BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

174 related articles for article (PubMed ID: 36176070)

  • 21. Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.
    Cluceru J; Interian Y; Phillips JJ; Molinaro AM; Luks TL; Alcaide-Leon P; Olson MP; Nair D; LaFontaine M; Shai A; Chunduru P; Pedoia V; Villanueva-Meyer JE; Chang SM; Lupo JM
    Neuro Oncol; 2022 Apr; 24(4):639-652. PubMed ID: 34653254
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.
    Liu Y; Zheng Z; Wang Z; Qian X; Yao Z; Cheng C; Zhou Z; Gao F; Dai Y
    Quant Imaging Med Surg; 2023 Apr; 13(4):2143-2155. PubMed ID: 37064376
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.
    Fukuma R; Yanagisawa T; Kinoshita M; Shinozaki T; Arita H; Kawaguchi A; Takahashi M; Narita Y; Terakawa Y; Tsuyuguchi N; Okita Y; Nonaka M; Moriuchi S; Takagaki M; Fujimoto Y; Fukai J; Izumoto S; Ishibashi K; Nakajima Y; Shofuda T; Kanematsu D; Yoshioka E; Kodama Y; Mano M; Mori K; Ichimura K; Kanemura Y; Kishima H
    Sci Rep; 2019 Dec; 9(1):20311. PubMed ID: 31889117
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.
    Zhao J; Huang Y; Song Y; Xie D; Hu M; Qiu H; Chu J
    Eur Radiol; 2020 Aug; 30(8):4664-4674. PubMed ID: 32193643
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas.
    Park CJ; Han K; Kim H; Ahn SS; Choi YS; Park YW; Chang JH; Kim SH; Jain R; Lee SK
    Eur Radiol; 2020 Dec; 30(12):6464-6474. PubMed ID: 32740813
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.
    Choi YS; Bae S; Chang JH; Kang SG; Kim SH; Kim J; Rim TH; Choi SH; Jain R; Lee SK
    Neuro Oncol; 2021 Feb; 23(2):304-313. PubMed ID: 32706862
    [TBL] [Abstract][Full Text] [Related]  

  • 27. The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas.
    Niu L; Feng WH; Duan CF; Liu YC; Liu JH; Liu XJ
    Biomed Res Int; 2020; 2020():4630218. PubMed ID: 33163535
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.
    Li Y; Liu X; Qian Z; Sun Z; Xu K; Wang K; Fan X; Zhang Z; Li S; Wang Y; Jiang T
    Eur Radiol; 2018 Jul; 28(7):2960-2968. PubMed ID: 29404769
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas.
    Arita H; Kinoshita M; Kawaguchi A; Takahashi M; Narita Y; Terakawa Y; Tsuyuguchi N; Okita Y; Nonaka M; Moriuchi S; Takagaki M; Fujimoto Y; Fukai J; Izumoto S; Ishibashi K; Nakajima Y; Shofuda T; Kanematsu D; Yoshioka E; Kodama Y; Mano M; Mori K; Ichimura K; Kanemura Y
    Sci Rep; 2018 Aug; 8(1):11773. PubMed ID: 30082856
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Comparison of Genetic Profiles and Prognosis of High-Grade Gliomas Using Quantitative and Qualitative MRI Features: A Focus on G3 Gliomas.
    Hong EK; Choi SH; Shin DJ; Jo SW; Yoo RE; Kang KM; Yun TJ; Kim JH; Sohn CH; Park SH; Won JK; Kim TM; Park CK; Kim IH; Lee ST
    Korean J Radiol; 2021 Feb; 22(2):233-242. PubMed ID: 32932560
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.
    Kandalgaonkar P; Sahu A; Saju AC; Joshi A; Mahajan A; Thakur M; Sahay A; Epari S; Sinha S; Dasgupta A; Chatterjee A; Shetty P; Moiyadi A; Agarwal J; Gupta T; Goda JS
    Front Oncol; 2022; 12():879376. PubMed ID: 36276136
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Diagnostic Performance of [
    Nakajo K; Uda T; Kawashima T; Terakawa Y; Ishibashi K; Tsuyuguchi N; Tanoue Y; Nagahama A; Uda H; Koh S; Sasaki T; Ohata K; Kanemura Y; Goto T
    World Neurosurg; 2021 Apr; 148():e471-e481. PubMed ID: 33444827
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.
    Aliotta E; Dutta SW; Feng X; Tustison NJ; Batchala PP; Schiff D; Lopes MB; Jain R; Druzgal TJ; Mukherjee S; Patel SH
    J Neurooncol; 2020 Sep; 149(2):325-335. PubMed ID: 32909115
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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]  

  • 36. Use of telomerase promoter mutations to mark specific molecular subsets with reciprocal clinical behavior in IDH mutant and IDH wild-type diffuse gliomas.
    Akyerli CB; Yüksel Ş; Can Ö; Erson-Omay EZ; Oktay Y; Coşgun E; Ülgen E; Erdemgil Y; Sav A; von Deimling A; Günel M; Yakıcıer MC; Pamir MN; Özduman K
    J Neurosurg; 2018 Apr; 128(4):1102-1114. PubMed ID: 28621624
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas.
    Buz-Yalug B; Turhan G; Cetin AI; Dindar SS; Danyeli AE; Yakicier C; Pamir MN; Özduman K; Dincer A; Ozturk-Isik E
    Eur J Radiol; 2024 Jan; 170():111257. PubMed ID: 38134710
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features.
    Peng H; Huo J; Li B; Cui Y; Zhang H; Zhang L; Ma L
    J Magn Reson Imaging; 2021 May; 53(5):1399-1407. PubMed ID: 33179832
    [TBL] [Abstract][Full Text] [Related]  

  • 39. World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient.
    Maynard J; Okuchi S; Wastling S; Busaidi AA; Almossawi O; Mbatha W; Brandner S; Jaunmuktane Z; Koc AM; Mancini L; Jäger R; Thust S
    Radiology; 2020 Jul; 296(1):111-121. PubMed ID: 32315266
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Characteristics of the isocitrate dehydrogenase gene and telomerase reverse transcriptase promoter mutations in gliomas in Chinese patients.
    Qu CX; Ji HM; Shi XC; Bi H; Zhai LQ; Han DW
    Brain Behav; 2020 Apr; 10(4):e01583. PubMed ID: 32146731
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.