AIMC Topic: Brain Neoplasms

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Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma.

Scientific reports
Glioblastoma is characterized by diffuse infiltration, making accurate detection of residual disease essential for improving prognostication and guiding treatment. This study evaluates whether the volume of predicted infiltration, generated by a mach...

PRCnet: An efficient model for automatic detection of brain tumor in MRI images.

PloS one
Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consu...

Reticular-Induced Energy Transfer Driven Renewable ECL System with Machine Learning for Glioma-Specific Dual-Biomarker Detection and Expression Correlation Mechanism.

Analytical chemistry
Rapid, accurate, and renewable electrochemiluminescence (ECL) bioassays are crucial for multiplexed biomarker detection. Integrated with efficient analytical model for processing sensing data, these tools enable precise differentiation of tumor stage...

MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems.

Scientific reports
Recent advances in artificial intelligence have greatly increased the accuracy of computer-assisted diagnosis for serious conditions including brain tumours. However, concerns about data privacy, class imbalance, and the diversity of medical datasets...

Brain tumour segmentation in fused MRI-PET images with permutate U-Net framework.

PloS one
Brain tumor segmentation from MRI's and PET has always been a challenging and time-consuming phase for radiologists, due to low sensitivity boundary region pixels in this image modality. Deep learning-based image segmentation is the hot research topi...

Enhancing lipid nanoparticles-mediated RNA delivery to glioblastoma via targeted strategies.

Journal of controlled release : official journal of the Controlled Release Society
Glioblastoma (GBM) is an aggressive central nervous system (CNS) malignancy with a poor prognosis and limited responses to conventional therapies. RNA-based therapeutics, with their gene-targeting specificity, present a promising avenue for GBM treat...

Conditional diffusion model for high-accuracy brain tumor segmentation in MRI images.

Scientific reports
The segmentation accuracy of deep learning-based brain tumor MRI images still requires further improvement. We proposed a conditional diffusion network that incorporates image information into the mask's perturbed diffusion process. By optimizing the...

Machine learning-enhanced discovery of a basement membrane-related gene signature in glioblastoma via single-cell and Spatial transcriptomics.

Journal of translational medicine
BACKGROUND: The complex invasiveness and heterogeneity of glioblastoma multiforme (GBM) hinder the complete eradication of the tumor. The invasion of the basement membrane (BM) occurs before the spread to the meninges and the metastasis of glioma cel...

An Integrated Dataset of Metastatic Breast Cancer to the Brain with Imaging, Radiomics, and Tumor Genetics.

Scientific data
This study introduces a unique magnetic resonance imaging dataset focusing on metastatic breast cancer to the brain, a significant clinical challenge in cancer treatment. Comprising 297 T1-weighted post-contrast images from 165 patients, this dataset...

MU-Glioma Post: A comprehensive dataset of automated MR multi-sequence segmentation and clinical features.

Scientific data
Gliomas represent a heterogenous group of primary brain tumors with overlapping imaging phenotypes. Treatment typically includes surgery and/or chemoradiation, however this varies based on the overall lesion and clinical presentation. This heterogene...