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.


Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

202 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.