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 *

242 related articles for article (PubMed ID: 33890149)

  • 1. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.
    Bathla G; Priya S; Liu Y; Ward C; Le NH; Soni N; Maheshwarappa RP; Monga V; Zhang H; Sonka M
    Eur Radiol; 2021 Nov; 31(11):8703-8713. PubMed ID: 33890149
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.
    Xia W; Hu B; Li H; Geng C; Wu Q; Yang L; Yin B; Gao X; Li Y; Geng D
    J Magn Reson Imaging; 2021 Jan; 53(1):242-250. PubMed ID: 32864825
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
    Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
    Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.
    Priya S; Ward C; Locke T; Soni N; Maheshwarappa RP; Monga V; Agarwal A; Bathla G
    Neuroradiol J; 2021 Aug; 34(4):320-328. PubMed ID: 33657924
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?
    Priya S; Liu Y; Ward C; Le NH; Soni N; Pillenahalli Maheshwarappa R; Monga V; Zhang H; Sonka M; Bathla G
    Cancers (Basel); 2021 May; 13(11):. PubMed ID: 34073840
    [TBL] [Abstract][Full Text] [Related]  

  • 6. AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.
    Bathla G; Dhruba DD; Soni N; Liu Y; Larson NB; Kassmeyer BA; Mohan S; Roberts-Wolfe D; Rathore S; Le NH; Zhang H; Sonka M; Priya S
    J Neuroradiol; 2024 May; 51(3):258-264. PubMed ID: 37652263
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.
    Priya S; Liu Y; Ward C; Le NH; Soni N; Pillenahalli Maheshwarappa R; Monga V; Zhang H; Sonka M; Bathla G
    Sci Rep; 2021 May; 11(1):10478. PubMed ID: 34006893
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.
    Chen Y; Li Z; Wu G; Yu J; Wang Y; Lv X; Ju X; Chen Z
    Int J Neurosci; 2018 Jul; 128(7):608-618. PubMed ID: 29183170
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.
    Xia W; Hu B; Li H; Shi W; Tang Y; Yu Y; Geng C; Wu Q; Yang L; Yu Z; Geng D; Li Y
    J Magn Reson Imaging; 2021 Sep; 54(3):880-887. PubMed ID: 33694250
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.
    Saini J; Kumar Gupta P; Awasthi A; Pandey CM; Singh A; Patir R; Ahlawat S; Sadashiva N; Mahadevan A; Kumar Gupta R
    Clin Radiol; 2018 Nov; 73(11):986.e7-986.e15. PubMed ID: 30197047
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.
    Sim Y; Kim M; Kim J; Lee SK; Han K; Sohn B
    Eur Radiol; 2024 May; 34(5):3102-3112. PubMed ID: 37848774
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
    Wang X; Wan Q; Chen H; Li Y; Li X
    Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation.
    Kang D; Park JE; Kim YH; Kim JH; Oh JY; Kim J; Kim Y; Kim ST; Kim HS
    Neuro Oncol; 2018 Aug; 20(9):1251-1261. PubMed ID: 29438500
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient.
    Choi YS; Lee HJ; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
    Eur Radiol; 2017 Apr; 27(4):1344-1351. PubMed ID: 27436023
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.
    Nguyen AV; Blears EE; Ross E; Lall RR; Ortega-Barnett J
    Neurosurg Focus; 2018 Nov; 45(5):E5. PubMed ID: 30453459
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation.
    Bhattacharjee R; Gupta M; Singh T; Sharma S; Khanna G; Parvaze SP; Patir R; Vaishya S; Ahlawat S; Singh A; Gupta RK
    Neuroradiology; 2022 Sep; 64(9):1801-1818. PubMed ID: 35435463
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Detecting Double Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning.
    Liu G; Zhang X; Zhang N; Xiao H; Chen X; Ma L
    J Magn Reson Imaging; 2024 Jan; 59(1):231-239. PubMed ID: 37199225
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma.
    Wang X; Zhao L; Wang S; Zhao X; Chen L; Sun X; Liu Y; Liu J; Sun S
    J Neurooncol; 2024 Feb; 166(3):451-460. PubMed ID: 38308802
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation.
    Park YW; Choi D; Park JE; Ahn SS; Kim H; Chang JH; Kim SH; Kim HS; Lee SK
    Sci Rep; 2021 Feb; 11(1):2913. PubMed ID: 33536499
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.