Brain Cancer

Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy.

Large language models (LLMs) have recently gained attention for their potential. However, concerns remain regarding their reliability due to limitations such as hallucinations and insufficient domain-specific knowledge. Retrieval-augmented generation (RAG) has emerged as a promising approach, enabling LLMs to reference external knowledge sources and generate accurate outputs. We aimed to clarify t...

Artificial intelligence, omics, and biomarkers: Redefining lung cancer early detection.

Lung cancer, the leading cause of death worldwide, claims millions of lives yearly, largely due to limited early interventions. Currently used lung cancer screening methods are still limited in their reach and accuracy due to invasiveness, radiation exposure, and low sensitivity, especially in early stages, necessitating the need for innovative technologies. This review examines emerging tools for...

Radiomics and deep learning models for predicting glioma p53 status: A diagnostic accuracy systematic review and meta-analysis of magnetic resonance imaging studies.

PURPOSE: To systematically investigate the diagnostic performance of magnetic resonance imaging (MRI)-based radiomics and deep learning (DL) models fo...

Integrating multi-omics analysis and machine learning to refine molecular subtypes and prognostic assessment of lower-grade glioma.

Lower-grade glioma (LGG) is a highly heterogeneous disease, making accurate prognosis prediction and the development of precise, personalized treatmen...

Factors predicting MRI glioma segmentation accuracy in deep learning models: a systematic review and meta-analysis.

BACKGROUND: Accurate segmentation is central for the diagnosis and treatment of gliomas. Although manual segmentation remains the clinical standard, i...

Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.

OBJECTIVE: To evaluate the effects of arm positioning and reconstruction algorithms on radiation dose and image quality of abdominal CT. MATERIALS AND...

A Segmentation-Guided Feature Alignment and Fusion Network for Glioma IDH Genotyping.

Isocitrate dehydrogenase (IDH) is a pivotal molecular marker for glioma diagnosis, prognosis, and treatment planning. Multi-modal deep learning method...

Enhancing 1p/19q Classification in Brain Gliomas Using IDH Status: A Deep Learning Study.

BACKGROUND AND PURPOSE: IDH mutation & 1p/19q codeletion are critical biomarkers for glioma diagnosis & therapy. 1p/19q codeletion occurs exclusively ...

Automatic Extraction of PET RANO Criteria with an Externally Validated Deep Learning Model: Application to [18F]FDOPA PET Imaging.

BACKGROUND: Automatic segmentation of gliomas on amino acid PET is essential for quantitative tumor assessment, a pillar in monitoring gliomas under t...

National Institute of Allergy and Infectious Diseases (NIAID) - Bundeswehr Institute of Radiobiology (BIR) Joint Programmatic Workshop on a Global Approach to Medical Countermeasure Development in Radiological Public Health Preparedness.

This invited commentary grew out of a presentation made at the 2025 ConRad Meeting in Munich, Germany, and summarizes talks made by researchers suppor...

Positron emission tomography imaging of inflammation and infection in children: an update.

Positron emission tomography (PET) has been used in pediatric oncology since the modality gained traction 20 years ago but has been used more sparingl...

Low-dose CT reconstruction by self-supervised learning in the projection domain.

OBJECTIVE: To address the critical issue of compromised image quality and diagnostic accuracy in low-dose computed tomography (LDCT) due to increased ...

Non-small Cell Lung Cancer in Metastatic Renal Cell Carcinoma Patients.

INTRODUCTION: Renal cell carcinoma (RCC) most commonly metastasizes to the lungs and shares risk factors with lung cancer. However, primary lung cance...

Focused Ultrasound in Brain Tumors: Mechanisms, Imaging Guidance, and Emerging Clinical Applications.

Focused ultrasound (FUS) is an emerging therapeutic and diagnostic technology in neuro-oncology, offering new strategies for molecular diagnosis, drug...

Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.

Predicting isocitrate dehydrogenase (IDH) mutations in gliomas using magnetic resonance imaging (MRI) is clinically important for treatment planning. ...

A Novel Nomogram for Predicting Meningioma Grade Based on Radiomics Features and Clinical Characteristics.

OBJECTIVE: This study aimed to develop a predictive model utilizing radiomics features and clinical characteristics to accurately differentiate low-gr...

MRI Deep Learning for Differentiating Glioblastoma, IDH-Wildtype from Central Nervous System Diffuse Large B-cell Lymphoma.

Glioblastoma, IDH-wildtype (GBM) and central nervous system diffuse large B-Cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI...

CTM-DETR-frequency-aware and statistically guided transformer for early infrared forest fire detection.

Early-stage infrared forest fire detection is severely hindered by strong background thermal interference and extremely weak fire radiation signals. E...

Ultra-low-dose CT for malignant metastasis screening using a deep learning image reconstruction algorithm.

PURPOSE: To evaluate the feasibility and diagnostic performance of ultra-low-dose CT (ULD-CT) for screening malignant metastasis using super-resolutio...

Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.

BACKGROUND: Deep learning methods have made great progress in the automatic segmentation of nasopharyngeal carcinoma, but challenges remain. PURPOSE: ...