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 *

216 related articles for article (PubMed ID: 28520730)

  • 1. Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma.
    Booth TC; Larkin TJ; Yuan Y; Kettunen MI; Dawson SN; Scoffings D; Canuto HC; Vowler SL; Kirschenlohr H; Hobson MP; Markowetz F; Jefferies S; Brindle KM
    PLoS One; 2017; 12(5):e0176528. PubMed ID: 28520730
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study.
    Ismail M; Hill V; Statsevych V; Huang R; Prasanna P; Correa R; Singh G; Bera K; Beig N; Thawani R; Madabhushi A; Aahluwalia M; Tiwari P
    AJNR Am J Neuroradiol; 2018 Dec; 39(12):2187-2193. PubMed ID: 30385468
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals.
    Choi Y; Ahn KJ; Nam Y; Jang J; Shin NY; Choi HS; Jung SL; Kim BS
    PLoS One; 2019; 14(5):e0217785. PubMed ID: 31150499
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers.
    Le Fèvre C; Constans JM; Chambrelant I; Antoni D; Bund C; Leroy-Freschini B; Schott R; Cebula H; Noël G
    Crit Rev Oncol Hematol; 2021 Mar; 159():103230. PubMed ID: 33515701
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison between the Prebolus T1 Measurement and the Fixed T1 Value in Dynamic Contrast-Enhanced MR Imaging for the Differentiation of True Progression from Pseudoprogression in Glioblastoma Treated with Concurrent Radiation Therapy and Temozolomide Chemotherapy.
    Nam JG; Kang KM; Choi SH; Lim WH; Yoo RE; Kim JH; Yun TJ; Sohn CH
    AJNR Am J Neuroradiol; 2017 Dec; 38(12):2243-2250. PubMed ID: 29074633
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis.
    Liu Y; Zhang X; Feng N; Yin L; He Y; Xu X; Lu H
    Acta Radiol; 2018 Oct; 59(10):1239-1246. PubMed ID: 29430935
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Diffusion-weighted MR imaging for the differentiation of true progression from pseudoprogression following concomitant radiotherapy with temozolomide in patients with newly diagnosed high-grade gliomas.
    Lee WJ; Choi SH; Park CK; Yi KS; Kim TM; Lee SH; Kim JH; Sohn CH; Park SH; Kim IH
    Acad Radiol; 2012 Nov; 19(11):1353-61. PubMed ID: 22884399
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promoter methylation.
    Yoon RG; Kim HS; Paik W; Shim WH; Kim SJ; Kim JH
    Eur Radiol; 2017 Jan; 27(1):255-266. PubMed ID: 27048531
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis.
    Liu Y; Xu X; Yin L; Zhang X; Li L; Lu H
    AJNR Am J Neuroradiol; 2017 Sep; 38(9):1695-1701. PubMed ID: 28663266
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Visual inspection of MR relative cerebral blood volume maps has limited value for distinguishing progression from pseudoprogression in glioblastoma multiforme patients.
    Kerkhof M; Tans PL; Hagenbeek RE; Lycklama À Nijeholt GJ; Holla FK; Postma TJ; Straathof CS; Dirven L; Taphoorn MJ; Vos MJ
    CNS Oncol; 2017 Oct; 6(4):297-306. PubMed ID: 28984142
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma.
    Elshafeey N; Kotrotsou A; Hassan A; Elshafei N; Hassan I; Ahmed S; Abrol S; Agarwal A; El Salek K; Bergamaschi S; Acharya J; Moron FE; Law M; Fuller GN; Huse JT; Zinn PO; Colen RR
    Nat Commun; 2019 Jul; 10(1):3170. PubMed ID: 31320621
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.
    Artzi M; Bressler I; Ben Bashat D
    J Magn Reson Imaging; 2019 Aug; 50(2):519-528. PubMed ID: 30635952
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning.
    Thenier-Villa JL; Martínez-Ricarte FR; Figueroa-Vezirian M; Arikan-Abelló F
    World Neurosurg; 2024 Mar; 183():e953-e962. PubMed ID: 38253179
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features.
    Le Fèvre C; Lhermitte B; Ahle G; Chambrelant I; Cebula H; Antoni D; Keller A; Schott R; Thiery A; Constans JM; Noël G
    Crit Rev Oncol Hematol; 2021 Jan; 157():103188. PubMed ID: 33307200
    [TBL] [Abstract][Full Text] [Related]  

  • 20. True progression versus pseudoprogression in the treatment of glioblastomas: a comparison study of normalized cerebral blood volume and apparent diffusion coefficient by histogram analysis.
    Song YS; Choi SH; Park CK; Yi KS; Lee WJ; Yun TJ; Kim TM; Lee SH; Kim JH; Sohn CH; Park SH; Kim IH; Jahng GH; Chang KH
    Korean J Radiol; 2013; 14(4):662-72. PubMed ID: 23901325
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.