Long-Term Follow-Up of Cerebral Aneurysms Completely Occluded at 6 Months After Intervention with the Woven EndoBridge (WEB) Device: a Retrospective Multicenter Observational Study.

The Woven EndoBridge (WEB) device has been widely used to treat intracranial wide neck bifurcation aneurysms. Initial studies have demonstrated that approximately 90% of patients have same or improved long-term aneurysm occlusion after the initial 6-month follow up. The aim of this study is to assess the long-term follow-up in aneurysms that have achieved complete occlusion at 6 months. We also compared the predictive value of different imaging modalities used. This is an analysis of a prospectively maintained database across 13 academic institutions. We included patients with previously untreated cerebral aneurysms embolized using the WEB device who achieved complete occlusion at first follow-up and had available long-term follow-up. A total of 95 patients with a mean age of 61.6 ± 11.9 years were studied. The mean neck diameter and height were 3.9 ± 1.3 mm and 6.0 ± 1.8 mm, respectively. The mean time to first and last follow-up was 5.4 ± 1.8 and 14.1 ± 12.9 months, respectively. Out of all the aneurysms that were completely occluded at 6 months, 84 (90.3%) showed complete occlusion at the final follow-up, and 11(11.5%) patients did not achieve complete occlusion. The positive predictive value (PPV) of complete occlusion at first follow was 88.4%. Importantly, this did not differ between digital subtraction angiography (DSA), magnetic resonance angiography (MRA), or computed tomography angiography (CTA). This study underlines the importance of repeat imaging in patients treated with the WEB device even if complete occlusion is achieved short term. Follow-up can be performed using DSA, MRA or CTA with no difference in positive predictive value.

Complex Morphologic Analysis of Cerebral Aneurysms Through the Novel Use of Fractal Dimension as a Predictor of Rupture Status: A Proof of Concept Study.

BACKGROUND: Aneurysm morphology has been correlated with rupture. Previous reports identified several morphologic indices that predict rupture status, but they measure only specific qualities of the morphology of an aneurysm in a semiquantitative fashion. Fractal analysis is a geometric technique whereby the overall complexity of a shape is quantified through the calculation of a fractal dimension (FD). By progressively altering the scale of measurement of a shape and determining the number of segments required to incorporate the entire shape, a noninteger value for the dimension of the shape is derived. We present a proof-of-concept study to calculate the FD of an aneurysm for a small cohort of patients with aneurysms in 2 specific locations to determine whether FD is associated with aneurysm rupture status. METHODS: Twenty-nine aneurysms of the posterior communicating and middle cerebral arteries were segmented from computed tomography angiograms in 29 patients. FD was calculated using a standard box-counting algorithm extended for use with three-dimensional shapes. Nonsphericity index and undulation index (UI) were used to validate the data against previously reported parameters associated with rupture status. RESULTS: Nineteen ruptured and 10 unruptured aneurysms were analyzed. Through logistic regression analysis, lower FD was found to be significantly associated with rupture status (P = 0.035; odds ratio, 0.64; 95% confidence interval, 0.42-0.97 per FD increment of 0.05). CONCLUSIONS: In this proof-of-concept study, we present a novel approach to quantify the geometric complexity of intracranial aneurysms through FD. These data suggest an association between FD and patient-specific aneurysm rupture status.

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

BACKGROUND. Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been reported primarily in adults. OBJECTIVE. The purpose of this study was to use an automated deep learning (DL) pipeline for muscle segmentation on abdominal CT to define normative age- and sex-based values for pediatric muscle cross-sectional area as a guide for diagnosis of sarcopenia in children. METHODS. This retrospective study reviewed records of patients who underwent abdominal CT at Cincinnati Children's Hospital Medical Center from January 1, 2009, to January 3, 2019. Patients were excluded on the basis of age outside of the eligible range (2.00-18.99 years), body mass index (BMI) outside of 5-95% age-based percentiles using CDC and WHO growth charts, known medical condition, medication use, support devices, surgery, or missing axial images at the L3 level. A previously validated automated DL pipeline was used to identify an axial slice at L3 and segment skeletal muscle to generate SMA and SMI. Pearson correlation coefficients were computed. Quantile regression analysis was used to plot SMA and SMI as functions of age and sex and to determine age- and sex-based percentile values. RESULTS. Of 8817 patients who underwent abdominal CT during the study period, 2168 (mean age, 12.3 ± 4.3 [SD] years; 1125 female patients, 1043 male patients) met inclusion criteria. Mean BMI-for-age percentile based on CDC and WHO growth charts was 64.8% ± 25.3% for female patients and 61.4% ± 25.8% for male patients. SMA showed strong correlation with weight, height, age, and BMI for male (0.79-0.94) and female (0.75-0.90) patients; SMI showed weak-to-moderate correlation with weight, height, age, and BMI for male (0.25-0.57) and female (0-0.43) patients. Normal SMA and SMI ranges for age and sex were expressed as curves and as a lookup table, identifying 54 male and 59 female patients with muscle measurements below the 5th percentile regression curve. CONCLUSION. By using an automated DL pipeline in a large sample of carefully selected children, normal ranges for SMA and SMI were calculated as functions of age and sex. CLINICAL IMPACT. The normative values should aid the diagnosis of sarcopenia in children.

