These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

500 related articles for article (PubMed ID: 30396648)

  • 1. 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; 108():147-154. PubMed ID: 30396648
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.
    Tateishi M; Nakaura T; Kitajima M; Uetani H; Nakagawa M; Inoue T; Kuroda JI; Mukasa A; Yamashita Y
    J Neurol Sci; 2020 Mar; 410():116514. PubMed ID: 31869660
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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
    [TBL] [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; 60(12):1297-1305. PubMed ID: 30232517
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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; 73(11):986.e7-986.e15. PubMed ID: 30197047
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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; 173():84-90. PubMed ID: 30092408
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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; 18(1):44-52. PubMed ID: 29769456
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging.
    Kickingereder P; Wiestler B; Sahm F; Heiland S; Roethke M; Schlemmer HP; Wick W; Bendszus M; Radbruch A
    Radiology; 2014 Sep; 272(3):843-50. PubMed ID: 24814181
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: a preliminary investigation in terms of identification and segmentation.
    Assefa D; Keller H; Ménard C; Laperriere N; Ferrari RJ; Yeung I
    Med Phys; 2010 Apr; 37(4):1722-36. PubMed ID: 20443493
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Diagnostic utility of intravoxel incoherent motion mr imaging in differentiating primary central nervous system lymphoma from glioblastoma multiforme.
    Yamashita K; Hiwatashi A; Togao O; Kikuchi K; Kitamura Y; Mizoguchi M; Yoshimoto K; Kuga D; Suzuki SO; Baba S; Isoda T; Iwaki T; Iihara K; Honda H
    J Magn Reson Imaging; 2016 Nov; 44(5):1256-1261. PubMed ID: 27093558
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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; 27(4):1344-1351. PubMed ID: 27436023
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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; 54(3):880-887. PubMed ID: 33694250
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features.
    Nakagawa M; Nakaura T; Namimoto T; Iyama Y; Kidoh M; Hirata K; Nagayama Y; Yuki H; Oda S; Utsunomiya D; Yamashita Y
    Acad Radiol; 2019 Oct; 26(10):1390-1399. PubMed ID: 30661978
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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; 28(9):3801-3810. PubMed ID: 29619520
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
    Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
    Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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; 18(1):53-61. PubMed ID: 29848919
    [TBL] [Abstract][Full Text] [Related]  

  • 17.
    Zhou W; Wen J; Hua F; Xu W; Lu X; Yin B; Geng D; Guan Y
    Eur J Radiol; 2018 Jul; 104():26-32. PubMed ID: 29857862
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion.
    Bauer AH; Erly W; Moser FG; Maya M; Nael K
    Neuroradiology; 2015 Jul; 57(7):697-703. PubMed ID: 25845813
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Quantitative Evaluation of Diffusion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Differentiation Between Primary Central Nervous System Lymphoma and Glioblastoma.
    Lu S; Wang S; Gao Q; Zhou M; Li Y; Cao P; Hong X; Shi H
    J Comput Assist Tomogr; 2017; 41(6):898-903. PubMed ID: 28806317
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Does adding FDG-PET to MRI improve the differentiation between primary cerebral lymphoma and glioblastoma? Observer performance study.
    Makino K; Hirai T; Nakamura H; Murakami R; Kitajima M; Shigematsu Y; Nakashima R; Shiraishi S; Uetani H; Iwashita K; Akter M; Yamashita Y; Kuratsu J
    Ann Nucl Med; 2011 Jul; 25(6):432-8. PubMed ID: 21404136
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.