Background: Stroke is one of the leading causes of death and disability worldwide. The diagnosis of stroke remains largely clinical, yet widely used stroke scoring systems and brain imaging do not satisfactorily allow the distinction of ischaemic stroke (IS) patients from stroke mimics (SMs). Blood biomarkers are promising tools that could facilitate clinical triage. Methods: This study recruited 66 patients with IS and 24 SMs. The levels of Glial fibrillary acidic protein (GFAP), Neuron-specific enolase (NSE), Neurofilament light chain (NfL) and blood-brain barrier (BBB) proteins [Occludin (OCLN), Zonula occludens 1 (ZO-1), Claudin-5] in blood serum were measured by enzyme-linked immunosorbent assay technique. Biomarker levels in IS patients and SMs were compared using the Mann–Whitney U test. Multivariable logistic regression analysis was used to evaluate the diagnostic performance of biomarkers in combination with the National Institutes of Health Stroke Scale (NIHSS) score. Results: More significant differences in circulating GFAP, NfL, OCLN, ZO-1, and Claudin-5 but not NSE were found in IS patients compared to SMs. A combination of circulating ZO-1, Claudin-5, and OCLN with NIHSS score gives the highest diagnostic accuracy, sensitivity, and specificity. Conclusions: A prediction model with circulating BBB proteins in combination with NIHSS score differentiates between IS patients and SMs.

Background: Stroke is one of the leading causes of death and disability worldwide. The diagnosis of stroke remains largely clinical, yet widely used stroke scoring systems and brain imaging do not satisfactorily allow distinction of ischaemic stroke (IS) patients from stroke mimics (SM). Blood biomarkers are promising tools that could facilitate clinical triage. Methods: This study recruited 66 patients with IS and 24 SM. The levels of Glial fibrillary acidic protein (GFAP), Neuron-specific enolase (NSE), Neurofilament light chain (NfL) and blood-brain barrier (BBB) proteins [Occludin (OCLN), Zonula occludens 1 (ZO-1), Claudin-5] in blood serum were measured by enzyme-linked immunosorbent assay technique. Biomarker levels in IS patients and SM was compared using the Mann–Whitney U test. Multivariable logistic regression analysis was used to evaluate the diagnostic performance of biomarkers in combination with the National Institutes of Stroke Scale (NIHSS) score. Results: Significant differences in circulating GFAP, NfL, OCLN, ZO-1, and Claudin-5 but not NSE in IS patients compared to SM. The combination of circulating ZO-1, Claudin-5, and OCLN with NIHSS score gives the highest diagnostic accuracy, sensitivity, and specificity. Conclusion: A prediction model with Circulating BBB proteins in combination with NIHSS score differentiates between IS patients and SM.

Background: Breast density is increasingly recognized as a vital risk factor that affects early breast cancer detection. Therefore, this study was aimed at evaluating the agreement between different breast density measurements across multiple imaging modalities and identifying the best breast cancer predictor among these methods. Methods: Data for women over 30 years old who underwent mammography, synthetic mammography of digital breast tomosynthesis (SM‐DBT), ultrasound (US), and magnetic resonance imaging (MRI) were collected. Breast density was assessed by two breast radiologists using the American College of Radiology (ACR) categories and the visual analog scale (VAS) for percentage density (%PD). The agreement was calculated using the kappa coefficient ( k ) and Spearman correlation coefficient ( ρ ). Logistic regression odds ratios (ORs) were used to assess the best predictor of breast cancer based on breast density. Results: Among 77 women (mean age 47.34 years), 25 had breast cancer. Categorical breast density assessments showed the highest agreement between mammography and SM‐DBT ( k = 0.535) and moderate agreement between mammography and MRI ( k = 0.452). VAS analysis revealed moderate positive correlations between mammography and SM‐DBT ( ρ = 0.49), mammography and MRI ( ρ = 0.56), and SM‐DBT and MRI ( ρ = 0.56), p < 0.05. Ultrasound showed the lowest correlation with all breast imaging modalities. Breast cancer risk prediction based on breast density showed significant associations for mammography (OR = 3.09) and MRI (OR = 4.16), p < 0.05. Conclusion: The results suggest notable variability in radiologists′ breast density assessment across different imaging modalities. MRI showed a greater ability to identify dense breast tissue and demonstrated potential value in breast cancer risk prediction, although these findings should be interpreted cautiously given the limited sample size. Establishing standardized approaches to breast density assessment remains important to improve the accuracy of breast cancer screening and risk prediction, and further research with larger cohorts is warranted.

