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 *

912 related articles for article (PubMed ID: 29626238)

  • 21. Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.
    Priya S; Ward C; Locke T; Soni N; Maheshwarappa RP; Monga V; Agarwal A; Bathla G
    Neuroradiol J; 2021 Aug; 34(4):320-328. PubMed ID: 33657924
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.
    Alcaide-Leon P; Dufort P; Geraldo AF; Alshafai L; Maralani PJ; Spears J; Bharatha A
    AJNR Am J Neuroradiol; 2017 Jun; 38(6):1145-1150. PubMed ID: 28450433
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.
    Park JE; Ham S; Kim HS; Park SY; Yun J; Lee H; Choi SH; Kim N
    Eur Radiol; 2021 May; 31(5):3127-3137. PubMed ID: 33128598
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Permeability measurement using dynamic susceptibility contrast magnetic resonance imaging enhances differential diagnosis of primary central nervous system lymphoma from glioblastoma.
    Lee JY; Bjørnerud A; Park JE; Lee BE; Kim JH; Kim HS
    Eur Radiol; 2019 Oct; 29(10):5539-5548. PubMed ID: 30877463
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients.
    Mehrnahad M; Rostami S; Kimia F; Kord R; Taheri MS; Rad HS; Haghighatkhah H; Moradi A; Kord A
    Neuroradiol J; 2020 Oct; 33(5):428-436. PubMed ID: 32628089
    [TBL] [Abstract][Full Text] [Related]  

  • 26.
    Kong Z; Jiang C; Zhu R; Feng S; Wang Y; Li J; Chen W; Liu P; Zhao D; Ma W; Wang Y; Cheng X
    Neuroimage Clin; 2019; 23():101912. PubMed ID: 31491820
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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]  

  • 28. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.
    Zhao SS; Feng XL; Hu YC; Han Y; Tian Q; Sun YZ; Zhang J; Ge XW; Cheng SC; Li XL; Mao L; Shen SN; Yan LF; Cui GB; Wang W
    BMC Neurol; 2020 Feb; 20(1):48. PubMed ID: 32033580
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis.
    Suh CH; Kim HS; Jung SC; Park JE; Choi CG; Kim SJ
    J Magn Reson Imaging; 2019 Aug; 50(2):560-572. PubMed ID: 30637843
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.
    Kim JY; Park JE; Jo Y; Shim WH; Nam SJ; Kim JH; Yoo RE; Choi SH; Kim HS
    Neuro Oncol; 2019 Feb; 21(3):404-414. PubMed ID: 30107606
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
    Wang X; Wan Q; Chen H; Li Y; Li X
    Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling.
    Yu X; Hong W; Ye M; Lai M; Shi C; Li L; Ye K; Xu J; Ai R; Shan C; Cai L; Luo L
    Eur Radiol; 2023 Aug; 33(8):5357-5367. PubMed ID: 37171492
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.
    Tang Y; Yang CM; Su S; Wang WJ; Fan LP; Shu J
    BMC Cancer; 2021 Nov; 21(1):1268. PubMed ID: 34819043
    [TBL] [Abstract][Full Text] [Related]  

  • 35. MR imaging based fractal analysis for differentiating primary CNS lymphoma and glioblastoma.
    Liu S; Fan X; Zhang C; Wang Z; Li S; Wang Y; Qiu X; Jiang T
    Eur Radiol; 2019 Mar; 29(3):1348-1354. PubMed ID: 30167811
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.
    Chen C; Zheng A; Ou X; Wang J; Ma X
    Front Oncol; 2020; 10():1151. PubMed ID: 33042784
    [No Abstract]   [Full Text] [Related]  

  • 37. 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]  

  • 38. 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]  

  • 39. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors.
    Wang H; Hu D; Yao H; Chen M; Li S; Chen H; Luo J; Feng Y; Guo Y
    Eur Radiol; 2019 Nov; 29(11):6182-6190. PubMed ID: 31016445
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Magnetic resonance texture analysis utility in differentiating intraparenchymal neurosarcoidosis from primary central nervous system lymphoma: a preliminary analysis.
    Bathla G; Soni N; Endozo R; Ganeshan B
    Neuroradiol J; 2019 Jun; 32(3):203-209. PubMed ID: 30789057
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 46.