188 related articles for article (PubMed ID: 33657924)
1. Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.
Priya S; Ward C; Locke T; Soni N; Maheshwarappa RP; Monga V; Agarwal A; Bathla G
Neuroradiol J; 2021 Aug; 34(4):320-328. PubMed ID: 33657924
[TBL] [Abstract][Full Text] [Related]
2. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.
Bathla G; Priya S; Liu Y; Ward C; Le NH; Soni N; Maheshwarappa RP; Monga V; Zhang H; Sonka M
Eur Radiol; 2021 Nov; 31(11):8703-8713. PubMed ID: 33890149
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models.
Priya S; Agarwal A; Ward C; Locke T; Monga V; Bathla G
Neuroradiol J; 2021 Aug; 34(4):355-362. PubMed ID: 33533273
[TBL] [Abstract][Full Text] [Related]
6. Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients.
Mehrnahad M; Rostami S; Kimia F; Kord R; Taheri MS; Rad HS; Haghighatkhah H; Moradi A; Kord A
Neuroradiol J; 2020 Oct; 33(5):428-436. PubMed ID: 32628089
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation.
Kang D; Park JE; Kim YH; Kim JH; Oh JY; Kim J; Kim Y; Kim ST; Kim HS
Neuro Oncol; 2018 Aug; 20(9):1251-1261. PubMed ID: 29438500
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma.
Wang BT; Liu MX; Chen ZY
Chin Med Sci J; 2019 Mar; 34(1):10-17. PubMed ID: 30961775
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis.
Joo B; Ahn SS; An C; Han K; Choi D; Kim H; Park JE; Kim HS; Lee SK
J Neuroradiol; 2023 Jun; 50(4):388-395. PubMed ID: 36370829
[TBL] [Abstract][Full Text] [Related]
14. Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.
Xia W; Hu B; Li H; Geng C; Wu Q; Yang L; Yin B; Gao X; Li Y; Geng D
J Magn Reson Imaging; 2021 Jan; 53(1):242-250. PubMed ID: 32864825
[TBL] [Abstract][Full Text] [Related]
15. An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.
Tateishi M; Nakaura T; Kitajima M; Uetani H; Nakagawa M; Inoue T; Kuroda JI; Mukasa A; Yamashita Y
J Neurol Sci; 2020 Mar; 410():116514. PubMed ID: 31869660
[TBL] [Abstract][Full Text] [Related]
16. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis.
Xiao DD; Yan PF; Wang YX; Osman MS; Zhao HY
Clin Neurol Neurosurg; 2018 Oct; 173():84-90. PubMed ID: 30092408
[TBL] [Abstract][Full Text] [Related]
17. Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.
Park JE; Ham S; Kim HS; Park SY; Yun J; Lee H; Choi SH; Kim N
Eur Radiol; 2021 May; 31(5):3127-3137. PubMed ID: 33128598
[TBL] [Abstract][Full Text] [Related]
18. Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.
Alcaide-Leon P; Dufort P; Geraldo AF; Alshafai L; Maralani PJ; Spears J; Bharatha A
AJNR Am J Neuroradiol; 2017 Jun; 38(6):1145-1150. PubMed ID: 28450433
[TBL] [Abstract][Full Text] [Related]
19. Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma.
Lin X; Lee M; Buck O; Woo KM; Zhang Z; Hatzoglou V; Omuro A; Arevalo-Perez J; Thomas AA; Huse J; Peck K; Holodny AI; Young RJ
AJNR Am J Neuroradiol; 2017 Mar; 38(3):485-491. PubMed ID: 27932505
[TBL] [Abstract][Full Text] [Related]
20. Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.
Kandalgaonkar P; Sahu A; Saju AC; Joshi A; Mahajan A; Thakur M; Sahay A; Epari S; Sinha S; Dasgupta A; Chatterjee A; Shetty P; Moiyadi A; Agarwal J; Gupta T; Goda JS
Front Oncol; 2022; 12():879376. PubMed ID: 36276136
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
