Brain Cancer

The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of (CADRA).

Artificial intelligence (AI) is poised to fundamentally transform radiation medicine, with growing influence across clinical decision-making, workflow efficiency, personalization of care, and quality assurance. While the technical potential of AI is well described in the literature, less attention has been given to how these tools should be responsibly implemented within real-world healthcare syst...

Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.

Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathol...

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acqui...

Multimodal deep learning-based prediction of radiation-induced temporal lobe injury via anatomical imaging and spatial dose: A multicenter study.

BACKGROUND: To develop and validate a multimodal deep learning model for pre-treatment prediction of radiation-induced temporal lobe injury (RTLI), an...

Advances in the management of pediatric low-grade glioma.

Pediatric low-grade gliomas (pLGGs), the most common CNS tumors in children, are increasingly recognized as chronic diseases with prolonged courses an...

Stochastic Expansion of Radionuclide Inhalation Dosimetry for Consequence Management Application: Uncertainty and Sensitivity Analysis in the ICRP 130 Human Respiratory Tract Model.

Releases from nuclear or radiological security events can result in significant internal radiation contamination through inhalation of particulate con...

Molecular characteristics and modifications of glioblastoma affecting the efficacy of immunotherapy.

Glioblastoma is a highly aggressive brain tumor characterized by complex genetic, molecular, and epigenetic features that present significant challeng...

Quantitative Impact of T1 Subtraction Maps on Enhancing Component Delineation and Measured Volumes in Minimally Enhancing Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: The delineation of contrast enhancement in pediatric brain tumors is crucial for effective surgical and treatment planning, as...

Graph informed biomarker discovery framework using transcriptomic machine learning for glioblastoma prognosis.

Identifying reproducible, interpretable prognostic signals from high-dimensional transcriptomics remains challenging because gene-level models often i...

Integrative single-cell and bulk transcriptomics reveal cuproptosis subtypes, prognostic models, and COL5A1 as a key gene in glioblastoma.

Glioblastoma (GBM) remains one of the most aggressive primary brain tumors with limited therapeutic options. Cuproptosis, a recently identified copper...

Multi-Omics Analyses Reveal BPA Accelerates Glioma Progression via Cell Cycle Dysregulation.

Bisphenol A (BPA), a pervasive environmental endocrine disruptor, its role in glioma progression is unclear. We sought to elucidate how BPA influences...

Radiation shielding and microstructural characteristics of nano-silica and nano-alumina modified cement composites.

The increasing use of nuclear technology in medicine, industry, and energy requires effective durable radiation shielding. This study aimed to develop...

Biology-informed risk stratification of glioblastoma by integrating MRI-based intratumoral heterogeneity with clinical features: a multicenter validation study.

BACKGROUND: Refined risk stratification before randomization is clinically important for reducing prognostic imbalance across study arms when evaluati...

Integrating MRI radiomics and transcriptomics to predict IDH mutation status and prognosis in glioma.

OBJECTIVE: To investigate MRI-based radiomic features in glioma and key genes related to IDH mutations, and to analyze their correlation. METHODS: 61 ...

Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer.

Homologous recombination deficiency (HRD) assays are used to select patients with ovarian cancer for PARP inhibitors, but they do not fully capture th...

Integrative multi-omics analysis and machine learning identify M2 macrophage-induced ferroptosis resistance in glioblastoma.

Glioblastoma (GBM) is characterized by profound intratumoral heterogeneity and an immunosuppressive microenvironment that drive therapeutic resistance...

Identification and validation of exosome-related biomarkers in pediatric glioblastoma.

Pediatric glioblastoma (pGBM) is an aggressive central nervous system (CNS) tumor whose pathological progression is significantly influenced by exosom...

AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.

BACKGROUND: Glioblastoma (GBM) remains one of the most lethal adult primary brain tumors, and neurosurgical decision-making increasingly depends on in...

Clinical registry metadata as a hidden bottleneck in AI-driven drug discovery: a computational audit of translational phase data in glioma research.

Clinical translation in glioma and glioblastoma remains inefficient despite advances in computational drug discovery. An underrecognized contributor t...

Latent twins: a framework for scene recognition and fast radiative transfer inversion in FORUM all-sky observations.

The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral me...