In this proof-of-concept study, machine learning–assisted CT colonography analysis allowed for differentiation of benign and premalignant colorectal polyps at high diagnostic performance associated... Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning–based differentiation of benign and premal...

This work demonstrated the viability of postmortem x-ray phase-contrast micro-CT for the volumetric high-spatial-resolution imaging and three-dimensional rendering of human cadaveric spinal cord an... Background Modern high-spatial-resolution radiologic methods enable increasingly detailed volumetric postmortem investigations of human neuroanatomy for diagnostic, research, and educational purpos...

Background and Purpose: Basilar artery occlusion is associated with high morbidity and mortality. Optimal imaging and treatment strategy are still controversial and prognosis estimation challenging. We, therefore, aimed to determine the predictive value of computed tomography perfusion (CTP) parameters for functional outcome in patients with basilar artery occlusion in the context of endovascular treatment. Methods: Patients with basilar artery occlusion who underwent endovascular treatment were selected from a prospectively acquired cohort. Ischemic changes were assessed with the posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score on noncontrast computed tomography, computed tomography angiography (CTA) source images, and CTP maps. Basilar artery on CTA score, posterior-circulation CTA score, and posterior-circulation collateral score were evaluated on CTA. Perfusion deficit volumes were quantified on CTP maps. Good functional outcome was defined as modified Rankin Scale score ≤3 at 90 days. Statistical analysis included binary logistic regressions and receiver operating characteristics analyses. Results: Among 49 patients who matched the inclusion criteria, 24 (49.0%) achieved a good outcome. In univariate analysis, age, National Institutes of Health Stroke Scale score on admission, posterior cerebral artery involvement, absence of or hypoplastic posterior communicating arteries, basilar artery on CTA score, posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score, and perfusion deficit volumes on all CTP parameter maps presented significant association with functional outcome ( P <0.05). In multivariate analyses, Basilar artery on CTA score, posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score (odds ratio range, 1.31–2.10 [95% CI, 1.00–7.24]), and perfusion deficit volumes on all CTP maps (odds ratio range, 0.77–0.98 [95% CI, 0.63–1.00]) remained as independent outcome predictors. Cerebral blood flow deficit volume yielded the best performance for the classification of good clinical outcome with an area under the curve of 0.92 (95% CI, 0.84–0.99). Age and admission National Institutes of Health Stroke Scale had lower discriminatory power (area under the curve, <0.7). Conclusions: CTP imaging parameters contain prognostic information for functional outcome in patients with stroke due to basilar artery occlusion and may identify patients with higher risk of disability at an early stage of hospitalization.

Introduction As white matter hyperintensities (WMHs) of the brain are associated with an increased risk of stroke, cognitive decline, and depression, elucidating the associated risk factors is important. In addition to age and hypertension, pre-diabetes and diabetes may play important roles in the development of WMHs. Previous studies have, however, shown conflicting results. We aimed to investigate the effect of diabetes status and quantitative markers of glucose metabolism on WMH volume in a population-based cohort without prior cardiovascular disease. Research design and methods 400 participants underwent 3 T MRI. WMHs were manually segmented on 3D fluid-attenuated inversion recovery images. An oral glucose tolerance test (OGTT) was administered to all participants not previously diagnosed with diabetes to assess 2-hour serum glucose concentrations. Fasting glucose concentrations and glycated hemoglobin (HbA1c) levels were measured. Zero-inflated negative binomial regression analyses of WMH volume and measures of glycemic status were performed while controlling for cardiovascular risk factors and multiple testing. Results The final study population comprised 388 participants (57% male; age 56.3±9.2 years; n=98 with pre-diabetes, n=51 with diabetes). Higher WMH volume was associated with pre-diabetes (p=0.001) and diabetes (p=0.026) compared with normoglycemic control participants after adjustment for cardiovascular risk factors. 2-hour serum glucose (p<0.001), but not fasting glucose (p=0.389) or HbA1c (p=0.050), showed a significant positive association with WMH volume after adjustment for cardiovascular risk factors. Conclusion Our results indicate that high 2-hour serum glucose concentration in OGTT, but not fasting glucose levels, may be an independent risk factor for the development of WMHs, with the potential to inform intensified prevention strategies in individuals at risk of WMH-associated morbidity.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.