160 related articles for article (PubMed ID: 35183900)
1. A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.
Sun K; Zhang J; Liu Z; Qiu Q; Gao H; Liu P; Chen K; Wei W; Wang L; Zhang J; Zhou J; Tian J
Eur J Radiol; 2022 Apr; 149():110187. PubMed ID: 35183900
[TBL] [Abstract][Full Text] [Related]
2. MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.
Gui Y; Chen F; Ren J; Wang L; Chen K; Zhang J
J Imaging Inform Med; 2024 Jun; 37(3):1054-1066. PubMed ID: 38351221
[TBL] [Abstract][Full Text] [Related]
3. A radiomics model enables prediction venous sinus invasion in meningioma.
Wang L; Cao Y; Zhang G; Sun D; Zhou W; Li W; Zhou J; Chen K; Zhang J
Ann Clin Transl Neurol; 2023 Aug; 10(8):1284-1295. PubMed ID: 37408500
[TBL] [Abstract][Full Text] [Related]
4. A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.
Yang L; Xu P; Zhang Y; Cui N; Wang M; Peng M; Gao C; Wang T
Neuroradiology; 2022 Jul; 64(7):1373-1382. PubMed ID: 35037985
[TBL] [Abstract][Full Text] [Related]
5. Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.
Yu J; Kong X; Xie D; Zheng F; Wang C; Shi D; He C; Liang X; Xu H; Li S; Chen X
BMC Med Imaging; 2024 Jun; 24(1):134. PubMed ID: 38840054
[TBL] [Abstract][Full Text] [Related]
6. A deep learning radiomics model for preoperative grading in meningioma.
Zhu Y; Man C; Gong L; Dong D; Yu X; Wang S; Fang M; Wang S; Fang X; Chen X; Tian J
Eur J Radiol; 2019 Jul; 116():128-134. PubMed ID: 31153553
[TBL] [Abstract][Full Text] [Related]
7. Predicting meningioma grades and pathologic marker expression via deep learning.
Chen J; Xue Y; Ren L; Lv K; Du P; Cheng H; Sun S; Hua L; Xie Q; Wu R; Gong Y
Eur Radiol; 2024 May; 34(5):2997-3008. PubMed ID: 37853176
[TBL] [Abstract][Full Text] [Related]
8. Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.
Yang L; Wang T; Zhang J; Kang S; Xu S; Wang K
BMC Med Imaging; 2024 Mar; 24(1):56. PubMed ID: 38443817
[TBL] [Abstract][Full Text] [Related]
9. Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation.
Joo L; Park JE; Park SY; Nam SJ; Kim YH; Kim JH; Kim HS
Neuro Oncol; 2021 Feb; 23(2):324-333. PubMed ID: 32789495
[TBL] [Abstract][Full Text] [Related]
10. Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.
Jun Y; Park YW; Shin H; Shin Y; Lee JR; Han K; Ahn SS; Lim SM; Hwang D; Lee SK
Eur Radiol; 2023 Sep; 33(9):6124-6133. PubMed ID: 37052658
[TBL] [Abstract][Full Text] [Related]
11. Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study.
Wu X; Dong D; Zhang L; Fang M; Zhu Y; He B; Ye Z; Zhang M; Zhang S; Tian J
Med Phys; 2021 May; 48(5):2374-2385. PubMed ID: 33580497
[TBL] [Abstract][Full Text] [Related]
12. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study.
Zhang J; Yao K; Liu P; Liu Z; Han T; Zhao Z; Cao Y; Zhang G; Zhang J; Tian J; Zhou J
EBioMedicine; 2020 Aug; 58():102933. PubMed ID: 32739863
[TBL] [Abstract][Full Text] [Related]
13. 3D Fast Spin-Echo T1 Black-Blood Imaging for the Preoperative Detection of Venous Sinus Invasion by Meningioma : Comparison with Contrast-Enhanced MRV.
Wang D; Lu Y; Yin B; Chen M; Geng D; Liu L; Wen J; Zhong P; Li Y
Clin Neuroradiol; 2019 Mar; 29(1):65-73. PubMed ID: 29071386
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging.
Kandemirli SG; Chopra S; Priya S; Ward C; Locke T; Soni N; Srivastava S; Jones K; Bathla G
Clin Neurol Neurosurg; 2020 Nov; 198():106205. PubMed ID: 32932028
[TBL] [Abstract][Full Text] [Related]
16. MRI predictors for brain invasion in meningiomas.
Ong T; Bharatha A; Alsufayan R; Das S; Lin AW
Neuroradiol J; 2021 Feb; 34(1):3-7. PubMed ID: 32924772
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas.
Zhang J; Sun J; Han T; Zhao Z; Cao Y; Zhang G; Zhou J
Eur J Radiol; 2020 Nov; 132():109287. PubMed ID: 32980725
[TBL] [Abstract][Full Text] [Related]
19. Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study.
Chen H; Li S; Zhang Y; Liu L; Lv X; Yi Y; Ruan G; Ke C; Feng Y
Eur Radiol; 2022 Oct; 32(10):7248-7259. PubMed ID: 35420299
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