Objective: Mammography and ultrasound are commonly used for early detection of breast cancer; however, they have several limitations and are outperformed by magnetic resonance imaging for screening and diagnosis because of its higher sensitivity. This study aimed to evaluate female awareness of magnetic resonance imaging as a tool for early breast cancer detection and to enhance early-stage diagnosis. Methods: A cross-sectional study was conducted using an online questionnaire. Data were analysed using Statistical Package for the Social Sciences software. Awareness was categorised as poor or good. Results: A total of 498 women participated in this study. Although 98% of the participants recognised the importance of early breast cancer detection, only 42.6% were aware of the role of breast magnetic resonance imaging. Awareness of the ability of magnetic resonance imaging to detect intraductal carcinoma and small invasive tumours, particularly in dense breast tissue, was moderate, with a mean awareness score of 2.5 ± 1.1 out of 4. Awareness was significantly higher among older women, those with family history of breast cancer and those who practised self-examinations or had prior magnetic resonance experience (p ≤ 0.002). Conclusions: This study highlights a deficiency in awareness regarding the value of magnetic resonance imaging in breast cancer screening, particularly among young women with dense breast tissue and those without prior exposure to magnetic resonance imaging.

Magnetic Resonance Imaging (MRI) exposes patients to radiofrequency energy measured by the specific absorption rate (SAR), a key safety metric. This study aimed to compare SAR values in brain MRI scans performed at 1.5 Tesla (T) and 3 T to inform safer imaging practices. A retrospective analysis of 200 adult brain MRI scans (100 at 1.5 T and 100 at 3 T) from King Khalid University Hospital was conducted. Data included SAR, demographics, scan parameters, and contrast use. Statistical tests assessed differences (P < .05). Brain SAR was significantly higher in 1.5 T scans (mean = 3.01 W/kg) than in 3 T (mean = 1.37 W/kg). Higher SAR values were noted in females and younger patients. Factors like image type, sequence, weight, flip angle, and contrast use significantly impacted SAR. SAR is more influenced by imaging parameters and patient characteristics than MRI magnetic field strength. Personalized MRI protocols and SAR monitoring are essential for patient safety.

Background/Objectives: Breast imaging reports contain Breast Imaging Reporting and Data System (BI-RADS) assessments and management recommendations that may be difficult for patients to understand across languages. Large language models (LLMs) may support patient-facing communication, but clinically important details must be preserved. This study compared radiologists’ opinions regarding the quality, clinical fidelity, safety of wording, and communication usefulness of patient-friendly Arabic translations of BI-RADS breast imaging reports generated by three LLMs. Methods: Five de-identified reports representing BI-RADS categories 0, 2, 3, 4, and 6 were translated from English into Arabic by DeepSeek, ChatGPT, and Gemini using an identical structured prompt. Fifty radiologists rated the blinded outputs across eight 5-point domains. Model ratings were compared using Friedman tests, Kendall’s W, and multiplicity-adjusted Wilcoxon signed-rank tests. Laterality was also verified against the source reports. Results: Gemini achieved the highest overall mean score (3.73 ± 0.78), followed by DeepSeek (3.54 ± 0.73) and ChatGPT (3.03 ± 0.70). The overall model effect was significant (χ² = 34.11, df = 2, p < 0.001; Kendall’s W = 0.341). Gemini and DeepSeek each outperformed ChatGPT across all eight domains (adjusted p < 0.001), and Gemini outperformed DeepSeek overall (adjusted p = 0.009). No laterality errors were identified among the 15 translations. Conclusions: Performance remained model-dependent despite the shared prompt. Among the participating radiologists, Gemini received the highest expert ratings, while DeepSeek remained competitive. Because errors involving BI-RADS categories, laterality, measurements, lesion location, or recommendations could change diagnostic understanding, LLM-generated Arabic translations should serve as radiologist-reviewed communication aids rather than autonomous substitutes for clinical explanation.