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PubMed2026

International study of coronary microvascular angina (iCorMicA): A registry-based diagnostic study and nested randomized trial.

BACKGROUND: Angina is a debilitating condition caused by coronary artery disease and microvascular dysfunction. Following coronary angiography angina and no obstructive coronary arteries is a common outcome, and women are disproportionately affected. The objectives are first, to assess causes of angina in patients undergoing invasive management; and second, to assess effects of coronary function test-guided management on clinical outcomes. METHODS: This is an international, multicenter, prospective, registry-based study and nested, randomized, controlled, triple-blind, and endpoint trial. Participants, community care providers, and outcomes assessors are masked. Consented participants enter the registry. Participants without obstructive coronary artery disease (luminal stenosis <50%, or fractional flow reserve >0.80) are eligible for randomization. Index of microcirculatory resistance (IMR; abnormal ≥25) and coronary flow reserve (CFR; abnormal <2.0; gray zone 2.0-2.5) are measured by bolus thermodilution, and results are disclosed (intervention) or not (control group) to the attending cardiologist. RESULTS: The primary outcome of the registry is the Seattle Angina Questionnaire summary score at baseline described by coronary artery disease status. Secondary outcomes include the prevalence of obstructive coronary artery disease, patient reported outcome measures and clinical outcomes. The primary outcome of the randomized trial is the within-individual change in Seattle Angina Questionnaire summary score at 12-months from baseline. Secondary outcomes include safety, diagnostic accuracy, patient reported outcome measures for quality of life, physical and psychological function, cardiovascular risk, clinical outcomes, health economics and mechanistic biomarkers. The first patient was screened on December 18, 2020 and the last patient was enrolled on June 30, 2026. Forty sites were included in the United Kingdom (n = 35), Republic of Ireland (n = 2), Holland (n = 2), and Poland (n = 1). In total, 1,483 participants were enrolled into the registry of whom 1,047 were randomized and 386 were not randomized (registry-only). CONCLUSION: This international, registry-based clinical trial will provide novel evidence on the natural history of angina and stratified therapy for angina with no obstructive coronary arteries. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT04674449. UNIQUE IDENTIFIER: NCT04674449.

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PubMed2026

Diagnostic performance of panfungal PCR on tissue specimens for the diagnosis of invasive fungal diseases: a systematic review and meta-analysis of the Fungal PCR Initiative (FPCRI).

UNLABELLED: Invasive fungal diseases are difficult to diagnose because of the limited sensitivity of culture. Panfungal PCR amplicon sequencing assays (targeting ribosomal RNA, such as 18S, 28S, ITS) are recommended for fungal identification in histopathology samples showing fungal elements. However, data describing its overall performance and consistency are lacking. This systematic literature review and meta-analysis assessed the performance of panfungal PCR on formalin-fixed paraffin-embedded (FFPE) and non-fixed (fresh or frozen) tissue samples. A systematic literature search was performed to include studies reporting the use of panfungal PCR for fungal identification in FFPE or non-fixed tissue samples. PCR sensitivity and specificity were assessed using the reference standard of histopathology showing fungal elements. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates were obtained using random-effects meta-analysis. Twenty-eight studies were included. In FFPE samples (18 studies, 852 samples), sensitivity and specificity were 75.4% (95% confidence interval [CI], 59.2-86.6) and 93.5% (70.2-98.9), respectively. Sensitivity in non-fixed samples (13 studies, 207 samples) was 86.5% (74.7-93.3), while specificity could not be assessed (insufficient data). Comparative analyses showed a significantly higher sensitivity of panfungal PCR over culture (88.2%; 76-94.7 vs 52.2%; 39-65, P = 0.001). Sub-analyses could not demonstrate the superiority of one PCR target over another due to limited data. Panfungal PCR exhibited adequate sensitivity and good specificity in FFPE samples. Sensitivity was even higher in non-fixed samples and largely superior to culture. Nevertheless, large interstudy variability was observed, warranting interlaboratory studies to define the optimal PCR target and standardized protocols. IMPORTANCE: Invasive fungal diseases are difficult to diagnose because of the low sensitivity of culture. Panfungal PCRs are widely used for fungal identification in tissue specimens but suffer from heterogeneous procedures and performance. This meta-analysis shows an acceptable sensitivity (75.4% and 86.5% in fixed and non-fixed samples, respectively) and good specificity (93.5%) of panfungal PCR, supporting its use, not only on histopathology-positive fixed samples but also in non-fixed samples concomitantly with other diagnostic tools (cultures and fungal-specific PCRs if available). These results provide a strong basis for further standardization of panfungal PCR techniques via interlaboratory assays to assess reproducibility and optimize analytical protocols. CLINICAL TRIALS: This study is registered with PROSPERO as CRD42023461148.

