219 related articles for article (PubMed ID: 31648967)
1. Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.
Bacchi S; Zerner T; Dongas J; Asahina AT; Abou-Hamden A; Otto S; Oakden-Rayner L; Patel S
J Clin Neurosci; 2019 Dec; 70():11-13. PubMed ID: 31648967
[TBL] [Abstract][Full Text] [Related]
2. Combined use of susceptibility weighted magnetic resonance imaging sequences and dynamic susceptibility contrast perfusion weighted imaging to improve the accuracy of the differential diagnosis of recurrence and radionecrosis in high-grade glioma patients.
Kim TH; Yun TJ; Park CK; Kim TM; Kim JH; Sohn CH; Won JK; Park SH; Kim IH; Choi SH
Oncotarget; 2017 Mar; 8(12):20340-20353. PubMed ID: 27823971
[TBL] [Abstract][Full Text] [Related]
3. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.
Alis D; Bagcilar O; Senli YD; Yergin M; Isler C; Kocer N; Islak C; Kizilkilic O
Jpn J Radiol; 2020 Feb; 38(2):135-143. PubMed ID: 31741126
[TBL] [Abstract][Full Text] [Related]
4. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.
Lee J; Wang N; Turk S; Mohammed S; Lobo R; Kim J; Liao E; Camelo-Piragua S; Kim M; Junck L; Bapuraj J; Srinivasan A; Rao A
Sci Rep; 2020 Nov; 10(1):20331. PubMed ID: 33230285
[TBL] [Abstract][Full Text] [Related]
5. Glioma grading using a machine-learning framework based on optimized features obtained from T
Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
[TBL] [Abstract][Full Text] [Related]
6. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.
Sauwen N; Acou M; Van Cauter S; Sima DM; Veraart J; Maes F; Himmelreich U; Achten E; Van Huffel S
Neuroimage Clin; 2016; 12():753-764. PubMed ID: 27812502
[TBL] [Abstract][Full Text] [Related]
7. A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.
Banzato T; Bernardini M; Cherubini GB; Zotti A
BMC Vet Res; 2018 Oct; 14(1):317. PubMed ID: 30348148
[TBL] [Abstract][Full Text] [Related]
8. A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance Images: A Preliminary Machine Learning Study.
Atici MA; Sagiroglu S; Celtikci P; Ucar M; Borcek AO; Emmez H; Celtikci E
Turk Neurosurg; 2020; 30(2):199-205. PubMed ID: 31608975
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Potential for differentiation of glioma recurrence from radionecrosis using integrated
Sogani SK; Jena A; Taneja S; Gambhir A; Mishra AK; D'Souza MM; Verma SM; Hazari PP; Negi P; Jadhav GK
Neurol India; 2017; 65(2):293-301. PubMed ID: 28290392
[TBL] [Abstract][Full Text] [Related]
11. Accuracy of High-Field Intraoperative MRI in the Detectability of Residual Tumor in Glioma Grade IV Resections.
Heßelmann V; Mager AK; Goetz C; Detsch O; Theisgen HK; Friese M; Schwindt W; Gottschalk J; Kremer P
Rofo; 2017 Jun; 189(6):519-526. PubMed ID: 28591887
[No Abstract] [Full Text] [Related]
12. Comparison between magnetic resonance spectroscopy and diffusion weighted imaging in the evaluation of gliomas response after treatment.
Lotumolo A; Caivano R; Rabasco P; Iannelli G; Villonio A; D' Antuono F; Gioioso M; Zandolino A; Macarini L; Guglielmi G; Cammarota A
Eur J Radiol; 2015 Dec; 84(12):2597-604. PubMed ID: 26391231
[TBL] [Abstract][Full Text] [Related]
13. Utility of intravoxel incoherent motion magnetic resonance imaging and arterial spin labeling for recurrent glioma after bevacizumab treatment.
Miyoshi F; Shinohara Y; Kambe A; Kuya K; Murakami A; Kurosaki M; Ogawa T
Acta Radiol; 2018 Nov; 59(11):1372-1379. PubMed ID: 29471670
[TBL] [Abstract][Full Text] [Related]
14. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images.
Zhang Q; Cao J; Zhang J; Bu J; Yu Y; Tan Y; Feng Q; Huang M
Comput Math Methods Med; 2019; 2019():2893043. PubMed ID: 31871484
[TBL] [Abstract][Full Text] [Related]
15. Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine.
Lao Y; Ruan D; Vassantachart A; Fan Z; Ye JC; Chang EL; Chin R; Kaprealian T; Zada G; Shiroishi MS; Sheng K; Yang W
Int J Radiat Oncol Biol Phys; 2022 Apr; 112(5):1279-1287. PubMed ID: 34963559
[TBL] [Abstract][Full Text] [Related]
16. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
Zhang X; Yan LF; Hu YC; Li G; Yang Y; Han Y; Sun YZ; Liu ZC; Tian Q; Han ZY; Liu LD; Hu BQ; Qiu ZY; Wang W; Cui GB
Oncotarget; 2017 Jul; 8(29):47816-47830. PubMed ID: 28599282
[TBL] [Abstract][Full Text] [Related]
17. Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.
Takahashi S; Takahashi W; Tanaka S; Haga A; Nakamoto T; Suzuki Y; Mukasa A; Takayanagi S; Kitagawa Y; Hana T; Nejo T; Nomura M; Nakagawa K; Saito N
Int J Radiat Oncol Biol Phys; 2019 Nov; 105(4):784-791. PubMed ID: 31344432
[TBL] [Abstract][Full Text] [Related]
18. Longitudinal DSC-MRI for Distinguishing Tumor Recurrence From Pseudoprogression in Patients With a High-grade Glioma.
Boxerman JL; Ellingson BM; Jeyapalan S; Elinzano H; Harris RJ; Rogg JM; Pope WB; Safran H
Am J Clin Oncol; 2017 Jun; 40(3):228-234. PubMed ID: 25436828
[TBL] [Abstract][Full Text] [Related]
19. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.
Cui S; Mao L; Jiang J; Liu C; Xiong S
J Healthc Eng; 2018; 2018():4940593. PubMed ID: 29755716
[TBL] [Abstract][Full Text] [Related]
20. Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index.
Yan R; Haopeng P; Xiaoyuan F; Jinsong W; Jiawen Z; Chengjun Y; Tianming Q; Ji X; Mao S; Yueyue D; Yong Z; Jianfeng L; Zhenwei Y
Neuroradiology; 2016 Feb; 58(2):121-32. PubMed ID: 26494463
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]