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Journal Abstract Search

618 related items for PubMed ID: 30895704

  • 1.
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  • 2. Radiomics strategy for glioma grading using texture features from multiparametric MRI.
    Tian Q, Yan LF, Zhang X, Zhang X, Hu YC, Han Y, Liu ZC, Nan HY, Sun Q, Sun YZ, Yang Y, Yu Y, Zhang J, Hu B, Xiao G, Chen P, Tian S, Xu J, Wang W, Cui GB.
    J Magn Reson Imaging; 2018 Dec; 48(6):1518-1528. PubMed ID: 29573085
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  • 3. Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma.
    Yang Y, Yan LF, Zhang X, Nan HY, Hu YC, Han Y, Zhang J, Liu ZC, Sun YZ, Tian Q, Yu Y, Sun Q, Wang SY, Zhang X, Wang W, Cui GB.
    J Magn Reson Imaging; 2019 May; 49(5):1263-1274. PubMed ID: 30623514
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  • 5. Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma.
    Bhattacharjee R, Gupta RK, Patir R, Vaishya S, Ahlawat S, Singh A.
    J Magn Reson Imaging; 2020 Jan; 51(1):225-233. PubMed ID: 31087724
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  • 6. Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.
    Vamvakas A, Williams SC, Theodorou K, Kapsalaki E, Fountas K, Kappas C, Vassiou K, Tsougos I.
    Phys Med; 2019 Apr; 60():188-198. PubMed ID: 30910431
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  • 7. 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
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  • 8. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
    Zhang X, Yan LF, Hu YC, Li G, Yang Y, Han Y, Sun YZ, Liu ZC, Tian Q, Han ZY, Liu LD, Hu BQ, Qiu ZY, Wang W, Cui GB.
    Oncotarget; 2017 Jul 18; 8(29):47816-47830. PubMed ID: 28599282
    [Abstract] [Full Text] [Related]

  • 9. Evaluating feasibility of high resolution T1-perfusion MRI with whole brain coverage using compressed SENSE: Application to glioma grading.
    Sasi S D, Ramaniharan AK, Bhattacharjee R, Gupta RK, Saha I, Van Cauteren M, Shah T, Gopalakrishnan K, Gupta A, Singh A.
    Eur J Radiol; 2020 Aug 18; 129():109049. PubMed ID: 32464580
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  • 12. Association of Glioma Grading With Inflow-Based Vascular-Space-Occupancy MRI: A Preliminary Study at 3T.
    Li X, Liao S, Hua J, Guo L, Wang D, Xiao X, Zhou J, Liu X, Tan Y, Lu L, Xu Y, Wu Y.
    J Magn Reson Imaging; 2019 Dec 18; 50(6):1817-1823. PubMed ID: 30932289
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  • 15. 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 18; 35(6):1103-10. PubMed ID: 24384119
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  • 16. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
    Park YW, Choi YS, Ahn SS, Chang JH, Kim SH, Lee SK.
    Korean J Radiol; 2019 Sep 18; 20(9):1381-1389. PubMed ID: 31464116
    [Abstract] [Full Text] [Related]

  • 17. Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.
    Fathi Kazerooni A, Nabil M, Zeinali Zadeh M, Firouznia K, Azmoudeh-Ardalan F, Frangi AF, Davatzikos C, Saligheh Rad H.
    J Magn Reson Imaging; 2018 Oct 18; 48(4):938-950. PubMed ID: 29412496
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