BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

141 related articles for article (PubMed ID: 34508971)

  • 1. Ensemble based machine learning approach for prediction of glioma and multi-grade classification.
    Chandra Joshi R; Mishra R; Gandhi P; Pathak VK; Burget R; Dutta MK
    Comput Biol Med; 2021 Oct; 137():104829. PubMed ID: 34508971
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
    Zhang X; Yan LF; Hu YC; Li G; Yang Y; Han Y; Sun YZ; Liu ZC; Tian Q; Han ZY; Liu LD; Hu BQ; Qiu ZY; Wang W; Cui GB
    Oncotarget; 2017 Jul; 8(29):47816-47830. PubMed ID: 28599282
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma.
    Cao H; Erson-Omay EZ; Li X; Günel M; Moliterno J; Fulbright RK
    Eur Radiol; 2020 Jun; 30(6):3073-3082. PubMed ID: 32025832
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Glioma grading using a machine-learning framework based on optimized features obtained from T
    Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
    J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.
    Sudre CH; Panovska-Griffiths J; Sanverdi E; Brandner S; Katsaros VK; Stranjalis G; Pizzini FB; Ghimenton C; Surlan-Popovic K; Avsenik J; Spampinato MV; Nigro M; Chatterjee AR; Attye A; Grand S; Krainik A; Anzalone N; Conte GM; Romeo V; Ugga L; Elefante A; Ciceri EF; Guadagno E; Kapsalaki E; Roettger D; Gonzalez J; Boutelier T; Cardoso MJ; Bisdas S
    BMC Med Inform Decis Mak; 2020 Jul; 20(1):149. PubMed ID: 32631306
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
    Buchlak QD; Esmaili N; Leveque JC; Bennett C; Farrokhi F; Piccardi M
    J Clin Neurosci; 2021 Jul; 89():177-198. PubMed ID: 34119265
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.
    Zhao J; Huang Y; Song Y; Xie D; Hu M; Qiu H; Chu J
    Eur Radiol; 2020 Aug; 30(8):4664-4674. PubMed ID: 32193643
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study.
    Ma C; Wang L; Song D; Gao C; Jing L; Lu Y; Liu D; Man W; Yang K; Meng Z; Zhang H; Xue P; Zhang Y; Guo F; Wang G
    BMC Med; 2023 May; 21(1):198. PubMed ID: 37248527
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.
    De Looze C; Beausang A; Cryan J; Loftus T; Buckley PG; Farrell M; Looby S; Reilly R; Brett F; Kearney H
    J Neurooncol; 2018 Sep; 139(2):491-499. PubMed ID: 29770897
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development and validation of a clinical prediction model for glioma grade using machine learning.
    Wu M; Luan J; Zhang D; Fan H; Qiao L; Zhang C
    Technol Health Care; 2024; 32(3):1977-1990. PubMed ID: 38306068
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.
    Ertosun MG; Rubin DL
    AMIA Annu Symp Proc; 2015; 2015():1899-908. PubMed ID: 26958289
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
    Hashido T; Saito S; Ishida T
    J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):606-613. PubMed ID: 34270479
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.
    Citak-Er F; Firat Z; Kovanlikaya I; Ture U; Ozturk-Isik E
    Comput Biol Med; 2018 Aug; 99():154-160. PubMed ID: 29933126
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An ensemble learning approach for brain cancer detection exploiting radiomic features.
    Brunese L; Mercaldo F; Reginelli A; Santone A
    Comput Methods Programs Biomed; 2020 Mar; 185():105134. PubMed ID: 31675644
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.
    Kang J; Ullah Z; Gwak J
    Sensors (Basel); 2021 Mar; 21(6):. PubMed ID: 33810176
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI.
    Zahoor MM; Qureshi SA; Bibi S; Khan SH; Khan A; Ghafoor U; Bhutta MR
    Sensors (Basel); 2022 Apr; 22(7):. PubMed ID: 35408340
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.
    Ranjith G; Parvathy R; Vikas V; Chandrasekharan K; Nair S
    Neuroradiol J; 2015 Apr; 28(2):106-11. PubMed ID: 25923676
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks.
    Niu B; Liang C; Lu Y; Zhao M; Chen Q; Zhang Y; Zheng L; Chou KC
    Genomics; 2020 Jan; 112(1):837-847. PubMed ID: 31150762
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Radiomics strategy for glioma grading using texture features from multiparametric MRI.
    Tian Q; Yan LF; Zhang X; Zhang X; Hu YC; Han Y; Liu ZC; Nan HY; Sun Q; Sun YZ; Yang Y; Yu Y; Zhang J; Hu B; Xiao G; Chen P; Tian S; Xu J; Wang W; Cui GB
    J Magn Reson Imaging; 2018 Dec; 48(6):1518-1528. PubMed ID: 29573085
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.