282 related articles for article (PubMed ID: 32758279)
1. Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.
Suter Y; Knecht U; Alão M; Valenzuela W; Hewer E; Schucht P; Wiest R; Reyes M
Cancer Imaging; 2020 Aug; 20(1):55. PubMed ID: 32758279
[TBL] [Abstract][Full Text] [Related]
2. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.
Liao X; Cai B; Tian B; Luo Y; Song W; Li Y
J Cell Mol Med; 2019 Jun; 23(6):4375-4385. PubMed ID: 31001929
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
[TBL] [Abstract][Full Text] [Related]
5. Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.
Sanghani P; Ang BT; King NKK; Ren H
Med Biol Eng Comput; 2019 Aug; 57(8):1683-1691. PubMed ID: 31104273
[TBL] [Abstract][Full Text] [Related]
6. Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma.
Kim JY; Yoon MJ; Park JE; Choi EJ; Lee J; Kim HS
Neuroradiology; 2019 Nov; 61(11):1261-1272. PubMed ID: 31289886
[TBL] [Abstract][Full Text] [Related]
7. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.
Kim JY; Park JE; Jo Y; Shim WH; Nam SJ; Kim JH; Yoo RE; Choi SH; Kim HS
Neuro Oncol; 2019 Feb; 21(3):404-414. PubMed ID: 30107606
[TBL] [Abstract][Full Text] [Related]
8. Noninvasive O
Hajianfar G; Shiri I; Maleki H; Oveisi N; Haghparast A; Abdollahi H; Oveisi M
World Neurosurg; 2019 Dec; 132():e140-e161. PubMed ID: 31505292
[TBL] [Abstract][Full Text] [Related]
9. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.
Prasanna P; Patel J; Partovi S; Madabhushi A; Tiwari P
Eur Radiol; 2017 Oct; 27(10):4188-4197. PubMed ID: 27778090
[TBL] [Abstract][Full Text] [Related]
10. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation.
Bae S; An C; Ahn SS; Kim H; Han K; Kim SW; Park JE; Kim HS; Lee SK
Sci Rep; 2020 Jul; 10(1):12110. PubMed ID: 32694637
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma.
Li ZC; Bai H; Sun Q; Zhao Y; Lv Y; Zhou J; Liang C; Chen Y; Liang D; Zheng H
Cancer Med; 2018 Dec; 7(12):5999-6009. PubMed ID: 30426720
[TBL] [Abstract][Full Text] [Related]
13. Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets.
Um H; Tixier F; Bermudez D; Deasy JO; Young RJ; Veeraraghavan H
Phys Med Biol; 2019 Aug; 64(16):165011. PubMed ID: 31272093
[TBL] [Abstract][Full Text] [Related]
14. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.
Chaddad A; Sabri S; Niazi T; Abdulkarim B
Med Biol Eng Comput; 2018 Dec; 56(12):2287-2300. PubMed ID: 29915951
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction.
Bae S; Choi YS; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Radiology; 2018 Dec; 289(3):797-806. PubMed ID: 30277442
[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. 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]
19. Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics.
Choi Y; Ahn KJ; Nam Y; Jang J; Shin NY; Choi HS; Jung SL; Kim BS
Eur J Radiol; 2019 Nov; 120():108642. PubMed ID: 31546124
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]