Automated Segmentation of Abdominal Skeletal Muscle on Pediatric CT Scans Using Deep Learning.

PURPOSE: To automate skeletal muscle segmentation in a pediatric population using convolutional neural networks that identify and segment the L3 level at CT. MATERIALS AND METHODS: In this retrospective study, two sets of U-Net-based models were developed to identify the L3 level in the sagittal plane and segment the skeletal muscle from the corresponding axial image. For model development, 370 patients (sampled uniformly across age group from 0 to 18 years and including both sexes) were selected between January 2009 and January 2019, and ground truth L3 location and skeletal muscle segmentation were manually defined. Twenty percent (74 of 370) of the examinations were reserved for testing the L3 locator and muscle segmentation, while the remaining were used for training. For the L3 locator models, maximum intensity projections (MIPs) from a fixed number of central sections of sagittal reformats (either 12 or 18 sections) were used as input with or without transfer learning using an L3 localizer trained on an external dataset (four models total). For the skeletal muscle segmentation models, two loss functions (weighted Dice similarity coefficient [DSC] and binary cross-entropy) were used on models trained with or without data augmentation (four models total). Outputs from each model were compared with ground truth, and the mean relative error and DSC from each of the models were compared with one another. RESULTS: L3 section detection trained with an 18-section MIP model with transfer learning had a mean error of 3.23 mm ± 2.61 standard deviation, which was within the reconstructed image thickness (3 or 5 mm). Skeletal muscle segmentation trained with the weighted DSC loss model without data augmentation had a mean DSC of 0.93 ± 0.03 and mean relative error of 0.04 ± 0.04. CONCLUSION: Convolutional neural network models accurately identified the L3 level and segmented the skeletal muscle on pediatric CT scans.Supplemental material is available for this article.See also the commentary by Cadrin-Chênevert in this issue.© RSNA, 2021.

Blunt cerebrovascular injury in the geriatric population.

OBJECTIVE: Blunt cerebrovascular injury (BCVI) is associated with high rates of neurological morbidity and mortality. The detection and management of BCVI has improved with advances in imaging and sensitive screening protocols. Few studies have explored how these injuries specifically affect the geriatric population. The purpose of this retrospective analysis was to investigate the presentation and prognosis of BCVI in the elderly population and to assess its clinical implications in the management of these patients. METHODS: All patients presenting to the University of Cincinnati (UC) level I trauma center between February 2017 and December 2019 were screened for BCVI and entered into the prospectively maintained UC Neurotrauma Registry. Patients with BCVI confirmed by CT angiography underwent retrospective chart reviews to collect information regarding demographics, positive screening criteria, cause of injury, antithrombotic agent, injury location, Denver Grading Scale, hospital and ICU length of stay, and discharge disposition. Patients were divided into geriatric (age ≥ 65 years) and adult (age < 65 years) subgroups. Continuous variables were analyzed using the Student t-test and categorical variables with the Pearson chi-square test. RESULTS: Of 124 patients with BCVI, stratification by age yielded 23 geriatric and 101 adult patients. Injury in the geriatric group was associated with significantly higher mortality (p = 0.0194). The most common cause of injury in the elderly was falls (74%, 17/23; p < 0.0001), whereas motor vehicle accidents were most common in the adult group (38%, 38/100; p = 0.0642). With respect to the location of injury, carotid (p = 0.1171) and vertebral artery (p = 0.6981) injuries did not differ significantly for the geriatric group. The adult population presented more often with Denver grade I injuries (p < 0.0001), whereas the geriatric population presented with grade IV injuries (p = 0.0247). Elderly patients were more likely to be discharged to skilled nursing facilities (p = 0.0403) and adults to home or self-care (p = 0.0148). CONCLUSIONS: This study is the first to characterize BCVI to all cervical and intracranial vessels in the geriatric population. Older age at presentation is significantly associated with greater severity, morbidity, and mortality from injury, with no preference for the particular artery injured. These findings carry important clinical implications for adapting practice in an aging population.

Gastroenterology issues in schizophrenia: why the gut matters.

Genetic and environmental studies implicate immune pathologies in schizophrenia. The body's largest immune organ is the gastrointestinal (GI) tract. Historical associations of GI conditions with mental illnesses predate the introduction of antipsychotics. Current studies of antipsychotic-naïve patients support that gut dysfunction may be inherent to the schizophrenia disease process. Risk factors for schizophrenia (inflammation, food intolerances, Toxoplasma gondii exposure, cellular barrier defects) are part of biological pathways that intersect those operant in the gut. Central to GI function is a homeostatic microbial community, and early reports show that it is disrupted in schizophrenia. Bioactive and toxic products derived from digestion and microbial dysbiosis activate adaptive and innate immunity. Complement C1q, a brain-active systemic immune component, interacts with gut-related schizophrenia risk factors in clinical and experimental animal models. With accumulating evidence supporting newly discovered gut-brain physiological pathways, treatments to ameliorate brain symptoms of schizophrenia should be supplemented with therapies to correct GI dysfunction.