952 related articles for article (PubMed ID: 30910431)
1. Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.
Vamvakas A; Williams SC; Theodorou K; Kapsalaki E; Fountas K; Kappas C; Vassiou K; Tsougos I
Phys Med; 2019 Apr; 60():188-198. PubMed ID: 30910431
[TBL] [Abstract][Full Text] [Related]
2. Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma.
Yang Y; Yan LF; Zhang X; Nan HY; Hu YC; Han Y; Zhang J; Liu ZC; Sun YZ; Tian Q; Yu Y; Sun Q; Wang SY; Zhang X; Wang W; Cui GB
J Magn Reson Imaging; 2019 May; 49(5):1263-1274. PubMed ID: 30623514
[TBL] [Abstract][Full Text] [Related]
3. Radiomics strategy for glioma grading using texture features from multiparametric MRI.
Tian Q; Yan LF; Zhang X; Zhang X; Hu YC; Han Y; Liu ZC; Nan HY; Sun Q; Sun YZ; Yang Y; Yu Y; Zhang J; Hu B; Xiao G; Chen P; Tian S; Xu J; Wang W; Cui GB
J Magn Reson Imaging; 2018 Dec; 48(6):1518-1528. PubMed ID: 29573085
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. Glioma grading using a machine-learning framework based on optimized features obtained from T
Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
[TBL] [Abstract][Full Text] [Related]
8. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.
Sengupta A; Agarwal S; Gupta PK; Ahlawat S; Patir R; Gupta RK; Singh A
Eur J Radiol; 2018 Sep; 106():199-208. PubMed ID: 30150045
[TBL] [Abstract][Full Text] [Related]
9. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
Hashido T; Saito S; Ishida T
J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):606-613. PubMed ID: 34270479
[TBL] [Abstract][Full Text] [Related]
10. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.
Sauwen N; Acou M; Van Cauter S; Sima DM; Veraart J; Maes F; Himmelreich U; Achten E; Van Huffel S
Neuroimage Clin; 2016; 12():753-764. PubMed ID: 27812502
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.
Citak-Er F; Firat Z; Kovanlikaya I; Ture U; Ozturk-Isik E
Comput Biol Med; 2018 Aug; 99():154-160. PubMed ID: 29933126
[TBL] [Abstract][Full Text] [Related]
13. Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.
Yuan J; Siakallis L; Li HB; Brandner S; Zhang J; Li C; Mancini L; Bisdas S
BMC Med Imaging; 2024 May; 24(1):104. PubMed ID: 38702613
[TBL] [Abstract][Full Text] [Related]
14. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging.
Lin K; Cidan W; Qi Y; Wang X
Med Phys; 2022 Jul; 49(7):4419-4429. PubMed ID: 35366379
[TBL] [Abstract][Full Text] [Related]
15. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging.
Zhang X; Xu X; Tian Q; Li B; Wu Y; Yang Z; Liang Z; Liu Y; Cui G; Lu H
J Magn Reson Imaging; 2017 Nov; 46(5):1281-1288. PubMed ID: 28199039
[TBL] [Abstract][Full Text] [Related]
16. Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.
Qin JB; Liu Z; Zhang H; Shen C; Wang XC; Tan Y; Wang S; Wu XF; Tian J
Med Sci Monit; 2017 May; 23():2168-2178. PubMed ID: 28478462
[TBL] [Abstract][Full Text] [Related]
17. Data-driven grading of brain gliomas: a multiparametric MR imaging study.
Caulo M; Panara V; Tortora D; Mattei PA; Briganti C; Pravatà E; Salice S; Cotroneo AR; Tartaro A
Radiology; 2014 Aug; 272(2):494-503. PubMed ID: 24661247
[TBL] [Abstract][Full Text] [Related]
18. The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas.
Alis D; Bagcilar O; Senli YD; Isler C; Yergin M; Kocer N; Islak C; Kizilkilic O
Clin Radiol; 2020 May; 75(5):351-357. PubMed ID: 31973941
[TBL] [Abstract][Full Text] [Related]
19. Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma.
Fan M; Liu Z; Xie S; Xu M; Wang S; Gao X; Li L
Phys Med Biol; 2019 Oct; 64(21):215001. PubMed ID: 31470420
[TBL] [Abstract][Full Text] [Related]
20. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]