Objective: The objective of this study was to evaluate the image quality of monoenergetic images (MEIs (+)) acquired from dual-energy computed tomography with low-concentration and low-flow-rate contrast media for the arterial supply to the nipple-areola complex (NAC) in breast cancer compared with conventional computed tomography angiography (CTA).
Methods: We enrolled 25 patients (MEI (+)300 group, 300 mg/mL and 2.5 mL/s of contrast media) and 23 patients (CTA370 group, 370 mg/mL and 3.5 mL/s of contrast media) for assessing NAC blood supply angiography. The image quality of the 2 groups was evaluated objectively and subjectively.
Results: The 40 keV MEI (+)300 demonstrated higher attenuation and contrast-to-noise ratio than CTA370 group (P < 0.001). The subjective image quality and visualization of the arteries were comparable between 2 groups.
Conclusions: The 40 keV MEI (+)300 acquired from dual-energy computed tomography can achieve comparable image quality of arterial supply to NAC with low-concentration and low-flow-rate contrast media in breast cancer compared with CTA370.
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http://dx.doi.org/10.1097/RCT.0000000000001063 | DOI Listing |
Headache
March 2025
Department of Otolaryngology, Hospital Universitario San Cecilio, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Granada, Spain.
Objectives: To perform a systematic review and meta-analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of vestibular migraine.
Background: Due to the absence of defined biomarkers for diagnosing vestibular migraine (VM), it is valuable to determine which clinical, physical, and exploratory information is most crucial to diagnosing this disease. The use of artificial intelligence tools could streamline this process.
Brain Behav
March 2025
Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland.
Unlabelled: Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations.
View Article and Find Full Text PDFMed Phys
March 2025
GenesisCare, Sydney, New South Wales, Australia.
Background: Diffusion-weighted imaging (DWI), a quantitative magnetic resonance imaging (qMRI) technique, has the potential to aid in disease characterization and treatment response monitoring. MR-Linacs (MRLs) enable simultaneous DWI acquisitions during radiotherapy, uniquely aiding in the collection of large-scale datasets for imaging biomarkers, such as the DWI-derived apparent diffusion coefficient (ADC), without additional patient burden. However, the limited data reporting on variability in MRL scanner performance characteristics, and a lack of established clinical trial quality assurance (QA) procedures, are barriers to this route for biomarker validation.
View Article and Find Full Text PDFEur J Breast Health
March 2025
Department of Pharmacy Practice, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Pune, India.
Objective: To assess health-related quality of life (HRQoL) using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire for Breast Cancer (EORTC QLQ-BR45) in conjunction with the Core questionnaire (EORTC QLQ-C30) in breast cancer patients receiving chemotherapy.
Materials And Methods: This prospective, cross-sectional study was conducted in the oncology department of a tertiary care hospital for six months. Patients aged ≥18 years, diagnosed with breast cancer, and who had received at least three chemotherapy cycles were included in the study.
Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large-scale, and dynamic data collection. Integrating EHR data with advanced analytical methods, including artificial intelligence (AI), transforms CVD risk prediction and management methodologies. This review examines the advancements and challenges of using EHR in developing CVD prediction models, covering traditional and AI-based approaches.
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