Machine learning in differentiating gliomas from primary CNS lymphomas: a systematic review, reporting quality, and risk of bias assessment
GIC Petersen, J Shatalov, T Verma… - American Journal …, 2022 - Am Soc Neuroradiology
BACKGROUND: Differentiating gliomas and primary CNS lymphoma represents a
diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method …
diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method …
Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta …
OBJECTIVE Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL)
are common intracranial pathologies encountered by neurosurgeons. They often may have …
are common intracranial pathologies encountered by neurosurgeons. They often may have …
[HTML][HTML] Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach-a systematic …
A Guha, JS Goda, A Dasgupta, A Mahajan… - Frontiers in …, 2022 - frontiersin.org
BACKGROUND Glioblastoma (GBM) and primary central nervous system lymphoma
(PCNSL) are common in the elderly yet difficult to differentiate on MRI. Their management …
(PCNSL) are common in the elderly yet difficult to differentiate on MRI. Their management …
[HTML][HTML] Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks
M McAvoy, PC Prieto, JR Kaczmarzyk, IS Fernández… - Scientific reports, 2021 - nature.com
A subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish
from glioblastoma multiforme (GBM) on magnetic resonance imaging (MRI). We developed …
from glioblastoma multiforme (GBM) on magnetic resonance imaging (MRI). We developed …
Primary central nervous system lymphoma and atypical glioblastoma: differentiation using radiomics approach
Objectives To evaluate the diagnostic performance of magnetic resonance (MR) radiomics-
based machine-learning algorithms in differentiating primary central nervous system …
based machine-learning algorithms in differentiating primary central nervous system …
Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI …
Objectives Despite the robust diagnostic performance of MRI-based radiomic features for
differentiating between glioblastoma (GBM) and primary central nervous system lymphoma …
differentiating between glioblastoma (GBM) and primary central nervous system lymphoma …
[HTML][HTML] Machine learning and deep learning CT-based models for predicting the primary central nervous system lymphoma and glioma types: a multicenter …
G Lu, Y Zhang, W Wang, L Miao, W Mou - Frontiers in Neurology, 2022 - frontiersin.org
Purpose and Background Distinguishing primary central nervous system lymphoma
(PCNSL) and glioma on computed tomography (CT) is an important task since treatment …
(PCNSL) and glioma on computed tomography (CT) is an important task since treatment …
MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis
CH Suh, HS Kim, SC Jung, JE Park… - Journal of Magnetic …, 2019 - Wiley Online Library
Background Accurate preoperative differentiation of primary central nervous system
lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies …
lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies …
[HTML][HTML] Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors
J Jaruenpunyasak, R Duangsoithong… - … of Neurosciences in …, 2023 - ncbi.nlm.nih.gov
Objectives: It can be challenging in some situations to distinguish primary central nervous
system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance …
system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance …
[HTML][HTML] Trends in development of novel machine learning methods for the identification of gliomas in datasets that include non-glioma images: a systematic review
H Subramanian, R Dey, WR Brim, N Tillmanns… - Frontiers in …, 2021 - frontiersin.org
Purpose Machine learning has been applied to the diagnostic imaging of gliomas to
augment classification, prognostication, segmentation, and treatment planning. A systematic …
augment classification, prognostication, segmentation, and treatment planning. A systematic …