283 related articles for article (PubMed ID: 30498643)
1. Classification of the glioma grading using radiomics analysis.
Cho HH; Lee SH; Kim J; Park H
PeerJ; 2018; 6():e5982. PubMed ID: 30498643
[TBL] [Abstract][Full Text] [Related]
2. Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.
Kumar A; Jha AK; Agarwal JP; Yadav M; Badhe S; Sahay A; Epari S; Sahu A; Bhattacharya K; Chatterjee A; Ganeshan B; Rangarajan V; Moyiadi A; Gupta T; Goda JS
J Pers Med; 2023 May; 13(6):. PubMed ID: 37373909
[TBL] [Abstract][Full Text] [Related]
3. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
[TBL] [Abstract][Full Text] [Related]
4. Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study.
Ding J; Zhao R; Qiu Q; Chen J; Duan J; Cao X; Yin Y
Quant Imaging Med Surg; 2022 Feb; 12(2):1517-1528. PubMed ID: 35111644
[TBL] [Abstract][Full Text] [Related]
5. Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model.
Du P; Liu X; Wu X; Chen J; Cao A; Geng D
Brain Sci; 2023 Jun; 13(6):. PubMed ID: 37371390
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.
Moodi F; Khodadadi Shoushtari F; Ghadimi DJ; Valizadeh G; Khormali E; Salari HM; Ohadi MAD; Nilipour Y; Jahanbakhshi A; Rad HS
J Magn Reson Imaging; 2023 Nov; ():. PubMed ID: 38031466
[TBL] [Abstract][Full Text] [Related]
8. 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]
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. 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]
11. Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features.
Ning Z; Luo J; Xiao Q; Cai L; Chen Y; Yu X; Wang J; Zhang Y
Ann Transl Med; 2021 Feb; 9(4):298. PubMed ID: 33708925
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach.
Zhang L; Liu X; Xu X; Liu W; Jia Y; Chen W; Fu X; Li Q; Sun X; Zhang Y; Shu S; Zhang X; Xiang R; Chen H; Sun P; Geng D; Yu Z; Liu J; Wang J
Eur J Radiol; 2023 Jan; 158():110639. PubMed ID: 36463703
[TBL] [Abstract][Full Text] [Related]
15. [Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model].
He H; Guo E; Meng W; Wang Y; Wang W; He W; Wu Y; Yang W
Nan Fang Yi Ke Da Xue Xue Bao; 2024 Jan; 44(1):194-200. PubMed ID: 38293992
[TBL] [Abstract][Full Text] [Related]
16. Classification of low-grade and high-grade glioma using multi-modal image radiomics features.
Hwan-Ho Cho ; Hyunjin Park
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():3081-3084. PubMed ID: 29060549
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.
Tang Y; Yang CM; Su S; Wang WJ; Fan LP; Shu J
BMC Cancer; 2021 Nov; 21(1):1268. PubMed ID: 34819043
[TBL] [Abstract][Full Text] [Related]
19. Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features.
Deng DB; Liao YT; Zhou JF; Cheng LN; He P; Wu SN; Wang WS; Zhou Q
Front Neurol; 2022; 13():866274. PubMed ID: 35585843
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]