BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

420 related articles for article (PubMed ID: 31001929)

  • 1. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.
    Liao X; Cai B; Tian B; Luo Y; Song W; Li Y
    J Cell Mol Med; 2019 Jun; 23(6):4375-4385. PubMed ID: 31001929
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Noninvasive O
    Hajianfar G; Shiri I; Maleki H; Oveisi N; Haghparast A; Abdollahi H; Oveisi M
    World Neurosurg; 2019 Dec; 132():e140-e161. PubMed ID: 31505292
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning.
    Sanghani P; Ang BT; King NKK; Ren H
    Surg Oncol; 2018 Dec; 27(4):709-714. PubMed ID: 30449497
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.
    Suter Y; Knecht U; Alão M; Valenzuela W; Hewer E; Schucht P; Wiest R; Reyes M
    Cancer Imaging; 2020 Aug; 20(1):55. PubMed ID: 32758279
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction.
    Zhou M; Chaudhury B; Hall LO; Goldgof DB; Gillies RJ; Gatenby RA
    J Magn Reson Imaging; 2017 Jul; 46(1):115-123. PubMed ID: 27678245
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.
    Li ZC; Bai H; Sun Q; Li Q; Liu L; Zou Y; Chen Y; Liang C; Zheng H
    Eur Radiol; 2018 Sep; 28(9):3640-3650. PubMed ID: 29564594
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Radiogenomics model for overall survival prediction of glioblastoma.
    Wijethilake N; Islam M; Ren H
    Med Biol Eng Comput; 2020 Aug; 58(8):1767-1777. PubMed ID: 32488372
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.
    Sanghani P; Ang BT; King NKK; Ren H
    Med Biol Eng Comput; 2019 Aug; 57(8):1683-1691. PubMed ID: 31104273
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients.
    Sanghani P; Ti AB; Kam King NK; Ren H
    Surg Oncol; 2019 Jun; 29():178-183. PubMed ID: 31196485
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma.
    Fathi Kazerooni A; Akbari H; Shukla G; Badve C; Rudie JD; Sako C; Rathore S; Bakas S; Pati S; Singh A; Bergman M; Ha SM; Kontos D; Nasrallah M; Bagley SJ; Lustig RA; O'Rourke DM; Sloan AE; Barnholtz-Sloan JS; Mohan S; Bilello M; Davatzikos C
    JCO Clin Cancer Inform; 2020 Mar; 4():234-244. PubMed ID: 32191542
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.
    Chaddad A; Desrosiers C; Hassan L; Tanougast C
    Br J Radiol; 2016 Dec; 89(1068):20160575. PubMed ID: 27781499
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time.
    Chaddad A; Daniel P; Desrosiers C; Toews M; Abdulkarim B
    IEEE J Biomed Health Inform; 2019 Mar; 23(2):795-804. PubMed ID: 29993848
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival.
    Zhang X; Lu H; Tian Q; Feng N; Yin L; Xu X; Du P; Liu Y
    Eur Radiol; 2019 Oct; 29(10):5528-5538. PubMed ID: 30847586
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma.
    Beig N; Bera K; Prasanna P; Antunes J; Correa R; Singh S; Saeed Bamashmos A; Ismail M; Braman N; Verma R; Hill VB; Statsevych V; Ahluwalia MS; Varadan V; Madabhushi A; Tiwari P
    Clin Cancer Res; 2020 Apr; 26(8):1866-1876. PubMed ID: 32079590
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
    Nakagawa M; Nakaura T; Namimoto T; Kitajima M; Uetani H; Tateishi M; Oda S; Utsunomiya D; Makino K; Nakamura H; Mukasa A; Hirai T; Yamashita Y
    Eur J Radiol; 2018 Nov; 108():147-154. PubMed ID: 30396648
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.