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 *

279 related articles for article (PubMed ID: 31226327)

  • 1. Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis.
    Li X; Lu Y; Xiong J; Wang D; She D; Kuai X; Geng D; Yin B
    J Neuroradiol; 2019 Sep; 46(5):281-287. PubMed ID: 31226327
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.
    Dong J; Yu M; Miao Y; Shen H; Sui Y; Liu Y; Han L; Li X; Lin M; Guo Y; Xie L
    Biomed Res Int; 2020; 2020():5042356. PubMed ID: 33344637
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Preoperative Prediction of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Magnetic Resonance Imaging Texture Analysis.
    Kanazawa T; Minami Y; Jinzaki M; Toda M; Yoshida K; Sasaki H
    World Neurosurg; 2018 Dec; 120():e1208-e1216. PubMed ID: 30240864
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.
    Park YW; Oh J; You SC; Han K; Ahn SS; Choi YS; Chang JH; Kim SH; Lee SK
    Eur Radiol; 2019 Aug; 29(8):4068-4076. PubMed ID: 30443758
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Whole-tumor histogram analysis of apparent diffusion coefficient in differentiating intracranial solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma.
    He W; Xiao X; Li X; Guo Y; Guo L; Liu X; Xu Y; Zhou J; Wu Y
    Eur J Radiol; 2019 Mar; 112():186-191. PubMed ID: 30777209
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.
    Hamerla G; Meyer HJ; Schob S; Ginat DT; Altman A; Lim T; Gihr GA; Horvath-Rizea D; Hoffmann KT; Surov A
    Magn Reson Imaging; 2019 Nov; 63():244-249. PubMed ID: 31425811
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest.
    Lu Y; Liu L; Luan S; Xiong J; Geng D; Yin B
    Eur Radiol; 2019 Mar; 29(3):1318-1328. PubMed ID: 30088065
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Differentiating intracranial solitary fibrous tumor/hemangiopericytoma from meningioma using diffusion-weighted imaging and susceptibility-weighted imaging.
    Chen T; Jiang B; Zheng Y; She D; Zhang H; Xing Z; Cao D
    Neuroradiology; 2020 Feb; 62(2):175-184. PubMed ID: 31673748
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.
    Laukamp KR; Shakirin G; Baeßler B; Thiele F; Zopfs D; Große Hokamp N; Timmer M; Kabbasch C; Perkuhn M; Borggrefe J
    World Neurosurg; 2019 Dec; 132():e366-e390. PubMed ID: 31476455
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI.
    Han Y; Wang T; Wu P; Zhang H; Chen H; Yang C
    Magn Reson Imaging; 2021 Apr; 77():36-43. PubMed ID: 33220449
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A deep learning radiomics model for preoperative grading in meningioma.
    Zhu Y; Man C; Gong L; Dong D; Yu X; Wang S; Fang M; Wang S; Fang X; Chen X; Tian J
    Eur J Radiol; 2019 Jul; 116():128-134. PubMed ID: 31153553
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Signal value difference between white matter and tumor parenchyma in T1- and T2- weighted images may help differentiating solitary fibrous tumor/ hemangiopericytoma and angiomatous meningioma.
    He L; Li B; Song X; Yu S
    Clin Neurol Neurosurg; 2020 Nov; 198():106221. PubMed ID: 32947194
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of ADC values of intracranial hemangiopericytomas and angiomatous and anaplastic meningiomas.
    Liu L; Yin B; Geng DY; Li Y; Zhang BY; Peng WJ
    J Neuroradiol; 2014 Jul; 41(3):188-94. PubMed ID: 24524869
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI.
    Hu J; Zhao Y; Li M; Liu J; Wang F; Weng Q; Wang X; Cao D
    Eur J Radiol; 2020 Oct; 131():109251. PubMed ID: 32916409
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A radiomics-based study for differentiating parasellar cavernous hemangiomas from meningiomas.
    Wang C; You L; Zhang X; Zhu Y; Zheng L; Huang W; Guo D; Dong Y
    Sci Rep; 2022 Sep; 12(1):15509. PubMed ID: 36109577
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.
    Hale AT; Stonko DP; Wang L; Strother MK; Chambless LB
    Neurosurg Focus; 2018 Nov; 45(5):E4. PubMed ID: 30453458
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An investigation of machine learning methods in delta-radiomics feature analysis.
    Chang Y; Lafata K; Sun W; Wang C; Chang Z; Kirkpatrick JP; Yin FF
    PLoS One; 2019; 14(12):e0226348. PubMed ID: 31834910
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.
    Cepeda S; Arrese I; García-García S; Velasco-Casares M; Escudero-Caro T; Zamora T; Sarabia R
    World Neurosurg; 2021 Feb; 146():e1147-e1159. PubMed ID: 33259973
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area.
    Mori N; Mugikura S; Endo T; Endo H; Oguma Y; Li L; Ito A; Watanabe M; Kanamori M; Tominaga T; Takase K
    Neuroradiology; 2023 Feb; 65(2):257-274. PubMed ID: 36044063
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Clinical and Radiographic Features for Differentiating Solitary Fibrous Tumor/Hemangiopericytoma From Meningioma.
    Ohba S; Murayama K; Nishiyama Y; Adachi K; Yamada S; Abe M; Hasegawa M; Hirose Y
    World Neurosurg; 2019 Oct; 130():e383-e392. PubMed ID: 31233926
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.