890 related articles for article (PubMed ID: 29626238)
1. 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]
2. 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]
3. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient.
Choi YS; Lee HJ; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Eur Radiol; 2017 Apr; 27(4):1344-1351. PubMed ID: 27436023
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging.
You SH; Yun TJ; Choi HJ; Yoo RE; Kang KM; Choi SH; Kim JH; Sohn CH
Eur Radiol; 2018 Sep; 28(9):3801-3810. PubMed ID: 29619520
[TBL] [Abstract][Full Text] [Related]
7. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.
Chen Y; Li Z; Wu G; Yu J; Wang Y; Lv X; Ju X; Chen Z
Int J Neurosci; 2018 Jul; 128(7):608-618. PubMed ID: 29183170
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.
Xia W; Hu B; Li H; Shi W; Tang Y; Yu Y; Geng C; Wu Q; Yang L; Yu Z; Geng D; Li Y
J Magn Reson Imaging; 2021 Sep; 54(3):880-887. PubMed ID: 33694250
[TBL] [Abstract][Full Text] [Related]
11. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.
Nguyen AV; Blears EE; Ross E; Lall RR; Ortega-Barnett J
Neurosurg Focus; 2018 Nov; 45(5):E5. PubMed ID: 30453459
[TBL] [Abstract][Full Text] [Related]
12.
Zhou W; Wen J; Hua F; Xu W; Lu X; Yin B; Geng D; Guan Y
Eur J Radiol; 2018 Jul; 104():26-32. PubMed ID: 29857862
[TBL] [Abstract][Full Text] [Related]
13. Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach.
Bae S; Choi YS; Sohn B; Ahn SS; Lee SK; Yang J; Kim J
Yonsei Med J; 2020 Oct; 61(10):895-900. PubMed ID: 32975065
[TBL] [Abstract][Full Text] [Related]
14. Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.
Saini J; Kumar Gupta P; Awasthi A; Pandey CM; Singh A; Patir R; Ahlawat S; Sadashiva N; Mahadevan A; Kumar Gupta R
Clin Radiol; 2018 Nov; 73(11):986.e7-986.e15. PubMed ID: 30197047
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.
Chen X; Fang M; Dong D; Liu L; Xu X; Wei X; Jiang X; Qin L; Liu Z
Acad Radiol; 2019 Oct; 26(10):1292-1300. PubMed ID: 30660472
[TBL] [Abstract][Full Text] [Related]
18. Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient.
Bao S; Watanabe Y; Takahashi H; Tanaka H; Arisawa A; Matsuo C; Wu R; Fujimoto Y; Tomiyama N
Magn Reson Med Sci; 2019 Jan; 18(1):53-61. PubMed ID: 29848919
[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. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T
Sun YZ; Yan LF; Han Y; Nan HY; Xiao G; Tian Q; Pu WH; Li ZY; Wei XC; Wang W; Cui GB
BMC Med Imaging; 2021 Feb; 21(1):17. PubMed ID: 33535988
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
