Low and Borderline Ankle-Brachial Index Is Associated With Intracranial Aneurysms: A Retrospective Cohort Study.

BACKGROUND AND OBJECTIVES: A low ankle-brachial index (ABI) has been linked to systemic inflammation and an elevated risk of cardiovascular events, most notably myocardial infarction and stroke. Intracranial aneurysms (IAs) share similar risk factors with other cardiovascular diseases. However, the association between low ABI and IAs has not been sufficiently investigated. Our objective was to investigate the potential connection between ABI values and the prevalence of unruptured IAs. METHODS: This retrospective cohort study reviewed 2751 patients who had ABI measurements at a public tertiary hospital from January 2011 to December 2013. Patients with available cerebrovascular imaging or a diagnosis of ruptured IA were included in the study (n = 776) to examine the association between ABI and saccular IAs. The patients were classified into 4 groups: low ABI (≤0.9, n = 464), borderline ABI (0.91-0.99; n = 47), high ABI (>1.4, n = 57), and normal ABI (1.00-1.40; n = 208). RESULTS: The prevalence of IAs was 20.3% (18.1% unruptured IAs) in the low ABI group, 14.9% (12.8% unruptured IAs) in the borderline ABI group, 7.0% (5.3% unruptured IAs) in the high ABI group, and 2.4% (1.9% unruptured IAs) in the normal ABI group (P < .001). There were no significant differences in the prevalence of ruptured IAs between the ABI groups (P = .277). Sex- and age-adjusted multinomial regression, including clinically relevant variables, revealed that low ABI (odds ratio [OR], 13.02; 95% CI, 4.01-42.24), borderline ABI (OR, 8.68; 95% CI, 2.05-36.69), and smoking history (OR, 2.01; 95% CI, 1.07-3.77) were associated with unruptured IAs. CONCLUSION: The prevalence of unruptured IAs was 9-fold higher in the low ABI group and nearly 7-fold higher in the borderline ABI group when compared with the normal ABI group. ABI measurements could be clinically relevant for identifying individuals at higher risk of IAs and may help guide screening and preventive strategies.

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.