Brain Cancer

Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration.

To clarify the relative contributions of meteorological conditions and anthropogenic activities to ozone (O3) pollution in the Chengdu-Chongqing urban agglomeration, this study systematically quantified their impacts on the daily maximum 8-h average ozone concentration (O3-8 h) during 2015-2024. Using ground-based observations and ERA5-Land reanalysis data, we combined the Light Gradient Boosting ...

Beyond binary classification: a pilot study of imaging-derived glioma severity modeling using T1-weighted and diffusion MRI radiomics.

OBJECTIVE: Gliomas are heterogeneous brain tumors with variable biology and treatment response. Accurate, non-invasive assessment of tumor aggressiveness is essential for prognosis and treatment planning. Conventional machine learning (ML) approaches typically frame glioma grading as a discrete classification task, which may overlook substantial intra-grade heterogeneity. This pilot study explores...

Glioblastoma region-specific metabolic signatures reflect patient survival duration.

PURPOSE: Spatial metabolic differences found in glioblastoma (GBM) tumor core (contrast enhancing) and peritumoral (T2/FLAIR hyperintense) edge tissue...

Face aging rate quantifies change in biological age to predict cancer outcomes.

Chronological age predicts cancer survival but does not capture differences in biological aging rates. We apply FaceAge, an artificial intelligence al...

Beyond HLA-Ia: A multidimensional framework for Neoantigen immunotherapy.

Glioblastoma (GBM) and other malignant gliomas are associated with aggressive progression, high recurrence rates, and poor long-term outcomes, while c...

Multi-Omics and Machine Learning Analyses Reveal PIK3CG, PRKCD, and TRIM22 as Potential Markers of Poor Prognosis and Immune Activation in Glioblastoma.

BACKGROUND: Glioblastoma (GBM) is one of the most aggressive brain tumors with a poor prognosis despite current treatment modalities. This study aimed...

Assessing large language models in radiation risk communication: susceptibility, cultural-linguistic effects, and ethical reasoning.

PURPOSE: To assess the extent to which large language models (LLMs) amplify or attenuate inaccurate or contested narratives in radiation contexts and ...

A methodological assessment of information complementarity between MRI and GAN-based synthetic PET for multimodal medical image analysis.

Multimodal medical imaging aims to enhance analysis by combining complementary anatomical and functional information. However, access to functional mo...

A macrophage co-expression signature enables robust prognostic prediction in glioblastoma.

BACKGROUND: Glioblastoma (GBM) exhibits profound cellular heterogeneity and a highly immunosuppressive microenvironment in which tumor-associated macr...

Evaluating the Utility and Limitations of Machine Learning Tumor Segmentation for Automated Longitudinal RANO Treatment Response Classification.

PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...

Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, Channel Attention, and ASPP.

Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study,...

Prognostic determinants and predictive modeling in higher-grade glioma patients receiving radiotherapy: a retrospective SEER-based study.

3D vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning.

Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D...

Methodological appraisal practices in AI-Based radiomics research in paediatric brain tumour Imaging: A Meta-Research study of systematic reviews.

BACKGROUND: Artificial intelligence-based radiomics offers a potential adjunct to the current clinical management of paediatric brain tumours by enabl...

AI-driven navigated hematoma aspiration vs. conservative treatment for spontaneous intracerebral hemorrhage: study protocol for a randomized controlled trial.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) remains one of the most devastating types of stroke, with high mortality and long-term disabili...

Fast Occupational Upper-Limb Radiation Dose Prediction Using Machine Learning and Monte Carlo Simulation.

OBJECTIVE: Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health ris...

Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.

BACKGROUND: Timely diagnosis of mesenteric vascular diseases, especially acute mesenteric ischemia (AMI) due to embolism in the superior mesenteric ar...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.

Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marke...

FDA-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) tools are increasingly embedded in cancer care, yet the scope of U.S. Food and Drug...