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 *

554 related articles for article (PubMed ID: 30623514)

  • 1. Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma.
    Yang Y; Yan LF; Zhang X; Nan HY; Hu YC; Han Y; Zhang J; Liu ZC; Sun YZ; Tian Q; Yu Y; Sun Q; Wang SY; Zhang X; Wang W; Cui GB
    J Magn Reson Imaging; 2019 May; 49(5):1263-1274. PubMed ID: 30623514
    [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. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low-Grade Gliomas Using Multiparametric MR Radiomic Features.
    Ren Y; Zhang X; Rui W; Pang H; Qiu T; Wang J; Xie Q; Jin T; Zhang H; Chen H; Zhang Y; Lu H; Yao Z; Zhang J; Feng X
    J Magn Reson Imaging; 2019 Mar; 49(3):808-817. PubMed ID: 30194745
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI.
    Zhang X; Tian Q; Wang L; Liu Y; Li B; Liang Z; Gao P; Zheng K; Zhao B; Lu H
    J Magn Reson Imaging; 2018 Oct; 48(4):916-926. PubMed ID: 29394005
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.
    Xie T; Chen X; Fang J; Kang H; Xue W; Tong H; Cao P; Wang S; Yang Y; Zhang W
    J Magn Reson Imaging; 2018 Apr; 47(4):1099-1111. PubMed ID: 28845594
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging.
    Zhang X; Xu X; Tian Q; Li B; Wu Y; Yang Z; Liang Z; Liu Y; Cui G; Lu H
    J Magn Reson Imaging; 2017 Nov; 46(5):1281-1288. PubMed ID: 28199039
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
    Inano R; Oishi N; Kunieda T; Arakawa Y; Yamao Y; Shibata S; Kikuchi T; Fukuyama H; Miyamoto S
    Neuroimage Clin; 2014; 5():396-407. PubMed ID: 25180159
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis.
    Xu X; Zhang X; Tian Q; Wang H; Cui LB; Li S; Tang X; Li B; Dolz J; Ayed IB; Liang Z; Yuan J; Du P; Lu H; Liu Y
    J Magn Reson Imaging; 2019 May; 49(5):1489-1498. PubMed ID: 30252978
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis.
    Liu Y; Zhang X; Feng N; Yin L; He Y; Xu X; Lu H
    Acta Radiol; 2018 Oct; 59(10):1239-1246. PubMed ID: 29430935
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Association of Glioma Grading With Inflow-Based Vascular-Space-Occupancy MRI: A Preliminary Study at 3T.
    Li X; Liao S; Hua J; Guo L; Wang D; Xiao X; Zhou J; Liu X; Tan Y; Lu L; Xu Y; Wu Y
    J Magn Reson Imaging; 2019 Dec; 50(6):1817-1823. PubMed ID: 30932289
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy.
    Rui W; Pang H; Xie Q; Wang Y; Duan S; Ren Y; Yao Z
    J Comput Assist Tomogr; 2021 Sep-Oct 01; 45(5):728-735. PubMed ID: 34347700
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.
    Bisdas S; Shen H; Thust S; Katsaros V; Stranjalis G; Boskos C; Brandner S; Zhang J
    Sci Rep; 2018 Apr; 8(1):6108. PubMed ID: 29666413
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Quantitative glioma grading using transformed gray-scale invariant textures of MRI.
    Li-Chun Hsieh K; Chen CY; Lo CM
    Comput Biol Med; 2017 Apr; 83():102-108. PubMed ID: 28254615
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis.
    Wu M; Krishna S; Thornhill RE; Flood TA; McInnes MDF; Schieda N
    J Magn Reson Imaging; 2019 Sep; 50(3):940-950. PubMed ID: 30701625
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.
    Alis D; Bagcilar O; Senli YD; Yergin M; Isler C; Kocer N; Islak C; Kizilkilic O
    Jpn J Radiol; 2020 Feb; 38(2):135-143. PubMed ID: 31741126
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma.
    Bhattacharjee R; Gupta RK; Patir R; Vaishya S; Ahlawat S; Singh A
    J Magn Reson Imaging; 2020 Jan; 51(1):225-233. PubMed ID: 31087724
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 28.