182 related articles for article (PubMed ID: 37507581)
1. An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection.
Felefly T; Roukoz C; Fares G; Achkar S; Yazbeck S; Meyer P; Kordahi M; Azoury F; Nasr DN; Nasr E; Noël G; Francis Z
J Digit Imaging; 2023 Dec; 36(6):2335-2346. PubMed ID: 37507581
[TBL] [Abstract][Full Text] [Related]
2. [High-throughput texture analysis in the distinction of single metastatic brain tumors from high-grade gliomas].
Yin HL; Li DB; Jiang Y; Li SH; Chen Y; Lin GW
Zhonghua Zhong Liu Za Zhi; 2018 Nov; 40(11):841-846. PubMed ID: 30481936
[No Abstract] [Full Text] [Related]
3. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
Inano R; Oishi N; Kunieda T; Arakawa Y; Yamao Y; Shibata S; Kikuchi T; Fukuyama H; Miyamoto S
Neuroimage Clin; 2014; 5():396-407. PubMed ID: 25180159
[TBL] [Abstract][Full Text] [Related]
4. Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas.
Jiang C; Kong Z; Zhang Y; Liu S; Liu Z; Chen W; Liu P; Liu D; Wang Y; Lyu Y; Zhao D; Wang Y; You H; Feng F; Ma W
Neuroradiology; 2020 Jul; 62(7):803-813. PubMed ID: 32239241
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from high-grade gliomas.
Park YW; Eom J; Kim D; Ahn SS; Kim EH; Kang SG; Chang JH; Kim SH; Lee SK
Eur Radiol; 2022 Jul; 32(7):4500-4509. PubMed ID: 35141780
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Radiomics strategy for glioma grading using texture features from multiparametric MRI.
Tian Q; Yan LF; Zhang X; Zhang X; Hu YC; Han Y; Liu ZC; Nan HY; Sun Q; Sun YZ; Yang Y; Yu Y; Zhang J; Hu B; Xiao G; Chen P; Tian S; Xu J; Wang W; Cui GB
J Magn Reson Imaging; 2018 Dec; 48(6):1518-1528. PubMed ID: 29573085
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas.
Jiang C; Kong Z; Liu S; Feng S; Zhang Y; Zhu R; Chen W; Wang Y; Lyu Y; You H; Zhao D; Wang R; Wang Y; Ma W; Feng F
Eur J Radiol; 2019 Dec; 121():108714. PubMed ID: 31704598
[TBL] [Abstract][Full Text] [Related]
13. Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.
Qin JB; Liu Z; Zhang H; Shen C; Wang XC; Tan Y; Wang S; Wu XF; Tian J
Med Sci Monit; 2017 May; 23():2168-2178. PubMed ID: 28478462
[TBL] [Abstract][Full Text] [Related]
14. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.
Kim M; Jung SY; Park JE; Jo Y; Park SY; Nam SJ; Kim JH; Kim HS
Eur Radiol; 2020 Apr; 30(4):2142-2151. PubMed ID: 31828414
[TBL] [Abstract][Full Text] [Related]
15. Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain.
Zachariah RM; Priya PS; Pendem S
J Cancer Res Ther; 2023; 19(2):435-446. PubMed ID: 37313916
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network.
Choi KS; Choi SH; Jeong B
Neuro Oncol; 2019 Sep; 21(9):1197-1209. PubMed ID: 31127834
[TBL] [Abstract][Full Text] [Related]
18. [A multi-modal feature fusion classification model based on distance matching and discriminative representation learning for differentiation of high-grade glioma from solitary brain metastasis].
Zhang Z; Xie J; Zhong W; Liang F; Yang R; Zhen X
Nan Fang Yi Ke Da Xue Xue Bao; 2024 Jan; 44(1):138-145. PubMed ID: 38293985
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
