Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas
P Chang, J Grinband, BD Weinberg… - American Journal …, 2018 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: The World Health Organization has recently placed new
emphasis on the integration of genetic information for gliomas. While tissue sampling …
emphasis on the integration of genetic information for gliomas. While tissue sampling …
[HTML][HTML] Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to
predict tumor genotypes using a pretrained convolutional neural network (CNN) from …
predict tumor genotypes using a pretrained convolutional neural network (CNN) from …
Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging
Background Diagnostic classification of diffuse gliomas now requires an assessment of
molecular features, often including IDH-mutation and 1p19q-codeletion status. Because …
molecular features, often including IDH-mutation and 1p19q-codeletion status. Because …
Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma
Background Glioblastoma is the most common primary brain malignancy, yet treatment
options are limited, and prognosis remains guarded. Individualized tumor genetic …
options are limited, and prognosis remains guarded. Individualized tumor genetic …
[PDF][PDF] A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas
CGB Yogananda, BR Shah, FF Yu… - Neuro-oncology …, 2020 - academic.oup.com
Background One of the most important recent discoveries in brain glioma biology has been
the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion …
the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion …
Retracted: A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas
CG Bangalore Yogananda, BR Shah… - Neuro …, 2020 - academic.oup.com
Background. Isocitrate dehydrogenase (IDH) mutation status has emerged as an important
prognostic marker in gliomas. Currently, reliable IDH mutation determination requires …
prognostic marker in gliomas. Currently, reliable IDH mutation determination requires …
Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning
SR van der Voort, F Incekara, MMJ Wijnenga… - Neuro …, 2023 - academic.oup.com
Background Accurate characterization of glioma is crucial for clinical decision making. A
delineation of the tumor is also desirable in the initial decision stages but is time-consuming …
delineation of the tumor is also desirable in the initial decision stages but is time-consuming …
Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging
Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival
and may guide treatment decision making. We aimed to predict the IDH status of gliomas …
and may guide treatment decision making. We aimed to predict the IDH status of gliomas …
[HTML][HTML] Prediction of molecular mutations in diffuse low-grade gliomas using MR imaging features
ZA Shboul, J Chen, K M. Iftekharuddin - Scientific reports, 2020 - nature.com
Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations,
which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by …
which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by …
Machine learning for the prediction of molecular markers in glioma on magnetic resonance imaging: a systematic review and meta-analysis
BACKGROUND Molecular characterization of glioma has implications for prognosis,
treatment planning, and prediction of treatment response. Current histopathology is limited …
treatment planning, and prediction of treatment response. Current histopathology is limited …