Radiomics and radiogenomics in gliomas: a contemporary update

G Singh, S Manjila, N Sakla, A True, AH Wardeh… - British journal of …, 2021 - nature.com
The natural history and treatment landscape of primary brain tumours are complicated by the
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …

Texture analysis in cerebral gliomas: a review of the literature

N Soni, S Priya, G Bathla - American Journal of …, 2019 - Am Soc Neuroradiology
Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify
macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity …

Glioma grading on conventional MR images: a deep learning study with transfer learning

Y Yang, LF Yan, X Zhang, Y Han, HY Nan… - Frontiers in …, 2018 - frontiersin.org
Background: Accurate glioma grading before surgery is of the utmost importance in
treatment planning and prognosis prediction. But previous studies on magnetic resonance …

Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading

P Sun, D Wang, VC Mok, L Shi - Ieee Access, 2019 - ieeexplore.ieee.org
Radiomics-based researches have shown predictive abilities with machine-learning
approaches. However, it is still unknown whether different radiomics strategies affect the …

Deep learning model for the automated detection and histopathological prediction of meningioma

H Zhang, J Mo, H Jiang, Z Li, W Hu, C Zhang, Y Wang… - Neuroinformatics, 2021 - Springer
The volumetric assessment and accurate grading of meningiomas before surgery are highly
relevant for therapy planning and prognosis prediction. This study was to design a deep …

Diagnostic performance between MR amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction at 3 T

Z Xu, C Ke, J Liu, S Xu, L Han, Y Yang, L Qian… - European journal of …, 2021 - Elsevier
Purpose Accurate glioma grading and IDH mutation status prediction are critically essential
for individualized preoperative treatment decisions. This study aims to compare the …

CGHF: A computational decision support system for glioma classification using hybrid radiomics-and stationary wavelet-based features

R Kumar, A Gupta, HS Arora, GN Pandian… - IEEE …, 2020 - ieeexplore.ieee.org
Brain tumors are the most prominent neurologically malignant cancers with the highest
injury and death rates worldwide. Glioma classification is crucial for the prognosis …

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical …

G Abdalla, L Dixon, E Sanverdi, PM Machado… - Neuroradiology, 2020 - Springer
Purpose We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI)
in the diagnosis of gliomas. Methods A review protocol was developed according to the …

Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging

X Wu, X Liang, X Wang, J Qin, L Zhang, Y Tan… - European Journal of …, 2021 - Elsevier
Purpose To compare the diagnostic value of DKI and DTI in differentiation of high-grade
glioma recurrence and pseudoprogression (PsP). Method Forty patients with high-grade …

Tumor multiregional mean apparent propagator (MAP) features in evaluating gliomas—A comparative study with diffusion kurtosis imaging (DKI)

S Zeng, H Ma, D Xie, Y Huang, J Yang… - Journal of Magnetic …, 2024 - Wiley Online Library
Background Glioma classification affects treatment and prognosis. Reliable imaging
methods for preoperatively evaluating gliomas are essential. Purpose To evaluate tumor …