BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

404 related articles for article (PubMed ID: 30277442)

  • 1. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction.
    Bae S; Choi YS; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
    Radiology; 2018 Dec; 289(3):797-806. PubMed ID: 30277442
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models.
    Choi Y; Nam Y; Jang J; Shin NY; Lee YS; Ahn KJ; Kim BS; Park JS; Jeon SS; Hong YG
    Eur Radiol; 2021 Apr; 31(4):2084-2093. PubMed ID: 33006658
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.
    Choi YS; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Jain R; Lee SK
    Eur Radiol; 2020 Jul; 30(7):3834-3842. PubMed ID: 32162004
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.
    Kickingereder P; Burth S; Wick A; Götz M; Eidel O; Schlemmer HP; Maier-Hein KH; Wick W; Bendszus M; Radbruch A; Bonekamp D
    Radiology; 2016 Sep; 280(3):880-9. PubMed ID: 27326665
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.
    Lu Y; Patel M; Natarajan K; Ughratdar I; Sanghera P; Jena R; Watts C; Sawlani V
    Magn Reson Imaging; 2020 Dec; 74():161-170. PubMed ID: 32980505
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma.
    Kickingereder P; Neuberger U; Bonekamp D; Piechotta PL; Götz M; Wick A; Sill M; Kratz A; Shinohara RT; Jones DTW; Radbruch A; Muschelli J; Unterberg A; Debus J; Schlemmer HP; Herold-Mende C; Pfister S; von Deimling A; Wick W; Capper D; Maier-Hein KH; Bendszus M
    Neuro Oncol; 2018 May; 20(6):848-857. PubMed ID: 29036412
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.
    Chaddad A; Sabri S; Niazi T; Abdulkarim B
    Med Biol Eng Comput; 2018 Dec; 56(12):2287-2300. PubMed ID: 29915951
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.
    Chen X; Fang M; Dong D; Liu L; Xu X; Wei X; Jiang X; Qin L; Liu Z
    Acad Radiol; 2019 Oct; 26(10):1292-1300. PubMed ID: 30660472
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Apparent Diffusion Coefficient as a Predictive Biomarker for Survival in Patients with Treatment-Naive Glioblastoma Using Quantitative Multiparametric Magnetic Resonance Profiling.
    Kim BS; Kim ST; Kim JH; Seol HJ; Nam DH; Shin HJ; Lee JI; Kong DS
    World Neurosurg; 2019 Feb; 122():e812-e820. PubMed ID: 30391622
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma.
    Ingrisch M; Schneider MJ; Nörenberg D; Negrao de Figueiredo G; Maier-Hein K; Suchorska B; Schüller U; Albert N; Brückmann H; Reiser M; Tonn JC; Ertl-Wagner B
    Invest Radiol; 2017 Jun; 52(6):360-366. PubMed ID: 28079702
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.
    Prasanna P; Patel J; Partovi S; Madabhushi A; Tiwari P
    Eur Radiol; 2017 Oct; 27(10):4188-4197. PubMed ID: 27778090
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma.
    Kim JY; Yoon MJ; Park JE; Choi EJ; Lee J; Kim HS
    Neuroradiology; 2019 Nov; 61(11):1261-1272. PubMed ID: 31289886
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics.
    Choi Y; Ahn KJ; Nam Y; Jang J; Shin NY; Choi HS; Jung SL; Kim BS
    Eur J Radiol; 2019 Nov; 120():108642. PubMed ID: 31546124
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas.
    Park CJ; Han K; Kim H; Ahn SS; Choi YS; Park YW; Chang JH; Kim SH; Jain R; Lee SK
    Eur Radiol; 2020 Dec; 30(12):6464-6474. PubMed ID: 32740813
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma.
    Choi YS; Ahn SS; Kim DW; Chang JH; Kang SG; Kim EH; Kim SH; Rim TH; Lee SK
    Radiology; 2016 Oct; 281(1):175-84. PubMed ID: 27120357
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of Core Signaling Pathway by Using Diffusion- and Perfusion-based MRI Radiomics and Next-generation Sequencing in Isocitrate Dehydrogenase Wild-type Glioblastoma.
    Park JE; Kim HS; Park SY; Nam SJ; Chun SM; Jo Y; Kim JH
    Radiology; 2020 Feb; 294(2):388-397. PubMed ID: 31845844
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.
    Mes SW; van Velden FHP; Peltenburg B; Peeters CFW; Te Beest DE; van de Wiel MA; Mekke J; Mulder DC; Martens RM; Castelijns JA; Pameijer FA; de Bree R; Boellaard R; Leemans CR; Brakenhoff RH; de Graaf P
    Eur Radiol; 2020 Nov; 30(11):6311-6321. PubMed ID: 32500196
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Improving survival prediction of high-grade glioma via machine learning techniques based on MRI radiomic, genetic and clinical risk factors.
    Tan Y; Mu W; Wang XC; Yang GQ; Gillies RJ; Zhang H
    Eur J Radiol; 2019 Nov; 120():108609. PubMed ID: 31606714
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients.
    Peeken JC; Hesse J; Haller B; Kessel KA; Nüsslin F; Combs SE
    Strahlenther Onkol; 2018 Jun; 194(6):580-590. PubMed ID: 29442128
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.