BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

471 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 24.