BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

201 related articles for article (PubMed ID: 30131513)

  • 1. Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma.
    Jang BS; Jeon SH; Kim IH; Kim IA
    Sci Rep; 2018 Aug; 8(1):12516. PubMed ID: 30131513
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
    Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
    Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T
    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; 21(1):17. PubMed ID: 33535988
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial.
    Liu ZC; Yan LF; Hu YC; Sun YZ; Tian Q; Nan HY; Yu Y; Sun Q; Wang W; Cui GB
    BMC Med Imaging; 2017 Feb; 17(1):10. PubMed ID: 28143434
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluation of pseudoprogression rates and tumor progression patterns in a phase III trial of bevacizumab plus radiotherapy/temozolomide for newly diagnosed glioblastoma.
    Wick W; Chinot OL; Bendszus M; Mason W; Henriksson R; Saran F; Nishikawa R; Revil C; Kerloeguen Y; Cloughesy T
    Neuro Oncol; 2016 Oct; 18(10):1434-41. PubMed ID: 27515827
    [TBL] [Abstract][Full Text] [Related]  

  • 6. DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet.
    Li M; Tang H; Chan MD; Zhou X; Qian X
    Med Phys; 2020 Mar; 47(3):1139-1150. PubMed ID: 31885094
    [TBL] [Abstract][Full Text] [Related]  

  • 7. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
    Choi Y; Nam Y; Lee YS; Kim J; Ahn KJ; Jang J; Shin NY; Kim BS; Jeon SS
    Eur J Radiol; 2020 Jul; 128():109031. PubMed ID: 32417712
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.
    McAvoy M; Prieto PC; Kaczmarzyk JR; Fernández IS; McNulty J; Smith T; Yu KH; Gormley WB; Arnaout O
    Sci Rep; 2021 Jul; 11(1):15219. PubMed ID: 34312463
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Serial FLT PET imaging to discriminate between true progression and pseudoprogression in patients with newly diagnosed glioblastoma: a long-term follow-up study.
    Brahm CG; den Hollander MW; Enting RH; de Groot JC; Solouki AM; den Dunnen WFA; Heesters MAAM; Wagemakers M; Verheul HMW; de Vries EGE; Pruim J; Walenkamp AME
    Eur J Nucl Med Mol Imaging; 2018 Dec; 45(13):2404-2412. PubMed ID: 30032322
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma.
    Yang D; Rao G; Martinez J; Veeraraghavan A; Rao A
    Med Phys; 2015 Nov; 42(11):6725-35. PubMed ID: 26520762
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation.
    Park YW; Choi D; Park JE; Ahn SS; Kim H; Chang JH; Kim SH; Kim HS; Lee SK
    Sci Rep; 2021 Feb; 11(1):2913. PubMed ID: 33536499
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging.
    Suh CH; Kim HS; Choi YJ; Kim N; Kim SJ
    AJNR Am J Neuroradiol; 2013 Dec; 34(12):2278-86. PubMed ID: 23828115
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Long-term follow-up results of concomitant chemoradiotherapy followed by adjuvant temozolomide therapy for glioblastoma multiforme patients. The importance of MRI information in survival: Single-center experience.
    Lukács G; Tóth Z; Sipos D; Csima M; Hadjiev J; Bajzik G; Cselik Z; Semjén D; Repa I; Kovács Á
    Ideggyogy Sz; 2018 Mar; 71(3-04):95-103. PubMed ID: 29889468
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Characterization of pseudoprogression in patients with glioblastoma: is histology the gold standard?
    Melguizo-Gavilanes I; Bruner JM; Guha-Thakurta N; Hess KR; Puduvalli VK
    J Neurooncol; 2015 May; 123(1):141-50. PubMed ID: 25894594
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of pseudoprogression in patients with glioblastoma multiforme using dynamic magnetic resonance imaging with ferumoxytol calls RANO criteria into question.
    Nasseri M; Gahramanov S; Netto JP; Fu R; Muldoon LL; Varallyay C; Hamilton BE; Neuwelt EA
    Neuro Oncol; 2014 Aug; 16(8):1146-54. PubMed ID: 24523362
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging.
    Choi YJ; Kim HS; Jahng GH; Kim SJ; Suh DC
    Acta Radiol; 2013 May; 54(4):448-54. PubMed ID: 23592805
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Pseudoprogression in glioblastoma patients: the impact of extent of resection.
    Park HH; Roh TH; Kang SG; Kim EH; Hong CK; Kim SH; Ahn SS; Lee SK; Choi HJ; Cho J; Kim SH; Lee KS; Suh CO; Chang JH
    J Neurooncol; 2016 Feb; 126(3):559-66. PubMed ID: 26608521
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.
    Lee J; Wang N; Turk S; Mohammed S; Lobo R; Kim J; Liao E; Camelo-Piragua S; Kim M; Junck L; Bapuraj J; Srinivasan A; Rao A
    Sci Rep; 2020 Nov; 10(1):20331. PubMed ID: 33230285
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.