori H, Abe O.
    Magn Reson Med Sci; 2019 Jan 10; 18(1):44-52. PubMed ID: 29769456
    [Abstract] [Full Text] [Related]

  • 9. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
    Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, Oda S, Utsunomiya D, Makino K, Nakamura H, Mukasa A, Hirai T, Yamashita Y.
    Eur J Radiol; 2018 Nov 10; 108():147-154. PubMed ID: 30396648
    [Abstract] [Full Text] [Related]

  • 10. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.
    Xia W, Hu B, Li H, Shi W, Tang Y, Yu Y, Geng C, Wu Q, Yang L, Yu Z, Geng D, Li Y.
    J Magn Reson Imaging; 2021 Sep 10; 54(3):880-887. PubMed ID: 33694250
    [Abstract] [Full Text] [Related]

  • 11. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.
    Nguyen AV, Blears EE, Ross E, Lall RR, Ortega-Barnett J.
    Neurosurg Focus; 2018 Nov 01; 45(5):E5. PubMed ID: 30453459
    [Abstract] [Full Text] [Related]

  • 12. 18F-FDG PET/CT in immunocompetent patients with primary central nervous system lymphoma: Differentiation from glioblastoma and correlation with DWI.
    Zhou W, Wen J, Hua F, Xu W, Lu X, Yin B, Geng D, Guan Y.
    Eur J Radiol; 2018 Jul 01; 104():26-32. PubMed ID: 29857862
    [Abstract] [Full Text] [Related]

  • 13. Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach.
    Bae S, Choi YS, Sohn B, Ahn SS, Lee SK, Yang J, Kim J.
    Yonsei Med J; 2020 Oct 01; 61(10):895-900. PubMed ID: 32975065
    [Abstract] [Full Text] [Related]

  • 14. Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.
    Saini J, Kumar Gupta P, Awasthi A, Pandey CM, Singh A, Patir R, Ahlawat S, Sadashiva N, Mahadevan A, Kumar Gupta R.
    Clin Radiol; 2018 Nov 01; 73(11):986.e7-986.e15. PubMed ID: 30197047
    [Abstract] [Full Text] [Related]

  • 15. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis.
    Xiao DD, Yan PF, Wang YX, Osman MS, Zhao HY.
    Clin Neurol Neurosurg; 2018 Oct 01; 173():84-90. PubMed ID: 30092408
    [Abstract] [Full Text] [Related]

  • 16. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.
    Bathla G, Priya S, Liu Y, Ward C, Le NH, Soni N, Maheshwarappa RP, Monga V, Zhang H, Sonka M.
    Eur Radiol; 2021 Nov 01; 31(11):8703-8713. PubMed ID: 33890149
    [Abstract] [Full Text] [Related]

  • 17. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.
    Chen X, Fang M, Dong D, Liu L, Xu X, Wei X, Jiang X, Qin L, Liu Z.
    Acad Radiol; 2019 Oct 01; 26(10):1292-1300. PubMed ID: 30660472
    [Abstract] [Full Text] [Related]

  • 18. Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient.
    Bao S, Watanabe Y, Takahashi H, Tanaka H, Arisawa A, Matsuo C, Wu R, Fujimoto Y, Tomiyama N.
    Magn Reson Med Sci; 2019 Jan 10; 18(1):53-61. PubMed ID: 29848919
    [Abstract] [Full Text] [Related]

  • 19. Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma.
    Lin X, Lee M, Buck O, Woo KM, Zhang Z, Hatzoglou V, Omuro A, Arevalo-Perez J, Thomas AA, Huse J, Peck K, Holodny AI, Young RJ.
    AJNR Am J Neuroradiol; 2017 Mar 10; 38(3):485-491. PubMed ID: 27932505
    [Abstract] [Full Text] [Related]

  • 20. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.
    Sun YZ, Yan LF, Han Y, Nan HY, Xiao G, Tian Q, Pu WH, Li ZY, Wei XC, Wang W, Cui GB.
    BMC Med Imaging; 2021 Feb 03; 21(1):17. PubMed ID: 33535988
    [Abstract] [Full Text] [Related]

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