28 related articles for article (PubMed ID: 38614870)
1. Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis.
Silva Santana L; Borges Camargo Diniz J; Mothé Glioche Gasparri L; Buccaran Canto A; Batista Dos Reis S; Santana Neville Ribeiro I; Gadelha Figueiredo E; Paulo Mota Telles J
World Neurosurg; 2024 Jun; 186():204-218.e2. PubMed ID: 38580093
[TBL] [Abstract][Full Text] [Related]
2. AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.
Bathla G; Dhruba DD; Soni N; Liu Y; Larson NB; Kassmeyer BA; Mohan S; Roberts-Wolfe D; Rathore S; Le NH; Zhang H; Sonka M; Priya S
J Neuroradiol; 2024 May; 51(3):258-264. PubMed ID: 37652263
[TBL] [Abstract][Full Text] [Related]
3. Meta-Analysis of the Efficacy of Raman Spectroscopy and Machine-Learning-Based Identification of Glioma Tissue.
Goff NK; Ashby L; Ashour R
World Neurosurg; 2024 May; ():. PubMed ID: 38796149
[TBL] [Abstract][Full Text] [Related]
4. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.
Nguyen AV; Blears EE; Ross E; Lall RR; Ortega-Barnett J
Neurosurg Focus; 2018 Nov; 45(5):E5. PubMed ID: 30453459
[TBL] [Abstract][Full Text] [Related]
5. MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis.
Suh CH; Kim HS; Jung SC; Park JE; Choi CG; Kim SJ
J Magn Reson Imaging; 2019 Aug; 50(2):560-572. PubMed ID: 30637843
[TBL] [Abstract][Full Text] [Related]
6. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
[TBL] [Abstract][Full Text] [Related]
7. Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.
Guha A; Goda JS; Dasgupta A; Mahajan A; Halder S; Gawde J; Talole S
Front Oncol; 2022; 12():884173. PubMed ID: 36263203
[TBL] [Abstract][Full Text] [Related]
8. Diagnostic performance of DWI for differentiating primary central nervous system lymphoma from glioblastoma: a systematic review and meta-analysis.
Lu X; Xu W; Wei Y; Li T; Gao L; Fu X; Yao Y; Wang L
Neurol Sci; 2019 May; 40(5):947-956. PubMed ID: 30706241
[TBL] [Abstract][Full Text] [Related]
9. How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review.
Guha A; Halder S; Shinde SH; Gawde J; Munnolli S; Talole S; Goda JS
Clin Radiol; 2024 Jun; 79(6):460-472. PubMed ID: 38614870
[TBL] [Abstract][Full Text] [Related]
10. Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation.
Abrigo JM; Fountain DM; Provenzale JM; Law EK; Kwong JS; Hart MG; Tam WWS
Cochrane Database Syst Rev; 2018 Jan; 1(1):CD011551. PubMed ID: 29357120
[TBL] [Abstract][Full Text] [Related]
11. The performance of MR perfusion-weighted imaging for the differentiation of high-grade glioma from primary central nervous system lymphoma: A systematic review and meta-analysis.
Xu W; Wang Q; Shao A; Xu B; Zhang J
PLoS One; 2017; 12(3):e0173430. PubMed ID: 28301491
[TBL] [Abstract][Full Text] [Related]
12. Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.
Zhao J; Huang Y; Song Y; Xie D; Hu M; Qiu H; Chu J
Eur Radiol; 2020 Aug; 30(8):4664-4674. PubMed ID: 32193643
[TBL] [Abstract][Full Text] [Related]
13.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
14.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
15.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
16.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
17.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
18.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
19.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]