141 related articles for article (PubMed ID: 34508971)
21. 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]
22. 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]
23. Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.
Takahashi S; Takahashi W; Tanaka S; Haga A; Nakamoto T; Suzuki Y; Mukasa A; Takayanagi S; Kitagawa Y; Hana T; Nejo T; Nomura M; Nakagawa K; Saito N
Int J Radiat Oncol Biol Phys; 2019 Nov; 105(4):784-791. PubMed ID: 31344432
[TBL] [Abstract][Full Text] [Related]
24. Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma 'ground truth' simulation on MRI.
Sepehri K; Song X; Proulx R; Hajra SG; Dobberthien B; Liu CC; D'Arcy RCN; Murray D; Krauze AV
Int J Med Inform; 2021 Feb; 146():104348. PubMed ID: 33285357
[TBL] [Abstract][Full Text] [Related]
25. MR-guided non-invasive typing of brain gliomas using machine learning.
Danilov GV; Pronin IN; Korolev VV; Maloyan NG; Ilyushin EA; Shifrin MA; Afandiev RM; Shevchenko AM; Konakova TA; Shugai SV; Potapov AA
Zh Vopr Neirokhir Im N N Burdenko; 2022; 86(6):36-42. PubMed ID: 36534622
[TBL] [Abstract][Full Text] [Related]
26. Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques.
Anjum S; Hussain L; Ali M; Abbasi AA; Duong TQ
Math Biosci Eng; 2021 Mar; 18(3):2882-2908. PubMed ID: 33892576
[TBL] [Abstract][Full Text] [Related]
27. MRI radiomics analysis of molecular alterations in low-grade gliomas.
Shofty B; Artzi M; Ben Bashat D; Liberman G; Haim O; Kashanian A; Bokstein F; Blumenthal DT; Ram Z; Shahar T
Int J Comput Assist Radiol Surg; 2018 Apr; 13(4):563-571. PubMed ID: 29270916
[TBL] [Abstract][Full Text] [Related]
28. Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine.
Munquad S; Si T; Mallik S; Li A; Das AB
Brief Funct Genomics; 2022 Sep; 21(5):408-421. PubMed ID: 35923100
[TBL] [Abstract][Full Text] [Related]
29. RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion.
Chen SC; Lo CM; Wang SH; Su EC
BMC Bioinformatics; 2019 Dec; 20(Suppl 19):659. PubMed ID: 31870275
[TBL] [Abstract][Full Text] [Related]
30. 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]
31. Classification of the glioma grading using radiomics analysis.
Cho HH; Lee SH; Kim J; Park H
PeerJ; 2018; 6():e5982. PubMed ID: 30498643
[TBL] [Abstract][Full Text] [Related]
32. Resting state fMRI feature-based cerebral glioma grading by support vector machine.
Wu J; Qian Z; Tao L; Yin J; Ding S; Zhang Y; Yu Z
Int J Comput Assist Radiol Surg; 2015 Jul; 10(7):1167-74. PubMed ID: 25227532
[TBL] [Abstract][Full Text] [Related]
33. Annotation-free glioma grading from pathological images using ensemble deep learning.
Su F; Cheng Y; Chang L; Wang L; Huang G; Yuan P; Zhang C; Ma Y
Heliyon; 2023 Mar; 9(3):e14654. PubMed ID: 37009333
[TBL] [Abstract][Full Text] [Related]
34. Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.
Ahammed Muneer K V ; Rajendran VR; K PJ
J Med Syst; 2019 Mar; 43(5):113. PubMed ID: 30900029
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. 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]
37. 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]
38. Molecular and metabolic pattern classification for detection of brain glioma progression.
Imani F; Boada FE; Lieberman FS; Davis DK; Mountz JM
Eur J Radiol; 2014 Feb; 83(2):e100-5. PubMed ID: 24321226
[TBL] [Abstract][Full Text] [Related]
39. The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.
Zhu S; Kong W; Zhu J; Huang L; Wang S; Bi S; Xie Z
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36088543
[TBL] [Abstract][Full Text] [Related]
40. Ensemble learning for glioma patients overall survival prediction using pre-operative MRIs.
Yang Z; Chen M; Kazemimoghadam M; Ma L; Stojadinovic S; Wardak Z; Timmerman R; Dan T; Lu W; Gu X
Phys Med Biol; 2022 Dec; 67(24):. PubMed ID: 36384039
[No Abstract] [Full Text] [Related]
[Previous] [Next] [New Search]
