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 *

647 related articles for article (PubMed ID: 29404769)

  • 21. 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]  

  • 22. 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]  

  • 23. 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]  

  • 24. 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]  

  • 25. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis.
    Nakamoto T; Takahashi W; Haga A; Takahashi S; Kiryu S; Nawa K; Ohta T; Ozaki S; Nozawa Y; Tanaka S; Mukasa A; Nakagawa K
    Sci Rep; 2019 Dec; 9(1):19411. PubMed ID: 31857632
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas.
    Zhang X; Liu S; Zhao X; Shi X; Li J; Guo J; Niedermann G; Luo R; Zhang X
    Strahlenther Onkol; 2020 Oct; 196(10):913-921. PubMed ID: 32025804
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.
    Qian Z; Li Y; Sun Z; Fan X; Xu K; Wang K; Li S; Zhang Z; Jiang T; Liu X; Wang Y
    Aging (Albany NY); 2018 Oct; 10(10):2884-2899. PubMed ID: 30362964
    [TBL] [Abstract][Full Text] [Related]  

  • 28. BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models.
    Jiang C; Sun C; Wang X; Ma S; Jia W; Zhang D
    J Imaging Inform Med; 2024 Aug; 37(4):1359-1374. PubMed ID: 38381384
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes.
    Foltyn-Dumitru M; Schell M; Rastogi A; Sahm F; Kessler T; Wick W; Bendszus M; Brugnara G; Vollmuth P
    Eur Radiol; 2024 Apr; 34(4):2782-2790. PubMed ID: 37672053
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.
    Pei D; Guan F; Hong X; Liu Z; Wang W; Qiu Y; Duan W; Wang M; Sun C; Wang W; Wang X; Guo Y; Wang Z; Liu Z; Xing A; Guo Z; Luo L; Liu X; Cheng J; Zhang B; Zhang Z; Yan J
    Eur Radiol; 2023 May; 33(5):3455-3466. PubMed ID: 36853347
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study.
    Cui S; Tang T; Su Q; Wang Y; Shu Z; Yang W; Gong X
    Cancer Imaging; 2021 Mar; 21(1):26. PubMed ID: 33750453
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.
    Ma A; Yan X; Qu Y; Wen H; Zou X; Liu X; Lu M; Mo J; Wen Z
    BMC Med Imaging; 2024 Apr; 24(1):85. PubMed ID: 38600452
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Multi-pool chemical exchange saturation transfer MRI in glioma grading, molecular subtyping and evaluating tumor proliferation.
    Zhu H; Li Y; Ding Y; Liu Y; Shen N; Xie Y; Yan S; Liu D; Zhang X; Li L; Zhu W
    J Neurooncol; 2024 Sep; 169(2):287-297. PubMed ID: 38874844
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.
    Kocak B; Durmaz ES; Ates E; Sel I; Turgut Gunes S; Kaya OK; Zeynalova A; Kilickesmez O
    Eur Radiol; 2020 Feb; 30(2):877-886. PubMed ID: 31691122
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric
    Haubold J; Demircioglu A; Gratz M; Glas M; Wrede K; Sure U; Antoch G; Keyvani K; Nittka M; Kannengiesser S; Gulani V; Griswold M; Herrmann K; Forsting M; Nensa F; Umutlu L
    Eur J Nucl Med Mol Imaging; 2020 Jun; 47(6):1435-1445. PubMed ID: 31811342
    [TBL] [Abstract][Full Text] [Related]  

  • 36. 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]  

  • 37. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma.
    Fan Y; Liu Z; Hou B; Li L; Liu X; Liu Z; Wang R; Lin Y; Feng F; Tian J; Feng M
    Eur J Radiol; 2019 Dec; 121():108647. PubMed ID: 31561943
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.
    Wu S; Meng J; Yu Q; Li P; Fu S
    J Cancer Res Clin Oncol; 2019 Mar; 145(3):543-550. PubMed ID: 30719536
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.
    Vamvakas A; Williams SC; Theodorou K; Kapsalaki E; Fountas K; Kappas C; Vassiou K; Tsougos I
    Phys Med; 2019 Apr; 60():188-198. PubMed ID: 30910431
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
    Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
    Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 33.