235 related articles for article (PubMed ID: 31921635)
1. Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach.
Zhang Y; Chen C; Cheng Y; Teng Y; Guo W; Xu H; Ou X; Wang J; Li H; Ma X; Xu J
Front Oncol; 2019; 9():1371. PubMed ID: 31921635
[No Abstract] [Full Text] [Related]
2. Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma.
Fan Y; Chen C; Zhao F; Tian Z; Wang J; Ma X; Xu J
Front Oncol; 2019; 9():1164. PubMed ID: 31750250
[No Abstract] [Full Text] [Related]
3. Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques.
Chen B; Chen C; Wang J; Teng Y; Ma X; Xu J
Front Oncol; 2021; 11():521313. PubMed ID: 34141605
[TBL] [Abstract][Full Text] [Related]
4. Use of radiomics based on
Zhou Y; Ma XL; Zhang T; Wang J; Zhang T; Tian R
Eur J Nucl Med Mol Imaging; 2021 Aug; 48(9):2904-2913. PubMed ID: 33547553
[TBL] [Abstract][Full Text] [Related]
5. Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.
Chen C; Zheng A; Ou X; Wang J; Ma X
Front Oncol; 2020; 10():1151. PubMed ID: 33042784
[No Abstract] [Full Text] [Related]
6. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.
Dai H; Lu M; Huang B; Tang M; Pang T; Liao B; Cai H; Huang M; Zhou Y; Chen X; Ding H; Feng ST
Quant Imaging Med Surg; 2021 May; 11(5):1836-1853. PubMed ID: 33936969
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study.
Chen C; Guo X; Wang J; Guo W; Ma X; Xu J
Front Oncol; 2019; 9():1338. PubMed ID: 31867272
[No Abstract] [Full Text] [Related]
9. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.
Zhao SS; Feng XL; Hu YC; Han Y; Tian Q; Sun YZ; Zhang J; Ge XW; Cheng SC; Li XL; Mao L; Shen SN; Yan LF; Cui GB; Wang W
BMC Neurol; 2020 Feb; 20(1):48. PubMed ID: 32033580
[TBL] [Abstract][Full Text] [Related]
10. The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.
Teng Y; Chen C; Zhang Y; Xu J
Transl Cancer Res; 2022 Nov; 11(11):4079-4088. PubMed ID: 36523299
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
[TBL] [Abstract][Full Text] [Related]
14. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
Yin P; Mao N; Zhao C; Wu J; Sun C; Chen L; Hong N
Eur Radiol; 2019 Apr; 29(4):1841-1847. PubMed ID: 30280245
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.
Tsuchiya M; Masui T; Terauchi K; Yamada T; Katyayama M; Ichikawa S; Noda Y; Goshima S
Eur Radiol; 2022 Jun; 32(6):4090-4100. PubMed ID: 35044510
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study.
Shang L; Wang F; Gao Y; Zhou C; Wang J; Chen X; Chughtai AR; Pu H; Zhang G; Kong W
Front Oncol; 2022; 12():1043163. PubMed ID: 36505817
[TBL] [Abstract][Full Text] [Related]
20. Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.
Liu Y; Zheng Z; Wang Z; Qian X; Yao Z; Cheng C; Zhou Z; Gao F; Dai Y
Quant Imaging Med Surg; 2023 Apr; 13(4):2143-2155. PubMed ID: 37064376
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]