192 related articles for article (PubMed ID: 35625967)
1. Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data.
Stadlbauer A; Marhold F; Oberndorfer S; Heinz G; Buchfelder M; Kinfe TM; Meyer-Bäse A
Cancers (Basel); 2022 May; 14(10):. PubMed ID: 35625967
[TBL] [Abstract][Full Text] [Related]
2. Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning.
Stadlbauer A; Heinz G; Marhold F; Meyer-Bäse A; Ganslandt O; Buchfelder M; Oberndorfer S
Metabolites; 2022 Dec; 12(12):. PubMed ID: 36557302
[TBL] [Abstract][Full Text] [Related]
3. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
Buchlak QD; Esmaili N; Leveque JC; Bennett C; Farrokhi F; Piccardi M
J Clin Neurosci; 2021 Jul; 89():177-198. PubMed ID: 34119265
[TBL] [Abstract][Full Text] [Related]
4. Machine Learning-Based Prediction of Glioma
Stadlbauer A; Nikolic K; Oberndorfer S; Marhold F; Kinfe TM; Meyer-Bäse A; Bistrian DA; Schnell O; Doerfler A
Cancers (Basel); 2024 Mar; 16(6):. PubMed ID: 38539436
[TBL] [Abstract][Full Text] [Related]
5. Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.
Swinburne NC; Schefflein J; Sakai Y; Oermann EK; Titano JJ; Chen I; Tadayon S; Aggarwal A; Doshi A; Nael K
Ann Transl Med; 2019 Jun; 7(11):232. PubMed ID: 31317002
[TBL] [Abstract][Full Text] [Related]
6. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.
Ranjith G; Parvathy R; Vikas V; Chandrasekharan K; Nair S
Neuroradiol J; 2015 Apr; 28(2):106-11. PubMed ID: 25923676
[TBL] [Abstract][Full Text] [Related]
7. Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features.
Thakran S; Gupta RK; Singh A
NMR Biomed; 2022 May; 35(5):e4665. PubMed ID: 34962326
[TBL] [Abstract][Full Text] [Related]
8. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Shrot S; Salhov M; Dvorski N; Konen E; Averbuch A; Hoffmann C
Neuroradiology; 2019 Jul; 61(7):757-765. PubMed ID: 30949746
[TBL] [Abstract][Full Text] [Related]
9. Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques.
Bhattacharjee S; Prakash D; Kim CH; Kim HC; Choi HK
Healthc Inform Res; 2022 Jan; 28(1):46-57. PubMed ID: 35172090
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Vascular architecture mapping for early detection of glioblastoma recurrence.
Stadlbauer A; Eyüpoglu I; Buchfelder M; Dörfler A; Zimmermann M; Heinz G; Oberndorfer S
Neurosurg Focus; 2019 Dec; 47(6):E14. PubMed ID: 31786560
[TBL] [Abstract][Full Text] [Related]
12. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.
Tandel GS; Balestrieri A; Jujaray T; Khanna NN; Saba L; Suri JS
Comput Biol Med; 2020 Jul; 122():103804. PubMed ID: 32658726
[TBL] [Abstract][Full Text] [Related]
13. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.
Cepeda S; Arrese I; García-García S; Velasco-Casares M; Escudero-Caro T; Zamora T; Sarabia R
World Neurosurg; 2021 Feb; 146():e1147-e1159. PubMed ID: 33259973
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Machine learning random forest for predicting oncosomatic variant NGS analysis.
Pellegrino E; Jacques C; Beaufils N; Nanni I; Carlioz A; Metellus P; Ouafik L
Sci Rep; 2021 Nov; 11(1):21820. PubMed ID: 34750410
[TBL] [Abstract][Full Text] [Related]
16. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.
Payabvash S; Aboian M; Tihan T; Cha S
Front Oncol; 2020; 10():71. PubMed ID: 32117728
[TBL] [Abstract][Full Text] [Related]
17. Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data.
Vallée R; Vallée JN; Guillevin C; Lallouette A; Thomas C; Rittano G; Wager M; Guillevin R; Vallée A
Front Oncol; 2023; 13():1089998. PubMed ID: 37614505
[TBL] [Abstract][Full Text] [Related]
18. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers.
Dong F; Li Q; Jiang B; Zhu X; Zeng Q; Huang P; Chen S; Zhang M
Eur Radiol; 2020 May; 30(5):3015-3022. PubMed ID: 32006166
[TBL] [Abstract][Full Text] [Related]
19. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation.
Bae S; An C; Ahn SS; Kim H; Han K; Kim SW; Park JE; Kim HS; Lee SK
Sci Rep; 2020 Jul; 10(1):12110. PubMed ID: 32694637
[TBL] [Abstract][Full Text] [Related]
20. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
Juntu J; Sijbers J; De Backer S; Rajan J; Van Dyck D
J Magn Reson Imaging; 2010 Mar; 31(3):680-9. PubMed ID: 20187212
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]