Marcel Koek

Abdominal Fat in Children Measured by Ultrasound and Computed Tomography

The prevalence of childhood obesity is increasing rapidly. Visceral fat plays an important role in the pathogenesis of metabolic and cardiovascular diseases. Currently, computed tomography (CT) is broadly seen as the most accurate method of determining the amount of visceral fat. The main objective was to examine whether measures of abdominal visceral fat can be determined by ultrasound in children and whether CT can be replaced by ultrasound for this purpose. To assess whether preperitoneal fat thickness and area are good approximations of visceral fat at the umbilical level, we first retrospectively examined 47 CT scans of nonobese children (body mass index <30kg/m(2); median age 7.9 y [95% range 1.2 to 16.2]). Correlation coefficients between visceral and preperitoneal fat thickness and area were 0.58 (p<0.001) and 0.76 (p<0.001), respectively. Then, to assess how preperitoneal and subcutaneous fat thicknesses and areas measured by ultrasound compare with these parameters in CT, we examined 34 nonobese children (median age 9.5 [95% range 0.3 to 17.0]) by ultrasound and CT. Ultrasound measurements of preperitoneal and subcutaneous fat were correlated with CT measurements, with correlation coefficients ranging from 0.75-0.97 (all p<0.001). Systematic differences of up to 24.0cm(2) for preperitoneal fat area (95% confidence interval -29.9 to 77.9cm(2)) were observed when analyzing the results described by the Bland-Altman method. Our findings suggest that preperitoneal fat can be used as an approximation for visceral fat in children and that measuring abdominal fat with ultrasound in children is a valid method for epidemiological and clinical studies. However, the exact agreement between the ultrasound and CT scan was limited, which indicates that ultrasound should be used carefully for obtaining exact fat distribution measurements in individual children.

Sarcopenia impairs survival in patients with potentially curable hepatocellular carcinoma.

A reduction in skeletal muscle mass (sarcopenia) independently predicts poor survival in patients with hepatocellular carcinoma (HCC) undergoing treatment with curative intent. Whether this is due to an increased risk of recurrence and disease specific death, or due to an increased risk of postoperative morbidity and mortality is currently unclear. In this study, we investigate the association between sarcopenia and death in a cohort of HCC patients undergoing treatment with curative intent.

A comparative study of software programmes for cross-sectional skeletal muscle and adipose tissue measurements on abdominal computed tomography scans of rectal cancer patients

The association between body composition (e.g. sarcopenia or visceral obesity) and treatment outcomes, such as survival, using single‐slice computed tomography (CT)‐based measurements has recently been studied in various patient groups. These studies have been conducted with different software programmes, each with their specific characteristics, of which the inter‐observer, intra‐observer, and inter‐software correlation are unknown. Therefore, a comparative study was performed.

Small cortical infarcts: prevalence, determinants, and cognitive correlates in the general population

Background Cortical brain infarcts are defined as infarcts involving cortical gray matter, but may differ considerably in size. It is unknown whether small cortical infarcts have a similar clinical phenotype as larger counterparts. We investigated prevalence, determinants, and cognitive correlates of small cortical infarcts in the general population and compared these with large cortical infarcts and lacunar infarcts. Methods Four thousand nine hundred five nondemented individuals (age 63.95 ± 10.99) from a population-based study were included. Infarcts were rated on magnetic resonance imaging and participants were classified according to mean infarct diameter into small (≤15 mm in largest diameter) or large (>15 mm) cortical infarcts, lacunar infarcts, or a combination of subtypes. Spatial distribution maps were created for manually labeled small and large infarcts. Participants underwent cognitive testing. Analyses were performed using multinomial regression and analysis of covariance. Results Three hundred eighty-one (7.8%) persons had any infarct on magnetic resonance imaging, among whom 54 with small (1.1%) and 77 (1.6%) with large cortical infarcts. Small cortical infarcts were mainly localized in external watershed areas, whereas large cortical infarcts were localized primarily in large arterial territories. Age (odds ratio = 1.06; 95% confidence interval = 1.02, 1.09), male gender (1.98; 1.01, 3.92), and smoking (2.55; 1.06, 6.14) were determinants of small cortical infarcts. Participants with these infarcts had worse scores in delayed memory, processing speed, and attention tests than persons without infarcts, even after adjustment for cardiovascular risk factors. Conclusions In the elderly, small cortical infarcts appear as frequent as large infarcts but in different localization. Our results suggest that small cortical infarcts share cardiovascular risk factors and cognitive correlates with large cortical, but also with lacunar infarcts.

Visceral adipose tissue: the link with esophageal adenocarcinoma

Abstract Objective. There is strong evidence for an association between obesity and esophageal adenocarcinoma (EAC). This study investigated the association between directly measured visceral adipose tissue and the risk of EAC. Methods. In a case–control setting, we measured visceral adipose tissue in patients with EAC and healthy controls. Visceral adipose tissue was determined by abdominal CT. Exclusion criteria were uninterpretable CT scans and severe comorbidity. Controls were healthy volunteers undergoing screening CT colonography. Cross-sectional areas of visceral and subcutaneous adipose tissues were measured in cm2 at L3/L4. Values of adipose tissue of EAC patients were extrapolated to stage 0 and compared to controls. The association between visceral adipose tissue and EAC was calculated with least-squares regression, adjusted for age, sex and TNM stage. Results. We included 175 EAC patients and 251 controls. While body mass index was similar in EAC patients (26.1 kg/m2) and controls (26.2 kg/m2), visceral adipose tissue was significantly higher in EAC patients at stage 0 than in controls (276 vs. 231 cm2; p = 0.015). Regarding subcutaneous adipose tissue, there was no difference. Conclusions. Patients with EAC have significantly higher visceral adipose tissue than healthy controls. Visceral adipose tissue is a risk factor in the development of EAC and seems to be more important than obesity alone.

