Brain tumors (BTs) pose a serious threat to human health, and the optimized treatment and results depend on early and accurate detection. Although MRIs and other medical imaging technologies provide insightful information, it is still difficult to de...
BACKGROUND: Glioblastoma (GBM) is no longer regarded as a single disease, as distinct molecular subgroups exist, with the mesenchymal (MES) having the worst prognosis. As such, there is a critical need for noninvasive methods to determine GBM molecul...
This study aimed to develop and validate an interpretable radiomics-based machine learning model using contrast-enhanced T1-weighted imaging (CE-T1WI) to differentiate glioblastoma (GB) from primary central nervous system lymphoma (PCNSL), while comp...
AJNR. American journal of neuroradiology
Nov 3, 2025
BACKGROUND AND PURPOSE: Our aim was to investigate the potential of using MRI-based habitat features for predicting progression-free survival (PFS) in patients with lung cancer brain metastasis (LCBM) receiving radiotherapy.
The abnormal growth of cells inside or near the brain is called a brain tumor. Brain tumors can be benign (non-cancerous) or malignant (cancerous). Both these types can exert pressure on the surrounding brain tissue, increasing intracranial pressure....
BACKGROUND: Neoadjuvant therapy plays an important role in the treatment of glioblastoma (GBM), but a considerable proportion of patients remain unresponsive to the combination of immune checkpoint blockade (ICB) and antiangiogenic therapy. Understan...
Automated brain tumor detection represents a fundamental challenge in contemporary medical imaging, demanding both precision and computational feasibility for practical implementation. This research introduces a novel Vision Transformer (ViT) framewo...
(Z)-endoxifen (endoxifen) is the active metabolite of tamoxifen. Endoxifen is a potent antiestrogen that binds and blocks estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ). Early-phase clinical trials have shown that endoxifen has promis...
Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global fea...
Accurate segmentation of brain tumors from multi-modal MRI scans is critical for diagnosis, treatment planning, and disease monitoring. Tumor heterogeneity and inter-image variability across MRI sequences pose challenging problems to state-of-the-art...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.