375 related articles for article (PubMed ID: 30623514)
21. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.
Sengupta A; Agarwal S; Gupta PK; Ahlawat S; Patir R; Gupta RK; Singh A
Eur J Radiol; 2018 Sep; 106():199-208. PubMed ID: 30150045
[TBL] [Abstract][Full Text] [Related]
22. Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions.
Osapoetra LO; Chan W; Tran W; Kolios MC; Czarnota GJ
PLoS One; 2020; 15(12):e0244965. PubMed ID: 33382837
[TBL] [Abstract][Full Text] [Related]
23. Automatic glioma segmentation based on adaptive superpixel.
Wu Y; Zhao Z; Wu W; Lin Y; Wang M
BMC Med Imaging; 2019 Aug; 19(1):73. PubMed ID: 31443642
[TBL] [Abstract][Full Text] [Related]
24. Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study.
Wang Q; Li Q; Mi R; Ye H; Zhang H; Chen B; Li Y; Huang G; Xia J
J Magn Reson Imaging; 2019 Mar; 49(3):825-833. PubMed ID: 30260592
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading.
Su CQ; Lu SS; Han QY; Zhou MD; Hong XN
Acta Radiol; 2019 Jun; 60(6):777-787. PubMed ID: 30244590
[TBL] [Abstract][Full Text] [Related]
27. T1-weighted and T2-weighted Subtraction MR Images for Glioma Visualization and Grading.
Goryawala M; Roy B; Gupta RK; Maudsley AA
J Neuroimaging; 2021 Jan; 31(1):124-131. PubMed ID: 33253433
[TBL] [Abstract][Full Text] [Related]
28. Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features.
Peng H; Huo J; Li B; Cui Y; Zhang H; Zhang L; Ma L
J Magn Reson Imaging; 2021 May; 53(5):1399-1407. PubMed ID: 33179832
[TBL] [Abstract][Full Text] [Related]
29. A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI.
Yin P; Mao N; Zhao C; Wu J; Chen L; Hong N
J Magn Reson Imaging; 2019 Mar; 49(3):752-759. PubMed ID: 30430686
[TBL] [Abstract][Full Text] [Related]
30. Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: Preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis.
Santarosa C; Castellano A; Conte GM; Cadioli M; Iadanza A; Terreni MR; Franzin A; Bello L; Caulo M; Falini A; Anzalone N
Eur J Radiol; 2016 Jun; 85(6):1147-56. PubMed ID: 27161065
[TBL] [Abstract][Full Text] [Related]
31. Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.
Sahran S; Albashish D; Abdullah A; Shukor NA; Hayati Md Pauzi S
Artif Intell Med; 2018 May; 87():78-90. PubMed ID: 29680688
[TBL] [Abstract][Full Text] [Related]
32. An Efficient Method for Brain Tumor Detection Using Texture Features and SVM Classifier in MR Images.
K KK; T MD; S M
Asian Pac J Cancer Prev; 2018 Oct; 19(10):2789-2794. PubMed ID: 30360607
[TBL] [Abstract][Full Text] [Related]
33. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI.
Xu X; Zhang X; Tian Q; Zhang G; Liu Y; Cui G; Meng J; Wu Y; Liu T; Yang Z; Lu H
Int J Comput Assist Radiol Surg; 2017 Apr; 12(4):645-656. PubMed ID: 28110476
[TBL] [Abstract][Full Text] [Related]
34. Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.
Kocak B; Durmaz ES; Ates E; Sel I; Turgut Gunes S; Kaya OK; Zeynalova A; Kilickesmez O
Eur Radiol; 2020 Feb; 30(2):877-886. PubMed ID: 31691122
[TBL] [Abstract][Full Text] [Related]
35. Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas.
Raja R; Sinha N; Saini J; Mahadevan A; Rao KN; Swaminathan A
Neuroradiology; 2016 Dec; 58(12):1217-1231. PubMed ID: 27796448
[TBL] [Abstract][Full Text] [Related]
36. A generic support vector machine model for preoperative glioma survival associations.
Emblem KE; Pinho MC; Zöllner FG; Due-Tonnessen P; Hald JK; Schad LR; Meling TR; Rapalino O; Bjornerud A
Radiology; 2015 Apr; 275(1):228-34. PubMed ID: 25486589
[TBL] [Abstract][Full Text] [Related]
37. Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.
Fathi Kazerooni A; Nabil M; Zeinali Zadeh M; Firouznia K; Azmoudeh-Ardalan F; Frangi AF; Davatzikos C; Saligheh Rad H
J Magn Reson Imaging; 2018 Oct; 48(4):938-950. PubMed ID: 29412496
[TBL] [Abstract][Full Text] [Related]
38. Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.
Tang TT; Zawaski JA; Francis KN; Qutub AA; Gaber MW
Sci Rep; 2019 Aug; 9(1):12529. PubMed ID: 31467303
[TBL] [Abstract][Full Text] [Related]
39. Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease.
Ota K; Oishi N; Ito K; Fukuyama H; ;
J Neurosci Methods; 2015 Dec; 256():168-83. PubMed ID: 26318777
[TBL] [Abstract][Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]