Fastr: A Workflow Engine for Advanced Data Flows in Medical Image Analysis

With the increasing number of datasets encountered in imaging studies, the increasing complexity of processing workflows, and a growing awareness for data stewardship, there is a need for managed, automated workflows. In this paper we introduce Fastr, an automated workflow engine with support for advanced data flows. Fastr has built-in data provenance for recording processing trails and ensuring reproducible results. The extensible plugin-based design allows the system to interface with virtually any image archive and processing infrastructure. This workflow engine is designed to consolidate quantitative imaging biomarker pipelines in order to enable easy application to new data.

XNAT imaging platform for BioMedBridges and CTMM TraIT

* Correspondence: s.klein@erasmusmc.nl Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, 3000CA Rotterdam, the Netherlands Full list of author information is available at the end of the article Figure 1 XNAT user interface. Example project with magnetic resonance imaging (MRI) data of the brain. Klein et al. Journal of Clinical Bioinformatics 2015, 5(Suppl 1):S18 http://www.jclinbioinformatics.com/content/5/S1/S18 JOURNAL OF CLINICAL BIOINFORMATICS

Quantitative imaging biomarkers in neurologic disease: Population study perspective

The capacity of recognizing the first signs of disease has enormous socio-economic benefits. Population studies have the potential to see disease develop before your eyes, and when including advanced imaging techniques in these studies, literally so. Population imaging studies, especially when complemented with other biomedical and genetic data, provide unique databases that can be exploited with advanced analysis and search techniques for discovering methods for early detection and prediction of disease. This new way of medical research will have considerable impact in the practice of medicine at large. In this presentation we will focus on the development of quantitative imaging biomarkers in neurology using imaging data acquired in a population setting. Currently, effective treatment strategies are lacking in e.g. dementia and stroke. In order to develop such strategies, improved understanding of the early, preclinical stages, of disease, is essential. Quantitative imaging biomarkers for neurologic disease are developed within the context of the Rotterdam Study, a prospective population based study of the causes and determinants of chronic diseases in the elderly that was initiated in 1995. MR brain imaging was performed during this study in random subsets in 1995 and 1999, and since 2005, MR brain imaging is part of the core protocol of the Rotterdam Study. The large scale acquisition of MR brain imaging within the Rotterdam Study allows us to study whether morphologic brain pathology is already present years before clinical onset of neurologic disease, and whether MRI based measurements may be used for prognosis. More information on the Rotterdam Scan Study can be found in [1]. Within the context of the Rotterdam Scan Study, a standardized and validated image analysis workflow is being developed to enable the objective, accurate, and reproducible extraction of relevant parameters describing brain anatomy, possible brain pathologies, and brain connectivity from multispectral MRI data. Image processing in the Rotterdam Scan Study has four main goals: First, owing to the sheer size and complexity of the imaging database being generated, automation of the tedious task of manual analysis is required. Second, qualitative image assessment should be replaced by objective quantitative analyses as much as possible. Third, we aim to limit or avoid altogether inter- and intraobserver variability. Fourth, image processing allows the extraction of relevant image-derived parameters that would not be feasible manually or cannot be assessed visually. This presentation will provide an overview of different quantitative imaging biomarkers that have been developed, or are currently developed as part of the Rotterdam Scan studies. These include brain tissue quantification (grey matter, white matter, also quantified per lobe), quantification of cerebrospinal fluid, volume and shape of neurostructures such as the hippocampus, ventricles and cerebellum, brain connectivity based on diffusion tensor MRI, and vascular brain pathologies such as white matter lesions and microbleeds.

Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: A clinical evaluation of diagnostic accuracy

Objectives To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting. Methods In 42 memory clinic patients, we evaluated whether automated quantification of brain tissue volumes, hippocampal volume and white matter lesion volume improves diagnostic accuracy for Alzheimers disease (AD) and frontotemporal dementia (FTD), compared to visual interpretation. Reference data were derived from a dementia-free aging population (n = 4915, aged >45 years), and were expressed as age- and sex-specific percentiles. Experienced radiologists determined the most likely imaging-based diagnosis based on structural brain MRI using three strategies (visual assessment of MRI only, quantitative normative information only, or a combination of both). Diagnostic accuracy of each strategy was calculated with the clinical diagnosis as the reference standard. Results Providing radiologists with only quantitative data decreased diagnostic accuracy both for AD and FTD compared to conventional visual rating. The combination of quantitative with visual information, however, led to better diagnostic accuracy compared to only visual ratings for AD. This was not the case for FTD. Conclusion Quantitative assessment of structural brain MRI combined with a reference standard in addition to standard visual assessment may improve diagnostic accuracy in a memory clinic setting.

No Significant Differences in Abdominal Fat Distribution Between Patients With Esophageal Adenocarcinoma, Esophageal Squamous Cell Carcinoma and Healthy Population Controls

G A A b st ra ct s and 66.4% for the non-HNC group (p=0.33, HR=1.36, 95% CI 0.72-2.72). [Conclusions] Patients with superficial esophageal cancer who had concurrent HNC were more likely to be relatively young males with higher Brinkman indexes and multiple Lugol voiding lesions than those without HNC. As recurrence rates were higher in patients with HNC than in those without, we should more closely observe HNC patients after endoscopic treatment of superficial esophageal cancer.