148 related articles for article (PubMed ID: 36194851)
1. Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging.
Hu G; Hu X; Yang K; Yu Y; Jiang Z; Liu Y; Liu D; Hu X; Xiao H; Zou Y; You Y; Liu H; Chen J
J Comput Assist Tomogr; 2023 Jan-Feb 01; 47(1):129-135. PubMed ID: 36194851
[TBL] [Abstract][Full Text] [Related]
2. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.
Su X; Chen N; Sun H; Liu Y; Yang X; Wang W; Zhang S; Tan Q; Su J; Gong Q; Yue Q
Neuro Oncol; 2020 Mar; 22(3):393-401. PubMed ID: 31563963
[TBL] [Abstract][Full Text] [Related]
3. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis.
Nakamoto T; Takahashi W; Haga A; Takahashi S; Kiryu S; Nawa K; Ohta T; Ozaki S; Nozawa Y; Tanaka S; Mukasa A; Nakagawa K
Sci Rep; 2019 Dec; 9(1):19411. PubMed ID: 31857632
[TBL] [Abstract][Full Text] [Related]
4. The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression.
Fu FX; Cai QL; Li G; Wu XJ; Hong L; Chen WS
Magn Reson Imaging; 2024 Sep; 111():168-178. PubMed ID: 38729227
[TBL] [Abstract][Full Text] [Related]
5. MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.
Sakai Y; Yang C; Kihira S; Tsankova N; Khan F; Hormigo A; Lai A; Cloughesy T; Nael K
Int J Mol Sci; 2020 Oct; 21(21):. PubMed ID: 33121211
[TBL] [Abstract][Full Text] [Related]
6. Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O
Zhang S; Sun H; Su X; Yang X; Wang W; Wan X; Tan Q; Chen N; Yue Q; Gong Q
J Magn Reson Imaging; 2021 Jul; 54(1):197-205. PubMed ID: 33393131
[TBL] [Abstract][Full Text] [Related]
7. Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults.
Lv K; Chen H; Cao X; Du P; Chen J; Liu X; Zhu L; Geng D; Zhang J
J Neurosurg; 2023 Aug; 139(2):393-401. PubMed ID: 36681946
[TBL] [Abstract][Full Text] [Related]
8. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
[TBL] [Abstract][Full Text] [Related]
9. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.
Kandemirli SG; Kocak B; Naganawa S; Ozturk K; Yip SSF; Chopra S; Rivetti L; Aldine AS; Jones K; Cayci Z; Moritani T; Sato TS
World Neurosurg; 2021 Jul; 151():e78-e85. PubMed ID: 33819703
[TBL] [Abstract][Full Text] [Related]
10. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.
Choi YS; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Jain R; Lee SK
Eur Radiol; 2020 Jul; 30(7):3834-3842. PubMed ID: 32162004
[TBL] [Abstract][Full Text] [Related]
11. [Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model].
He H; Guo E; Meng W; Wang Y; Wang W; He W; Wu Y; Yang W
Nan Fang Yi Ke Da Xue Xue Bao; 2024 Jan; 44(1):194-200. PubMed ID: 38293992
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle.
Mo H; Liang W; Huang Z; Li X; Xiao X; Liu H; He J; Xu Y; Wu Y
Eur Radiol; 2023 Jun; 33(6):4259-4269. PubMed ID: 36547672
[TBL] [Abstract][Full Text] [Related]
13. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
Hashido T; Saito S; Ishida T
J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):606-613. PubMed ID: 34270479
[TBL] [Abstract][Full Text] [Related]
14. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.
Li Y; Liu X; Qian Z; Sun Z; Xu K; Wang K; Fan X; Zhang Z; Li S; Wang Y; Jiang T
Eur Radiol; 2018 Jul; 28(7):2960-2968. PubMed ID: 29404769
[TBL] [Abstract][Full Text] [Related]
15. Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning.
Ubaldi L; Saponaro S; Giuliano A; Talamonti C; Retico A
Phys Med; 2023 Mar; 107():102538. PubMed ID: 36796177
[TBL] [Abstract][Full Text] [Related]
16. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.
Cao M; Suo S; Zhang X; Wang X; Xu J; Yang W; Zhou Y
Biomed Res Int; 2021; 2021():1235314. PubMed ID: 33553421
[TBL] [Abstract][Full Text] [Related]
17. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.
Li G; Li L; Li Y; Qian Z; Wu F; He Y; Jiang H; Li R; Wang D; Zhai Y; Wang Z; Jiang T; Zhang J; Zhang W
Brain; 2022 Apr; 145(3):1151-1161. PubMed ID: 35136934
[TBL] [Abstract][Full Text] [Related]
18. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
19. [Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer].
Wen DG; Hu SX; Li ZL; Deng XB; Tian C; Li X; Wang XR; Leng Q; Xia CC
Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Jul; 52(4):698-705. PubMed ID: 34323052
[TBL] [Abstract][Full Text] [Related]
20. Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.
Wu S; Meng J; Yu Q; Li P; Fu S
J Cancer Res Clin Oncol; 2019 Mar; 145(3):543-550. PubMed ID: 30719536
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]