AIMC Topic: Glioblastoma
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Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.
Magnetic resonance in medicine
Sep 13, 2024
PURPOSE: To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.
Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.
AJNR. American journal of neuroradiology
Sep 9, 2024
BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact ...
Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.
Neuroradiology
Sep 3, 2024
OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) a...
Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.
Scientific reports
Aug 21, 2024
Transcranial magnetic stimulation (TMS) has emerged as a promising neuromodulation technique with both therapeutic and diagnostic applications. As accurate coil placement is known to be essential for focal stimulation, computational models have been ...
Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.
NMR in biomedicine
Aug 20, 2024
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled p...
A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.
Cancer science
Aug 9, 2024
A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologis...
AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.
Journal of imaging informatics in medicine
Jul 8, 2024
TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females)...
CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma.
Journal of computational biology : a journal of computational molecular cell biology
Jul 8, 2024
Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively us...
Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.
Scientific reports
Jul 2, 2024
This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were rando...
Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.
International journal of computer assisted radiology and surgery
Jun 7, 2024
OBJECTIVES: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 disti...
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