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Journal Abstract Search

618 related items for PubMed ID: 30895704

  • 21. Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI.
    Vats N, Sengupta A, Gupta RK, Patir R, Vaishya S, Ahlawat S, Saini J, Agarwal S, Singh A.
    Magn Reson Imaging; 2023 May; 98():76-82. PubMed ID: 36572323
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  • 23. Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models.
    Guo H, Liu J, Hu J, Zhang H, Zhao W, Gao M, Zhang Y, Yang G, Cui Y.
    J Magn Reson Imaging; 2022 Dec; 56(6):1834-1844. PubMed ID: 35488516
    [Abstract] [Full Text] [Related]

  • 24. Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas.
    Raja R, Sinha N, Saini J, Mahadevan A, Rao KN, Swaminathan A.
    Neuroradiology; 2016 Dec; 58(12):1217-1231. PubMed ID: 27796448
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  • 26. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis.
    Nakamoto T, Takahashi W, Haga A, Takahashi S, Kiryu S, Nawa K, Ohta T, Ozaki S, Nozawa Y, Tanaka S, Mukasa A, Nakagawa K.
    Sci Rep; 2019 Dec 19; 9(1):19411. PubMed ID: 31857632
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  • 27. Imaging-Based Algorithm for the Local Grading of Glioma.
    Gates EDH, Lin JS, Weinberg JS, Prabhu SS, Hamilton J, Hazle JD, Fuller GN, Baladandayuthapani V, Fuentes DT, Schellingerhout D.
    AJNR Am J Neuroradiol; 2020 Mar 19; 41(3):400-407. PubMed ID: 32029466
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  • 28. Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI.
    Gupta M, Gupta A, Yadav V, Parvaze SP, Singh A, Saini J, Patir R, Vaishya S, Ahlawat S, Gupta RK.
    Neuroradiology; 2021 Aug 19; 63(8):1227-1239. PubMed ID: 33469693
    [Abstract] [Full Text] [Related]

  • 29. Facing contrast-enhancing gliomas: perfusion MRI in grade III and grade IV gliomas according to tumor area.
    Di Stefano AL, Bergsland N, Berzero G, Farina L, Rognone E, Gastaldi M, Aquino D, Frati A, Tomasello F, Ceroni M, Marchioni E, Bastianello S.
    Biomed Res Int; 2014 Aug 19; 2014():154350. PubMed ID: 24800207
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  • 30. 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 19; 85(6):1147-56. PubMed ID: 27161065
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  • 31. Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.
    Citak-Er F, Firat Z, Kovanlikaya I, Ture U, Ozturk-Isik E.
    Comput Biol Med; 2018 Aug 01; 99():154-160. PubMed ID: 29933126
    [Abstract] [Full Text] [Related]

  • 32. 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 Aug 01; 25(5):792-8. PubMed ID: 25867683
    [Abstract] [Full Text] [Related]

  • 33. A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma.
    Cao H, Erson-Omay EZ, Li X, Günel M, Moliterno J, Fulbright RK.
    Eur Radiol; 2020 Jun 01; 30(6):3073-3082. PubMed ID: 32025832
    [Abstract] [Full Text] [Related]

  • 34. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.
    De Looze C, Beausang A, Cryan J, Loftus T, Buckley PG, Farrell M, Looby S, Reilly R, Brett F, Kearney H.
    J Neurooncol; 2018 Sep 01; 139(2):491-499. PubMed ID: 29770897
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  • 36. Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas.
    Hussain NS, Moisi MD, Keogh B, McCullough BJ, Rostad S, Newell D, Gwinn R, Foltz G, Mayberg M, Aguedan B, Good V, Fouke SJ.
    J Neurosurg; 2017 Apr 01; 126(4):1220-1226. PubMed ID: 27285539
    [Abstract] [Full Text] [Related]

  • 37. Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.
    Ahammed Muneer K V, Rajendran VR, K PJ.
    J Med Syst; 2019 Mar 21; 43(5):113. PubMed ID: 30900029
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  • 38. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.
    Alis D, Bagcilar O, Senli YD, Yergin M, Isler C, Kocer N, Islak C, Kizilkilic O.
    Jpn J Radiol; 2020 Feb 21; 38(2):135-143. PubMed ID: 31741126
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  • 39. Whole-brain amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging in glioma patients using low-power steady-state pulsed chemical exchange saturation transfer (CEST) imaging at 7T.
    Heo HY, Jones CK, Hua J, Yadav N, Agarwal S, Zhou J, van Zijl PC, Pillai JJ.
    J Magn Reson Imaging; 2016 Jul 21; 44(1):41-50. PubMed ID: 26663561
    [Abstract] [Full Text] [Related]

  • 40. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma.
    Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y, Wang Y, Chen L, Mao Y.
    Eur Radiol; 2017 Aug 21; 27(8):3509-3522. PubMed ID: 28004160
    [Abstract] [Full Text] [Related]

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