Brain Cancer

Discriminating infectious and non-infectious fever after open craniotomy: a combined statistical and machine learning approach.

FDS-CAP: Modeling Fragmented Disease Subgraphs with Component-Level Attention for Comorbidity Prediction.

Understanding comorbidity between human diseases is essential for uncovering shared pathophysiological mechanisms and improving diagnostic and therapeutic strategies. Although prior studies have investigated genetic and network-based disease associations, they often overlook the fragmented nature of disease modules within the human interactome. To address this limitation, we introduce Fragmented D...

A comparative evaluation of commercial software programs for automatic segmentation in breast cancer radiotherapy planning.

Commercial software tools for automatic segmentation have been adopted in breast cancer radiotherapy planning. In this study, we directly compared com...

A Deep Learning-Driven Framework Integrating Organoid-Based Functional Validation Identifies Universal Neoantigens from Recurrent Glioma Mutations.

Glioblastoma (GBM) is the most common malignant intracranial tumor in adults, with a median survival of only 16-20 months. Neoantigen therapy has show...

U-Net-based transfer learning for automated tumour segmentation enabling fully automated [18F]F-DOPA PET analysis in paediatric gliomas.

BACKGROUND: PET imaging with [18F]F-DOPA shows great promise for assessing paediatric gliomas. Manual tumour delineation and parameter extraction are ...

Dissecting glioblastoma risk signatures in the tumor immune microenvironment based on multi-dimensional transcriptomics.

Glioblastoma (GBM) is characterized by pronounced tumor heterogeneity and a complex immune microenvironment, contributing to poor patient survival out...

Enhanced mechanical and shielding properties of heavy concrete: A machine learning approach to mix proportion optimization.

Shielding concrete is a multifunctional material that combines structural support with radiation shielding, offering significant practical value. Howe...

A cost-sensitive multiclass machine learning framework for postoperative neurosurgical triage (Neuro-TACTIC).

Postoperative placement of patients into a regular ward, an intermediate-care unit (IMC), or an intensive care unit (ICU) is critical for balancing pa...

Overall risk of cancer incidence attributable to adult body CT examinations: impact of a seven-year continuous quality improvement program.

OBJECTIVES: To estimate the impact of a continuous dose reduction and quality improvement program on radiation-induced cancer risk in adult computed t...

MRI-Based Synthetic CT Shows Promise as a Radiation-Free Alternative to Conventional CT in Orthopaedics.

➢ Computed tomography (CT) remains the gold standard for bone imaging, but radiation risks, especially in children, are driving interest in alternativ...

Predicting the performance of a graphene-based patch antenna using a machine learning model for terahertz applications.

This article presents the design of a graphene-based microstrip patch antenna, operating frequency range: (1-5) THz for terahertz (THz) applications. ...

Comparative study of the image quality and radiation dose in paranasal-sinus CT with different tube voltages and reconstruction algorithms.

ObjectivesTo evaluate the application of different tube voltages and image-reconstruction algorithms in paranasal-sinus computed tomography (CT) and o...

The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images.

Glioblastoma diagnostic models and therapeutic drug discovery based on GEO data and machine learning methods.

BACKGROUND: Glioblastoma (GBM) remains lethal due to high molecular heterogeneity and treatment resistance. While previous studies have proposed vario...

AI chatbots for patient education in localized prostate cancer radiotherapy: A comparative quality and readability analysis.

OBJECTIVES: This study aimed to conduct a comparative quality assessment of information provided by widely used artificial intelligence chatbots (AICs...

Optimizing pediatric chest CT: superior image quality and lower radiation dose with deep learning reconstruction.

OBJECTIVES: To compare image quality and radiation dose between deep learning reconstruction (DLIR) and hybrid iterative reconstruction (HIR) algorith...

The impact of emerging technologies on education, roles and career progression of radiation therapists: a delphi consensus based on the ESTRO RTT workshop.

Rapid technological advances in radiation oncology, including artificial intelligence (AI), online adaptive radiotherapy, and advanced imaging, are tr...

Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.

PURPOSE: Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a ...

Novel genomic risk stratification model for primary high-grade malignant peripheral nerve sheath tumor (MPNST).

Risk stratification across the three main clinical subsets of malignant peripheral nerve sheath tumor (MPNST), neurofibromatosis type I (NF1-related),...

Variable Treatment Effects of Cranial Surgery in Pediatric Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Severe traumatic brain injury (TBI) in children is associated with poor outcomes, but evidence surrounding the role of oper...