Latest AI and machine learning research in brain cancer for healthcare professionals.
Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized approaches from diagnosis to treatment. This review explores the transformative role of AI across the cancer care continuum, highlighting its contributions, challenges, and future directions. AI has significantly advanced cancer detection and diagnosis ...
BACKGROUND: Cardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, super-resolution deep learning reconstruction (SR-DLR) has emerged as a potential advancement over conventional deep learning reconstruction (C-DLR). OBJECTIVE: To evaluate the potential of SR-DLR for further radiation dose reduction in pediatric cardia...
PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, imp...
BACKGROUND: People with and who have survived paediatric brain tumour (PBT) have a poor quality of life due to physiological frailty, a primary compon...
Deformable image registration enables spatial alignment across sequential scans for longitudinal disease monitoring, multi-modal fusion in treatment p...
BACKGROUND: Glioblastoma (GBM) is the most common malignant glioma in adults. It has an extremely poor prognosis, highlighting an urgent need for new ...
OBJECTIVES: This study aims to develop and validate a novel multimodal interpretable artificial intelligence model capable of fusing radiomics feature...
Online adaptive radiotherapy (oART) represents a significant advancement in personalised radiation cancer treatment, offering improved daily dose to t...
BACKGROUND: Early detection of interstitial lung disease including radiation pneumonitis (ILD/RP) is crucial in consolidative durvalumab therapy after...
PURPOSE: To evaluate whether standalone synthesized mammography (SM) can maintain or improve diagnostic accuracy while reducing reading time and radia...
INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applic...
This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...
Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industri...
BACKGROUND: Medical radiation science (MRS) research faces a growing asymmetry between a small body of high-rigour, statistically robust studies and a...
RATIONALE AND OBJECTIVES: To develop and externally validate a preoperative multicontrast MRI stacking model integrating unsupervised habitat radiomic...
Treatment decisions for lower-grade gliomas (WHO grades 2-3) rest on trial averages, which lack temporal resolution. We applied Causal Analysis of Sur...
The expanding footprint of human radiation exposure, driven by advances in interventional diagnostics, the resurgence of the nuclear industry and the ...
PURPOSE: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy, often poorly predicted by static clinical and dosimetric ...
BACKGROUND: Positron emission tomography (PET) is a key tool for quantitative brain imaging, but its image quality and quantitative reliability are st...
Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and vari...