BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

154 related articles for article (PubMed ID: 37948373)

  • 1. Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma.
    Zheng F; Chen B; Zhang L; Chen H; Zang Y; Chen X; Li Y
    J Comput Assist Tomogr; 2023 Nov-Dec 01; 47(6):967-972. PubMed ID: 37948373
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.
    Li Y; Liang Y; Sun Z; Xu K; Fan X; Li S; Zhang Z; Jiang T; Liu X; Wang Y
    Neuroradiology; 2019 Nov; 61(11):1229-1237. PubMed ID: 31218383
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas.
    Sun Z; Li Y; Wang Y; Fan X; Xu K; Wang K; Li S; Zhang Z; Jiang T; Liu X
    Cancer Imaging; 2019 Oct; 19(1):68. PubMed ID: 31639060
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI.
    Zhang H; Zhang H; Zhang Y; Zhou B; Wu L; Lei Y; Huang B
    J Magn Reson Imaging; 2023 Nov; 58(5):1441-1451. PubMed ID: 36896953
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis.
    Liu Z; Jiang Z; Meng L; Yang J; Liu Y; Zhang Y; Peng H; Li J; Xiao G; Zhang Z; Zhou R
    J Oncol; 2021; 2021():5518717. PubMed ID: 34188680
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.
    Yan Q; Li F; Cui Y; Wang Y; Wang X; Jia W; Liu X; Li Y; Chang H; Shi F; Xia Y; Zhou Q; Zeng Q
    J Digit Imaging; 2023 Aug; 36(4):1480-1488. PubMed ID: 37156977
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MRI-based machine learning models predict the malignant biological behavior of meningioma.
    Li M; Liu L; Qi J; Qiao Y; Zeng H; Jiang W; Zhu R; Chen F; Huang H; Wu S
    BMC Med Imaging; 2023 Sep; 23(1):141. PubMed ID: 37759192
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
    Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
    Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T
    Sun YZ; Yan LF; Han Y; Nan HY; Xiao G; Tian Q; Pu WH; Li ZY; Wei XC; Wang W; Cui GB
    BMC Med Imaging; 2021 Feb; 21(1):17. PubMed ID: 33535988
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: a preliminary investigation in terms of identification and segmentation.
    Assefa D; Keller H; Ménard C; Laperriere N; Ferrari RJ; Yeung I
    Med Phys; 2010 Apr; 37(4):1722-36. PubMed ID: 20443493
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.
    Sakai Y; Yang C; Kihira S; Tsankova N; Khan F; Hormigo A; Lai A; Cloughesy T; Nael K
    Int J Mol Sci; 2020 Oct; 21(21):. PubMed ID: 33121211
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models.
    Zinn PO; Singh SK; Kotrotsou A; Hassan I; Thomas G; Luedi MM; Elakkad A; Elshafeey N; Idris T; Mosley J; Gumin J; Fuller GN; de Groot JF; Baladandayuthapani V; Sulman EP; Kumar AJ; Sawaya R; Lang FF; Piwnica-Worms D; Colen RR
    Clin Cancer Res; 2018 Dec; 24(24):6288-6299. PubMed ID: 30054278
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.
    Kandemirli SG; Kocak B; Naganawa S; Ozturk K; Yip SSF; Chopra S; Rivetti L; Aldine AS; Jones K; Cayci Z; Moritani T; Sato TS
    World Neurosurg; 2021 Jul; 151():e78-e85. PubMed ID: 33819703
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting epidermal growth factor receptor gene amplification status in glioblastoma multiforme by quantitative enhancement and necrosis features deriving from conventional magnetic resonance imaging.
    Dong F; Zeng Q; Jiang B; Yu X; Wang W; Xu J; Yu J; Li Q; Zhang M
    Medicine (Baltimore); 2018 May; 97(21):e10833. PubMed ID: 29794775
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.
    Guan F; Wang Z; Qiu Y; Guo Y; Pei D; Wang M; Xing A; Liu Z; Yu B; Cheng J; Liu X; Ji Y; Yan D; Yan J; Zhang Z
    J Transl Med; 2023 Nov; 21(1):841. PubMed ID: 37993907
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Magnetic resonance imaging and deoxyribonucleic acid methylation-based radiogenomic models for survival risk stratification of glioblastoma.
    Zhang W; Yan Z; Peng J; Zhao S; Ran L; Yin H; Zhong D; Yang J; Ye J; Xu S
    Med Biol Eng Comput; 2024 Mar; 62(3):853-864. PubMed ID: 38057447
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.
    Lee MH; Kim J; Kim ST; Shin HM; You HJ; Choi JW; Seol HJ; Nam DH; Lee JI; Kong DS
    World Neurosurg; 2019 May; 125():e688-e696. PubMed ID: 30735871
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.