147 related articles for article (PubMed ID: 27758687)
1. Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.
Yang Z; Feng P; Wen T; Wan M; Hong X
CNS Neurol Disord Drug Targets; 2017; 16(2):160-168. PubMed ID: 27758687
[TBL] [Abstract][Full Text] [Related]
2. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Shrot S; Salhov M; Dvorski N; Konen E; Averbuch A; Hoffmann C
Neuroradiology; 2019 Jul; 61(7):757-765. PubMed ID: 30949746
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.
Kunimatsu A; Kunimatsu N; Yasaka K; Akai H; Kamiya K; Watadani T; Mori H; Abe O
Magn Reson Med Sci; 2019 Jan; 18(1):44-52. PubMed ID: 29769456
[TBL] [Abstract][Full Text] [Related]
4. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.
Chen Y; Li Z; Wu G; Yu J; Wang Y; Lv X; Ju X; Chen Z
Int J Neurosci; 2018 Jul; 128(7):608-618. PubMed ID: 29183170
[TBL] [Abstract][Full Text] [Related]
5. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.
Artzi M; Bressler I; Ben Bashat D
J Magn Reson Imaging; 2019 Aug; 50(2):519-528. PubMed ID: 30635952
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
Kim Y; Cho HH; Kim ST; Park H; Nam D; Kong DS
Neuroradiology; 2018 Dec; 60(12):1297-1305. PubMed ID: 30232517
[TBL] [Abstract][Full Text] [Related]
8. Differentiation between treatment-related changes and progressive disease in patients with high grade brain tumors using support vector machine classification based on DCE MRI.
Artzi M; Liberman G; Nadav G; Blumenthal DT; Bokstein F; Aizenstein O; Ben Bashat D
J Neurooncol; 2016 May; 127(3):515-24. PubMed ID: 26754857
[TBL] [Abstract][Full Text] [Related]
9. Utility of dynamic contrast-enhanced magnetic resonance imaging for differentiating glioblastoma, primary central nervous system lymphoma and brain metastatic tumor.
Lu S; Gao Q; Yu J; Li Y; Cao P; Shi H; Hong X
Eur J Radiol; 2016 Oct; 85(10):1722-1727. PubMed ID: 27666608
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Clinical Value of Vascular Permeability Estimates Using Dynamic Susceptibility Contrast MRI: Improved Diagnostic Performance in Distinguishing Hypervascular Primary CNS Lymphoma from Glioblastoma.
Lee B; Park JE; Bjørnerud A; Kim JH; Lee JY; Kim HS
AJNR Am J Neuroradiol; 2018 Aug; 39(8):1415-1422. PubMed ID: 30026384
[TBL] [Abstract][Full Text] [Related]
12. Differentiating Glioblastoma from Primary Central Nervous System Lymphoma: The Value of Shaping and Nonenhancing Peritumoral Hyperintense Gyral Lesion on FLAIR Imaging.
Wang P; Shi YH; Li JY; Zhang CZ
World Neurosurg; 2021 May; 149():e696-e704. PubMed ID: 33548537
[TBL] [Abstract][Full Text] [Related]
13. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction.
Zhou M; Chaudhury B; Hall LO; Goldgof DB; Gillies RJ; Gatenby RA
J Magn Reson Imaging; 2017 Jul; 46(1):115-123. PubMed ID: 27678245
[TBL] [Abstract][Full Text] [Related]
14. Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study.
Ismail M; Hill V; Statsevych V; Huang R; Prasanna P; Correa R; Singh G; Bera K; Beig N; Thawani R; Madabhushi A; Aahluwalia M; Tiwari P
AJNR Am J Neuroradiol; 2018 Dec; 39(12):2187-2193. PubMed ID: 30385468
[TBL] [Abstract][Full Text] [Related]
15. Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis.
Yang G; Jones TL; Howe FA; Barrick TR
Magn Reson Med; 2016 Jun; 75(6):2505-16. PubMed ID: 26173745
[TBL] [Abstract][Full Text] [Related]
16. Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation.
Qian X; Tan H; Zhang J; Zhao W; Chan MD; Zhou X
Med Phys; 2016 Nov; 43(11):5889. PubMed ID: 27806598
[TBL] [Abstract][Full Text] [Related]
17. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.
Nguyen AV; Blears EE; Ross E; Lall RR; Ortega-Barnett J
Neurosurg Focus; 2018 Nov; 45(5):E5. PubMed ID: 30453459
[TBL] [Abstract][Full Text] [Related]
18. Non-invasive assessment of intratumoral vascularity using arterial spin labeling: A comparison to susceptibility-weighted imaging for the differentiation of primary cerebral lymphoma and glioblastoma.
Furtner J; Schöpf V; Preusser M; Asenbaum U; Woitek R; Wöhrer A; Hainfellner JA; Wolfsberger S; Prayer D
Eur J Radiol; 2014 May; 83(5):806-10. PubMed ID: 24613549
[TBL] [Abstract][Full Text] [Related]
19. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma.
Yun J; Park JE; Lee H; Ham S; Kim N; Kim HS
Sci Rep; 2019 Apr; 9(1):5746. PubMed ID: 30952930
[TBL] [Abstract][Full Text] [Related]
20. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers.
Dong F; Li Q; Jiang B; Zhu X; Zeng Q; Huang P; Chen S; Zhang M
Eur Radiol; 2020 May; 30(5):3015-3022. PubMed ID: 32006166
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
