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 *

111 related articles for article (PubMed ID: 39252245)

  • 1. Prediction of Glioma enhancement pattern using a MRI radiomics-based model.
    Wang W; Wang Y; Meng W; Guo E; He H; Huang G; He W; Wu Y
    Medicine (Baltimore); 2024 Sep; 103(36):e39512. PubMed ID: 39252245
    [TBL] [Abstract][Full Text] [Related]  

  • 2. [Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model].
    He H; Guo E; Meng W; Wang Y; Wang W; He W; Wu Y; Yang W
    Nan Fang Yi Ke Da Xue Xue Bao; 2024 Jan; 44(1):194-200. PubMed ID: 38293992
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
    Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
    Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.
    Kandemirli SG; Kocak B; Naganawa S; Ozturk K; Yip SSF; Chopra S; Rivetti L; Aldine AS; Jones K; Cayci Z; Moritani T; Sato TS
    World Neurosurg; 2021 Jul; 151():e78-e85. PubMed ID: 33819703
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.
    Ni J; Zhang H; Yang Q; Fan X; Xu J; Sun J; Zhang J; Hu Y; Xiao Z; Zhao Y; Zhu H; Shi X; Feng W; Wang J; Wan C; Zhang X; Liu Y; You Y; Yu Y
    Acad Radiol; 2024 Aug; 31(8):3397-3405. PubMed ID: 38458887
    [TBL] [Abstract][Full Text] [Related]  

  • 6. T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas.
    Tang WT; Su CQ; Lin J; Xia ZW; Lu SS; Hong XN
    Clin Radiol; 2024 May; 79(5):e750-e758. PubMed ID: 38360515
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.
    Moodi F; Khodadadi Shoushtari F; Ghadimi DJ; Valizadeh G; Khormali E; Salari HM; Ohadi MAD; Nilipour Y; Jahanbakhshi A; Rad HS
    J Magn Reson Imaging; 2024 Sep; 60(3):923-938. PubMed ID: 38031466
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.
    Cao M; Suo S; Zhang X; Wang X; Xu J; Yang W; Zhou Y
    Biomed Res Int; 2021; 2021():1235314. PubMed ID: 33553421
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine learning-based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle.
    Mo H; Liang W; Huang Z; Li X; Xiao X; Liu H; He J; Xu Y; Wu Y
    Eur Radiol; 2023 Jun; 33(6):4259-4269. PubMed ID: 36547672
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Noninvasive prediction of IDH mutation status in gliomas using preoperative multiparametric MRI radiomics nomogram: A mutlicenter study.
    Lu J; Xu W; Chen X; Wang T; Li H
    Magn Reson Imaging; 2023 Dec; 104():72-79. PubMed ID: 37778708
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas.
    Jiang C; Kong Z; Liu S; Feng S; Zhang Y; Zhu R; Chen W; Wang Y; Lyu Y; You H; Zhao D; Wang R; Wang Y; Ma W; Feng F
    Eur J Radiol; 2019 Dec; 121():108714. PubMed ID: 31704598
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.
    Kim M; Jung SY; Park JE; Jo Y; Park SY; Nam SJ; Kim JH; Kim HS
    Eur Radiol; 2020 Apr; 30(4):2142-2151. PubMed ID: 31828414
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting histological grade in pediatric glioma using multiparametric radiomics and conventional MRI features.
    Zhou T; Qiao B; Peng B; Liu Y; Gong Z; Kang M; He Y; Pang C; Dai Y; Sheng M
    Sci Rep; 2024 Jun; 14(1):13683. PubMed ID: 38871755
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study.
    Wang Q; Li Q; Mi R; Ye H; Zhang H; Chen B; Li Y; Huang G; Xia J
    J Magn Reson Imaging; 2019 Mar; 49(3):825-833. PubMed ID: 30260592
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.
    Yuan J; Siakallis L; Li HB; Brandner S; Zhang J; Li C; Mancini L; Bisdas S
    BMC Med Imaging; 2024 May; 24(1):104. PubMed ID: 38702613
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults.
    Lv K; Chen H; Cao X; Du P; Chen J; Liu X; Zhu L; Geng D; Zhang J
    J Neurosurg; 2023 Aug; 139(2):393-401. PubMed ID: 36681946
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.
    Su X; Chen N; Sun H; Liu Y; Yang X; Wang W; Zhang S; Tan Q; Su J; Gong Q; Yue Q
    Neuro Oncol; 2020 Mar; 22(3):393-401. PubMed ID: 31563963
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.
    Sakai Y; Yang C; Kihira S; Tsankova N; Khan F; Hormigo A; Lai A; Cloughesy T; Nael K
    Int J Mol Sci; 2020 Oct; 21(21):. PubMed ID: 33121211
    [TBL] [Abstract][Full Text] [Related]  

  • 20. High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management.
    Li J; Liu S; Qin Y; Zhang Y; Wang N; Liu H
    PLoS One; 2020; 15(1):e0227703. PubMed ID: 31968004
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.