These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
3. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Skogen K; Schulz A; Helseth E; Ganeshan B; Dormagen JB; Server A Acta Radiol; 2019 Mar; 60(3):356-366. PubMed ID: 29860889 [TBL] [Abstract][Full Text] [Related]
4. Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm. Wang L; Wang H; D'Angelo F; Curtin L; Sereduk CP; Leon G; Singleton KW; Urcuyo J; Hawkins-Daarud A; Jackson PR; Krishna C; Zimmerman RS; Patra DP; Bendok BR; Smith KA; Nakaji P; Donev K; Baxter LC; Mrugała MM; Ceccarelli M; Iavarone A; Swanson KR; Tran NL; Hu LS; Li J PLoS One; 2024; 19(4):e0299267. PubMed ID: 38568950 [TBL] [Abstract][Full Text] [Related]
5. 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]
6. The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis. Liu Y; Zhang X; Feng N; Yin L; He Y; Xu X; Lu H Acta Radiol; 2018 Oct; 59(10):1239-1246. PubMed ID: 29430935 [TBL] [Abstract][Full Text] [Related]
7. Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Yang D; Rao G; Martinez J; Veeraraghavan A; Rao A Med Phys; 2015 Nov; 42(11):6725-35. PubMed ID: 26520762 [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. Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. Fathi Kazerooni A; Mohseni M; Rezaei S; Bakhshandehpour G; Saligheh Rad H MAGMA; 2015 Feb; 28(1):13-22. PubMed ID: 24691860 [TBL] [Abstract][Full Text] [Related]
10. Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: a preliminary investigation in terms of identification and segmentation. Assefa D; Keller H; Ménard C; Laperriere N; Ferrari RJ; Yeung I Med Phys; 2010 Apr; 37(4):1722-36. PubMed ID: 20443493 [TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. 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]
15. A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma. Lausch A; Yeung TP; Chen J; Law E; Wang Y; Urbini B; Donelli F; Manco L; Fainardi E; Lee TY; Wong E Med Phys; 2017 Nov; 44(11):6074-6084. PubMed ID: 28875538 [TBL] [Abstract][Full Text] [Related]
16. 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]
17. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. Zhou M; Chaudhury B; Hall LO; Goldgof DB; Gillies RJ; Gatenby RA J Magn Reson Imaging; 2017 Jul; 46(1):115-123. PubMed ID: 27678245 [TBL] [Abstract][Full Text] [Related]
19. Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time. Chaddad A; Daniel P; Desrosiers C; Toews M; Abdulkarim B IEEE J Biomed Health Inform; 2019 Mar; 23(2):795-804. PubMed ID: 29993848 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]