AIMC Topic: Glioblastoma
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Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.
Scientific reports
Jul 14, 2021
Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and seco...
Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.
Communications biology
Jul 1, 2021
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an in...
Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach.
Computers in biology and medicine
Jun 6, 2021
The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a metho...
Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.
Scientific reports
May 18, 2021
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination o...
Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.
Scientific reports
May 11, 2021
Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning bot...
Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type.
Scientific reports
May 10, 2021
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic var...
Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Clinical radiology
May 1, 2021
AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.
Young Adult
Adult
Sensitivity and Specificity
Brain Neoplasms
Male
Retrospective Studies
Female
Reproducibility of Results
Aged
Magnetic Resonance Imaging
Diagnosis, Differential
Predictive Value of Tests
Glioblastoma
Image Interpretation, Computer-Assisted
Middle Aged
Adolescent
Treatment Outcome
Contrast Media
Humans
Machine Learning
Brain
Image Enhancement
Chemoradiotherapy
Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.
AJNR. American journal of neuroradiology
Mar 18, 2021
BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of ...
Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.
Journal of magnetic resonance imaging : JMRI
Mar 11, 2021
BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies.
Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.
Radiology
Mar 9, 2021
Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for ...
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