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PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

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PubMed2026

Teaching Acute Coronary Syndrome High-Risk ECG Interpretation and Clinical Decision-Making Through FOAMed Videos and Podcast Versus Print-Based Materials Among Emergency Care Providers: Randomized Controlled Mixed Methods Trial.

BACKGROUND: Accurate interpretation of high-risk acute coronary syndrome (ACS) electrocardiograms (ECGs) is essential for early diagnosis and timely reperfusion, yet substantial deficits persist across health care professions. Digital self-learning formats such as FOAMed (Free Open Access Medical Education) are widely used, but their effectiveness has rarely been evaluated for complex, high-risk ACS ECG patterns. Existing ECG education studies often focus on students or single professional groups and established ST-segment elevation myocardial infarction (STEMI) criteria, leaving newer guideline-recognized STEMI equivalents, selected emerging occlusion myocardial infarction (OMI)-related patterns, and interprofessional emergency care underrepresented. OBJECTIVE: This study aimed to compare the effectiveness of FOAMed podcast and videos versus traditional print-based materials for teaching high-risk ACS ECG patterns and related clinical decision-making in emergency providers. METHODS: We conducted a prospective, interprofessional, controlled mixed methods trial across 5 training sites in Germany. Paramedics, prehospital emergency physicians, and emergency department clinicians received either a FOAMed multimedia module or print-based materials through concealed allocation; deviations from the intended 1:1 ratio resulted from participant no-shows. The intervention consisted of a 30-minute supervised self-learning session. In total, 103 participants were allocated to FOAMed (n=45) or print-based materials (n=58). Two coprimary outcomes were assessed: ECG interpretation accuracy and text-based ACS clinical decision-making. Secondary outcomes included subjective confidence, learning experience, and exploratory qualitative free-text responses. Outcome assessment was automated and blinded; mixed ANOVA was the primary analysis. The study was not prospectively registered because it assessed educational outcomes in health care professionals rather than patient health outcomes. RESULTS: All 103 participants completed the study. Both groups improved, with greater gains in the FOAMed group: ECG interpretation increased from 55% to 65.5% and text-based ACS clinical decision-making from 45% to 68%, versus 57% to 60% and from 47% to 63%, respectively, in the print-based group. Effect sizes were η²=0.055 for ECG interpretation and η²=0.044 for clinical decision-making. Exploratory subgroup analyses provided no evidence of differential effects across age, gender, or professional background and were likely underpowered. Qualitative responses (46 and 37 entries) provided contextual insights into perceived clarity, engagement, and practical relevance supporting the quantitative findings. CONCLUSIONS: This study is innovative in directly comparing a curated FOAMed multimedia module with selected print-based materials in an interprofessional emergency care population. It differs from existing research by focusing on subtle, emerging ischemic patterns and evaluating realistic, time-limited self-learning formats. The findings provide evidence that curated FOAMed resources can produce greater short-term improvements in ECG interpretation and text-based ACS clinical decision-making than traditional print-based materials in this setting. Although implications for clinical performance remain hypothetical, concise, high-quality digital modules may represent a practical supplement to structured continuing education in emergency care.

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