Brain Cancer

A scoping review of radiation dose and image quality in paediatric CT: Towards safe imaging protocols and diagnostic reference levels in Jordan.

BACKGROUND: Computed tomography (CT) is an essential diagnostic tool, but its associated radiation exposure raises significant concerns, especially for radiosensitive paediatric populations. Diagnostic Reference Levels (DRLs) are vital for dose optimization, yet many regions, including Jordan, lack established national DRLs for children. OBJECTIVE: This study systematically reviews international e...

Contactless and Wireless Wound Monitoring Using Nitrogen-Doped Graphene Antenna Sensor.

Quantitative remote wound monitoring has the potential to shorten patient recovery time and alleviate the workload of healthcare professionals. In this study, a nitrogen-doped horizontally grown graphene (NHG) antenna sensor with a working frequency of 2.45 GHz was designed for wireless real-time monitoring of wounds. The sensor comprises 32 NHG microtubes (1 mm in diameter), a porous Cu radiation...

Clinical value of ultra-low-dose chest CT with deep learning image reconstruction in the detection and characterization of pulmonary nodules.

OBJECTIVE: To evaluate the clinical value of ultra-low-dose CT (ULDCT) with deep learning image reconstruction (DLIR) in the diagnosis of pulmonary no...

Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.

Non-contrast MRI, routinely used for the preoperative diagnosis of glioma tumors and establishing treatment strategies, provides the potential for ass...

Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.

INTRODUCTION: Critical workforce shortages in radiation oncology have led tertiary institutions to rapidly expand their radiation therapy (RT) student...

Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach.

The lower thermal behavior of solar-based thermal systems limits the contribution of solar systems to meet current energy demand of industries. The Fl...

Multi-reader evaluation of deep learning-based auto-segmentation of eloquent brain arteriovenous malformation on MRA and white matter tractography in stereotactic radiosurgery.

OBJECTIVE: To minimize the radiation injury for white matter (WM) pathways during brain arteriovenous malformation (bAVM) stereotactic radiosurgery (S...

Explainable machine learning prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage.

Accurate prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage (sICH) remains challenging. This study aimed to devel...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Diagnostic and Technological Advances in Magnetic Resonance (Focusing on Imaging Technique and the Gadolinium-Based Contrast Media), Computed Tomography (Focusing on Photon Counting CT), and Ultrasound-State of the Art.

Magnetic resonance continues to evolve and advance as a critical imaging modality for disease diagnosis and monitoring. Hardware and software advances...

Self-Supervised Text-Vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study).

Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, ...

A voxel-wise uncertainty-guided framework for glioma segmentation using spherical projection-based U-Net and localized refinement.

BACKGROUND: Accurate segmentation of glioma subregions from multi-parametric MRI (MP-MRI) is critical for clinical management but remains challenging ...

Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.

BACKGROUND: Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing ...

Thermal signatures in breast cancer: Deciphering latent biomarkers through deep learning and explainable AI.

Breast cancer, characterized by its aggressive pro-gression and high mortality rates, continues to be among the most common types of cancer. While ear...

Signature-aware deep learning reveals distinct driver gene programs and mutational processes in glioblastoma and colon adenocarcinoma.

The precise identification of cancer driver mutations is essential for precision oncology; however, it remains a significant challenge because of the ...

Diagnostic Imaging Pathways in Peripheral Artery Disease: A Comparative Review of Duplex Ultrasound Through Computed Tomography Angiography.

Peripheral artery disease (PAD) is a major global health challenge, affecting more than 200 million people worldwide and an estimated 8 to 12 million ...

Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic health records and computed tomography scans.

BACKGROUND AND PURPOSE: Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer sur...

Automated Delineation of Putative Non-Contrast-Enhancing Tumor in Glioblastoma: Prognostic Insights.

BACKGROUND: Precise delineation of non-contrast-enhancing tumor (nCET) in glioblastoma (GB) is critical for maximal safe resection, yet routine imagin...

Computational Fluid Dynamics Simulations to Inform Cancer Therapeutics.

Cancer therapies such as chemotherapy, radiopharmaceutical therapy, and transarterial embolization rely on effective drug or radiation delivery throug...

Experiences of Medical Imaging and Radiation Sciences students regarding an assessment based on an artificial intelligence-generated research concept paper.

INTRODUCTION: Artificial intelligence (AI) can complete tasks that once required human cognitive effort. As a result, assessments that traditionally m...