187 related articles for article (PubMed ID: 37422554)
1. A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors.
Godlewski A; Czajkowski M; Mojsak P; Pienkowski T; Gosk W; Lyson T; Mariak Z; Reszec J; Kondraciuk M; Kaminski K; Kretowski M; Moniuszko M; Kretowski A; Ciborowski M
Sci Rep; 2023 Jul; 13(1):11044. PubMed ID: 37422554
[TBL] [Abstract][Full Text] [Related]
2. Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema.
Server A; Kulle B; Maehlen J; Josefsen R; Schellhorn T; Kumar T; Langberg CW; Nakstad PH
Acta Radiol; 2009 Jul; 50(6):682-9. PubMed ID: 19449234
[TBL] [Abstract][Full Text] [Related]
3. Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning.
Safari Yazd H; Bazargani SF; Fitzpatrick G; Yost RA; Kresak J; Garrett TJ
J Am Soc Mass Spectrom; 2023 Oct; 34(10):2187-2198. PubMed ID: 37708056
[TBL] [Abstract][Full Text] [Related]
4. Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas.
Hussain NS; Moisi MD; Keogh B; McCullough BJ; Rostad S; Newell D; Gwinn R; Foltz G; Mayberg M; Aguedan B; Good V; Fouke SJ
J Neurosurg; 2017 Apr; 126(4):1220-1226. PubMed ID: 27285539
[TBL] [Abstract][Full Text] [Related]
5. Using R2* values to evaluate brain tumours on magnetic resonance imaging: preliminary results.
Liu Z; Liao H; Yin J; Li Y
Eur Radiol; 2014 Mar; 24(3):693-702. PubMed ID: 24275803
[TBL] [Abstract][Full Text] [Related]
6. Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques.
Svolos P; Tsolaki E; Kapsalaki E; Theodorou K; Fountas K; Fezoulidis I; Tsougos I
Magn Reson Imaging; 2013 Nov; 31(9):1567-77. PubMed ID: 23906533
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Malignancy of brain tumors evaluated by proton magnetic resonance spectroscopy (1H-MRS) in vitro.
Czernicki Z; Horsztyński D; Jankowski W; Grieb P; Walecki J
Acta Neurochir Suppl; 2000; 76():17-20. PubMed ID: 11449999
[TBL] [Abstract][Full Text] [Related]
9. Serum Levels of APRIL Increase in Patients with Glioma, Meningioma and Schwannoma.
Fouladseresht H; Ziaee SM; Erfani N; Doroudchi M
Asian Pac J Cancer Prev; 2019 Mar; 20(3):751-756. PubMed ID: 30909681
[TBL] [Abstract][Full Text] [Related]
10. Automated segmentation of MR images of brain tumors.
Kaus MR; Warfield SK; Nabavi A; Black PM; Jolesz FA; Kikinis R
Radiology; 2001 Feb; 218(2):586-91. PubMed ID: 11161183
[TBL] [Abstract][Full Text] [Related]
11. ARID4B is a good biomarker to predict tumour behaviour and decide WHO grades in gliomas and meningiomas.
Tsai WC; Hueng DY; Nieh S; Gao HW
J Clin Pathol; 2017 Feb; 70(2):162-167. PubMed ID: 27451434
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Distinguishing chordoid meningiomas from their histologic mimics: an immunohistochemical evaluation.
Sangoi AR; Dulai MS; Beck AH; Brat DJ; Vogel H
Am J Surg Pathol; 2009 May; 33(5):669-81. PubMed ID: 19194275
[TBL] [Abstract][Full Text] [Related]
14. Tumor extension in high-grade gliomas assessed with diffusion magnetic resonance imaging: values and lesion-to-brain ratios of apparent diffusion coefficient and fractional anisotropy.
van Westen D; Lätt J; Englund E; Brockstedt S; Larsson EM
Acta Radiol; 2006 Apr; 47(3):311-9. PubMed ID: 16613314
[TBL] [Abstract][Full Text] [Related]
15. A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.
Banzato T; Bernardini M; Cherubini GB; Zotti A
BMC Vet Res; 2018 Oct; 14(1):317. PubMed ID: 30348148
[TBL] [Abstract][Full Text] [Related]
16. Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging.
Provenzale JM; McGraw P; Mhatre P; Guo AC; Delong D
Radiology; 2004 Aug; 232(2):451-60. PubMed ID: 15215555
[TBL] [Abstract][Full Text] [Related]
17. Quantification of fibrin degradation products in glioma and meningioma patients.
Can O; Erdemgil Y; Yildirim ZZ; Ozduman K; Pamir MN; Sav A; Ozpinar A
Cancer Biomark; 2014; 14(4):253-8. PubMed ID: 24934368
[TBL] [Abstract][Full Text] [Related]
18. Nrf2 Expressions Correlate with WHO Grades in Gliomas and Meningiomas.
Tsai WC; Hueng DY; Lin CR; Yang TC; Gao HW
Int J Mol Sci; 2016 May; 17(5):. PubMed ID: 27187376
[TBL] [Abstract][Full Text] [Related]
19. Distinct peak at 3.8 ppm observed by 3T MR spectroscopy in meningiomas, while nearly absent in high-grade gliomas and cerebral metastases.
Kousi E; Tsougos I; Fountas K; Theodorou K; Tsolaki E; Fezoulidis I; Kapsalaki E
Mol Med Rep; 2012 Apr; 5(4):1011-8. PubMed ID: 22293950
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]