167 related articles for article (PubMed ID: 36044063)
1. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area.
Mori N; Mugikura S; Endo T; Endo H; Oguma Y; Li L; Ito A; Watanabe M; Kanamori M; Tominaga T; Takase K
Neuroradiology; 2023 Feb; 65(2):257-274. PubMed ID: 36044063
[TBL] [Abstract][Full Text] [Related]
2. WHO grade I meningioma subtypes: MRI features and pathological analysis.
Zhang T; Yu JM; Wang YQ; Yin DD; Fang LJ
Life Sci; 2018 Nov; 213():50-56. PubMed ID: 30153449
[TBL] [Abstract][Full Text] [Related]
3. Radiomic Features of the Edema Region May Contribute to Grading Meningiomas With Peritumoral Edema.
Guo Z; Tian Z; Shi F; Xu P; Zhang J; Ling C; Zeng Q
J Magn Reson Imaging; 2023 Jul; 58(1):301-310. PubMed ID: 36259547
[TBL] [Abstract][Full Text] [Related]
4. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.
Park YW; Oh J; You SC; Han K; Ahn SS; Choi YS; Chang JH; Kim SH; Lee SK
Eur Radiol; 2019 Aug; 29(8):4068-4076. PubMed ID: 30443758
[TBL] [Abstract][Full Text] [Related]
5. Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema.
Server A; Kulle B; Maehlen J; Josefsen R; Schellhorn T; Kumar T; Langberg CW; Nakstad PH
Acta Radiol; 2009 Jul; 50(6):682-9. PubMed ID: 19449234
[TBL] [Abstract][Full Text] [Related]
6. Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.
Laukamp KR; Shakirin G; Baeßler B; Thiele F; Zopfs D; Große Hokamp N; Timmer M; Kabbasch C; Perkuhn M; Borggrefe J
World Neurosurg; 2019 Dec; 132():e366-e390. PubMed ID: 31476455
[TBL] [Abstract][Full Text] [Related]
7. Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.
Dong J; Yu M; Miao Y; Shen H; Sui Y; Liu Y; Han L; Li X; Lin M; Guo Y; Xie L
Biomed Res Int; 2020; 2020():5042356. PubMed ID: 33344637
[TBL] [Abstract][Full Text] [Related]
8. Application of magnetic resonance fingerprinting to differentiate grade I transitional and fibrous meningiomas from meningothelial meningiomas.
Zhang R; Shen Y; Bai Y; Zhang X; Wei W; Lin R; Feng Q; Wang M; Zhang M; Nittka M; Koerzdoerfer G; Wang M
Quant Imaging Med Surg; 2021 Apr; 11(4):1447-1457. PubMed ID: 33816181
[TBL] [Abstract][Full Text] [Related]
9. Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI.
Han Y; Wang T; Wu P; Zhang H; Chen H; Yang C
Magn Reson Imaging; 2021 Apr; 77():36-43. PubMed ID: 33220449
[TBL] [Abstract][Full Text] [Related]
10. T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.
Cao T; Jiang R; Zheng L; Zhang R; Chen X; Wang Z; Jiang P; Chen Y; Zhong T; Chen H; Wu P; Xue Y; Lin L
Eur Radiol; 2023 Jan; 33(1):258-269. PubMed ID: 35953734
[TBL] [Abstract][Full Text] [Related]
11. Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas.
Bozdağ M; Er A; Ekmekçi S
Acta Radiol; 2021 Mar; 62(3):401-413. PubMed ID: 32397733
[TBL] [Abstract][Full Text] [Related]
12. Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas.
Zhao Z; Nie C; Zhao L; Xiao D; Zheng J; Zhang H; Yan P; Jiang X; Zhao H
Eur Radiol; 2024 Apr; 34(4):2468-2479. PubMed ID: 37812296
[TBL] [Abstract][Full Text] [Related]
13. Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas.
Lin L; Bhawana R; Xue Y; Duan Q; Jiang R; Chen H; Chen X; Sun B; Lin H
AJNR Am J Neuroradiol; 2018 Jun; 39(6):1032-1038. PubMed ID: 29748203
[TBL] [Abstract][Full Text] [Related]
14. Role of MR Morphology and Diffusion-Weighted Imaging in the Evaluation of Meningiomas: Radio-Pathologic Correlation.
Ranabhat K; Bishokarma S; Agrawal P; Shrestha P; Panth R; Ghimire RK
JNMA J Nepal Med Assoc; 2019; 57(215):37-44. PubMed ID: 31080244
[TBL] [Abstract][Full Text] [Related]
15. Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.
Zhang S; Chiang GC; Knapp JM; Zecca CM; He D; Ramakrishna R; Magge RS; Pisapia DJ; Fine HA; Tsiouris AJ; Zhao Y; Heier LA; Wang Y; Kovanlikaya I
J Neuroradiol; 2020 Jun; 47(4):272-277. PubMed ID: 31136748
[TBL] [Abstract][Full Text] [Related]
16. Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging.
Lin L; Xue Y; Duan Q; Chen X; Chen H; Jiang R; Zhong T; Xu G; Geng D; Zhang J
Clin Radiol; 2019 Aug; 74(8):651.e15-651.e23. PubMed ID: 31072590
[TBL] [Abstract][Full Text] [Related]
17. Preoperative Prediction of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Magnetic Resonance Imaging Texture Analysis.
Kanazawa T; Minami Y; Jinzaki M; Toda M; Yoshida K; Sasaki H
World Neurosurg; 2018 Dec; 120():e1208-e1216. PubMed ID: 30240864
[TBL] [Abstract][Full Text] [Related]
18. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.
Hamerla G; Meyer HJ; Schob S; Ginat DT; Altman A; Lim T; Gihr GA; Horvath-Rizea D; Hoffmann KT; Surov A
Magn Reson Imaging; 2019 Nov; 63():244-249. PubMed ID: 31425811
[TBL] [Abstract][Full Text] [Related]
19. Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.
Banzato T; Causin F; Della Puppa A; Cester G; Mazzai L; Zotti A
J Magn Reson Imaging; 2019 Oct; 50(4):1152-1159. PubMed ID: 30896065
[TBL] [Abstract][Full Text] [Related]
20. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study.
Goyal A; Razik A; Kandasamy D; Seth A; Das P; Ganeshan B; Sharma R
Abdom Radiol (NY); 2019 Oct; 44(10):3336-3349. PubMed ID: 31300850
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
