Brain Cancer

Semi-supervised learning for dose prediction in targeted radionuclide therapy: a synthetic data study.

Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds promise for this purpose. However, current DL-based dosimetry methods require large-scale supervised data, which is scarce in clinical practice.Approach.To address this challenge, we propose exploring semi-supervised learning (SSL) framework that leve...

Customized CNN Architectures Outperform Pre-Trained Models in Differentiating Normal Brain Tissues, Glioma, Meningioma, and Pituitary Tumors.

Early and accurate detection of brain tumors is essential for improving treatment outcomes and patient survival. While pre-trained deep learning models such as ResNet, VGG, and MobileNet have achieved notable success in medical image classification, their generalized architectures often struggle to capture the intricate heterogeneity of brain tissues. This study introduces a customized Convolution...

Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.

BACKGROUND: Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods f...

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-lea...

Integrating multiomics data using a correlation based graph attention network for subtype classification in lower grade glioma.

Accurate classification of cancer subtypes is crucial for personalised therapies and targeted interventions. In this study, we propose BioGAT-LGG, a d...

Budget Impact of Faricimab in Neovascular Age-Related Macular Degeneration in the Netherlands: A Systematic Review and Meta-Analysis of Injection Count.

INTRODUCTION: Frequent anti-vascular endothelial growth factor (anti-VEGF) injections for the treatment of neovascular age-related macular degeneratio...

BraTioUS: A multicenter dataset of baseline intraoperative brain tumor ultrasound images.

The BraTioUS (Brain Tumor Intraoperative Ultrasound) dataset [1] is a large-scale, multicenter, and publicly available collection of intraoperative ul...

TomoRay cranial: synthesis of cranial CT imaging from biplanar radiographs using a generative adversarial network.

OBJECTIVES: Besides clinical examination, cranial CT plays a critical role in diagnostics in neurosurgery. In trauma cases or perioperatively, having ...

Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.

OBJECTIVE: Pediatric scoliosis is the most prevalent spinal disorder, often leading to abnormal curvature and deformation of the spine. Early detectio...

Automated Gross Tumor Volume (GTV) Contouring in High-Grade Gliomas Using a Deep Learning Approach.

RATIONALE AND OBJECTIVES: Accurate contouring of the Gross Tumor Volume (GTV) in High-Grade Gliomas (HGGs) is a cornerstone of effective Radiation The...

Image Quality Assessment of Artificial Intelligence Iterative Reconstruction for Low-dose Bronchial Artery CTA in Preoperative Hemoptysis Patients.

OBJECTIVES: To investigate the feasibility and image quality of artificial intelligence iterative reconstruction (AIIR) for computed tomography angiog...

Exploring structural diversity and dynamic stability of small-molecule PRMT5 inhibitors through machine learning-based QSAR and molecular modelling.

Protein arginine methyltransferase 5 (PRMT5) is a key epigenetic enzyme that catalyses symmetric arginine methylation on histone and non-histone prote...

Externally validated knowledge-based planning model for machine learning-assisted radiation therapy treatment of high-grade glioma.

We have trained and externally validated a knowledge-based planning model for radiation therapy planning in the setting of high-grade glioma. Model pe...

Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation: a cross-sectional study with interpretable diagnostic boundaries.

BACKGROUND: Differentiating preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD) is challenging. Traditional ...

NEIL1 suppresses ROS accumulation to promote temozolomide resistance and malignant progression in glioma cells.

BACKGROUND: Glioma is the most common malignant primary brain tumor. Temozolomide (TMZ) is the standard first-line chemotherapy, but its efficacy is s...

CT-free attenuation and scatter correction of [11C]CFT brain PET using a Bi-directional matching network.

Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation ...

MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.

OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...

Building a national repository of dural-based lesions: clinical, pathological, and demographic insights from the Indian population.

BACKGROUND: Meningiomas are the most common dural-based intracranial tumors, yet Indian literature is predominantly composed of limited single-center ...

3D tibial HU reconstruction from biplanar X-rays utilizing a hybrid PCA-CNN framework.

High-resolution Computed Tomography (CT) is the gold standard medical imaging technique for bone assessment. However, its clinical use is limited by h...

Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.

Computed tomography (CT) is an important imaging modality that provides cross-sectional images, aiding in the detailed visualization of internal struc...