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.
10. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. Liu J; Mao Y; Li Z; Zhang D; Zhang Z; Hao S; Li B J Magn Reson Imaging; 2016 Aug; 44(2):445-55. PubMed ID: 26778191 [TBL] [Abstract][Full Text] [Related]
11. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. Juntu J; Sijbers J; De Backer S; Rajan J; Van Dyck D J Magn Reson Imaging; 2010 Mar; 31(3):680-9. PubMed ID: 20187212 [TBL] [Abstract][Full Text] [Related]
12. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Ortiz-Ramón R; Larroza A; Ruiz-España S; Arana E; Moratal D Eur Radiol; 2018 Nov; 28(11):4514-4523. PubMed ID: 29761357 [TBL] [Abstract][Full Text] [Related]
13. Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors. Rodriguez Gutierrez D; Awwad A; Meijer L; Manita M; Jaspan T; Dineen RA; Grundy RG; Auer DP AJNR Am J Neuroradiol; 2014 May; 35(5):1009-15. PubMed ID: 24309122 [TBL] [Abstract][Full Text] [Related]
14. Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas. Gu Z; Dai W; Chen J; Jiang Q; Lin W; Wang Q; Chen J; Gu C; Li J; Ying G; Zhu Y BMC Cancer; 2024 Mar; 24(1):350. PubMed ID: 38504164 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. Artzi M; Bressler I; Ben Bashat D J Magn Reson Imaging; 2019 Aug; 50(2):519-528. PubMed ID: 30635952 [TBL] [Abstract][Full Text] [Related]
17. fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations. Vu H; Kim HC; Jung M; Lee JH Neuroimage; 2020 Dec; 223():117328. PubMed ID: 32896633 [TBL] [Abstract][Full Text] [Related]
18. Imaging biomarker analysis of advanced multiparametric MRI for glioma grading. Vamvakas A; Williams SC; Theodorou K; Kapsalaki E; Fountas K; Kappas C; Vassiou K; Tsougos I Phys Med; 2019 Apr; 60():188-198. PubMed ID: 30910431 [TBL] [Abstract][Full Text] [Related]
19. The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas. Alis D; Bagcilar O; Senli YD; Isler C; Yergin M; Kocer N; Islak C; Kizilkilic O Clin Radiol; 2020 May; 75(5):351-357. PubMed ID: 31973941 [TBL] [Abstract][Full Text] [Related]
20. Analysis of structural brain MRI and multi-parameter classification for Alzheimer's disease. Zhang Y; Liu S Biomed Tech (Berl); 2018 Jul; 63(4):427-437. PubMed ID: 28622141 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]