AIMC Topic: Brain Neoplasms
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3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network.
PloS one
Jun 13, 2025
PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.
Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.
BMC neurology
Jun 5, 2025
BACKGROUND: Glioma is a common primary malignant brain tumor. This study aimed to develop a predictive model for glioma risk by these screened key SNPs in the Chinese Han population.
Machine learning and multi-omics analysis reveal key regulators of proneural-mesenchymal transition in glioblastoma.
Scientific reports
Jun 5, 2025
Glioblastoma (GBM) is classified into subtypes according to the molecular expression profile; the proneural subtype has a relatively good prognosis, and the mesenchymal type is the most aggressive form with the worst prognosis. GBM undergoes proneura...
A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.
Scientific reports
Jun 5, 2025
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource di...
Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.
Science advances
Jun 4, 2025
Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factor...
Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma.
Journal of translational medicine
Jun 3, 2025
BACKGROUND: Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and molecular features. Pathomics, which involves the extraction of digital pathology data, is gaining si...
Rapid diagnosis of TERT promoter mutation using Terahertz absorption spectroscopy in glioblastoma.
Scientific reports
May 27, 2025
Glioblastoma (GBM) is a highly aggressive brain tumor with poor outcomes and limited treatment options. The telomerase reverse transcriptase (TERT) promoter mutation, one of the key biomarkers in GBM, is linked to tumor progression and prognosis. Thi...
Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.
Brain research
May 26, 2025
Medical specialists need to perform precise MRI analysis for accurate diagnosis of brain tumors. Current research has developed multiple artificial intelligence (AI) techniques for the process automation of brain tumor identification. However, existi...
SLIMBRAIN database: A multimodal image database of in vivo human brains for tumour detection.
Scientific data
May 21, 2025
Hyperspectral imaging (HSI) and machine learning (ML) have been employed in the medical field for classifying highly infiltrative brain tumours. Although existing HSI databases of in vivo human brains are available, they present two main deficiencies...
A controlled trial examining large Language model conformity in psychiatric assessment using the Asch paradigm.
BMC psychiatry
May 12, 2025
BACKGROUND: Despite significant advances in AI-driven medical diagnostics, the integration of large language models (LLMs) into psychiatric practice presents unique challenges. While LLMs demonstrate high accuracy in controlled settings, their perfor...
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