Faculty Bibliography

PURPOSE/OBJECTIVE:To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine. MATERIALS AND METHODS/METHODS:From a nationwide multicenter and multidisciplinary lumbar stenosis outcome study (LSOS) register 82 patients, undergoing MR scans of the lumbar spine due to clinical indication of spinal claudication, with a single level central or lateral severe LSS were included. In total 343 transaxial T2-weighted images of the lumbar spine were included from one to five levels (L1 to S1) per patient. One expert radiologist serving as reference standard rated LSS grade according to a standard four-point (normal to severe) as well as to an eight-point Schizas grading scale. DICOM data were then rescaled to a defined pixel size. Two independent readers performed qualitative ratings analogous to expert reader in addition to TA of spinal canals by manually placing two regions of interest (ROI) per image reflecting qualitative scales: (1) dural sac only (2) inner contour of the spinal canal including epidural fat and bilateral recesses. Interreader agreements of qualitative and quantitative parameters were assessed by Cohen's Kappa (κ) and intraclass correlation (ICC), respectively. TA feature reduction was performed by ICC threshold > 0.75. Remaining features were analyzed with machine learning algorithms (Weka 3 tool) for correlation with LSS grades using 10-fold cross validation. RESULTS:Qualitative ratings showed only moderate reproducibility for both LSS classification systems but high correlation with cut-off cross-sectional area (CSA) <130mm² for severe spinal stenosis. In quantitative TA of both ROIs, machine learning analysis with a decision tree classifier revealed higher performances for LSS grading compared to qualitative assessments using the reference CSA cut-off, respectively. CONCLUSION/CONCLUSIONS:Qualitative LSS grading independent of classification system shows moderate reproducibility. TA with machine learning offers highly reproducible quantitative parameters that increase accuracy for severe LSS detection with minor impact of grading score and CSA border definition.

BACKGROUND/AIMS/OBJECTIVE:The influence of external factors such as opioids and alcohol has been extensively investigated for various segments of the gastrointestinal tract. However, the association between their use and the development of esophagogastric junction outflow obstruction disorders (EGJOODs) is unknown. Therefore, the aim of this study is to analyze prevalence and clinical relevance of opioids and alcohol intake in patients with EGJOODs. METHODS:< 0.05 was considered significant. RESULTS:= 0.011) was associated with EGJOODs. Excessive alcohol consumption was especially frequent in the non-achalasia esophagogastric junction outflow obstruction subgroup (16.2%) and opioid use in the achalasia type III subgroup (20.0%). CONCLUSIONS:We found a significant association between EGJOODs and opioid as well as excessive alcohol consumption. This underlines the importance of detailed history taking regarding medication and ethanol consumption in patients with dysphagia. Further prospective studies on mechanisms undelaying esophagogastric junction dysfunction due to opioids or alcohol are warranted.

Recent research focusing on brown adipose tissue (BAT) function emphasizes its importance in systemic metabolic homeostasis. We show here that genetic and pharmacological inhibition of the mevalonate pathway leads to reduced human and mouse brown adipocyte function in vitro and impaired adipose tissue browning in vivo. A retrospective analysis of a large patient cohort suggests an inverse correlation between statin use and active BAT in humans, while we show in a prospective clinical trial that fluvastatin reduces thermogenic gene expression in human BAT. We identify geranylgeranyl pyrophosphate as the key mevalonate pathway intermediate driving adipocyte browning in vitro and in vivo, whose effects are mediated by geranylgeranyltransferases (GGTases), enzymes catalyzing geranylgeranylation of small GTP-binding proteins, thereby regulating YAP1/TAZ signaling through F-actin modulation. Conversely, adipocyte-specific ablation of GGTase I leads to impaired adipocyte browning, reduced energy expenditure, and glucose intolerance under obesogenic conditions, highlighting the importance of this pathway in modulating brown adipocyte functionality and systemic metabolism.

Impaired cerebral vascular reactivity (CVR) increases long-term stroke risk. Obstructive sleep apnoea (OSA) is associated with peripheral vascular dysfunction and vascular events. The aim of this trial was to evaluate the effect of continuous positive airway pressure (CPAP) withdrawal on CVR.41 OSA patients (88% male, mean age 57±10 years) were randomised to either subtherapeutic or continuation of therapeutic CPAP. At baseline and after 2 weeks, patients underwent a sleep study and magnetic resonance imaging (MRI). CVR was estimated by quantifying the blood oxygen level-dependent (BOLD) MRI response to breathing stimuli.OSA did recur in the subtherapeutic CPAP group (mean treatment effect apnoea-hypopnoea index +38.0 events·h^-1, 95% CI 24.2-52.0; p<0.001) but remained controlled in the therapeutic group. Although there was a significant increase in blood pressure upon CPAP withdrawal (mean treatment effect +9.37 mmHg, 95% CI 1.36-17.39; p=0.023), there was no significant effect of CPAP withdrawal on CVR assessed via BOLD MRI under either hyperoxic or hypercapnic conditions.Short-term CPAP withdrawal did not result in statistically significant changes in CVR as assessed by functional MRI, despite the recurrence of OSA. We thus conclude that, unlike peripheral endothelial function, CVR is not affected by short-term CPAP withdrawal.

OBJECTIVE:High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas. METHODS:In this study, 20,578 mammography single views from 5221 different patients (58.3 ± 11.5 years) were downloaded from the picture archiving and communications system of our institution and automatically sorted according to the ACR density (a-d) provided by the corresponding radiological reports. A dCNN with 11 convolutional layers and 3 fully connected layers was trained and validated on an augmented dataset. The model was finally tested on two different datasets against: i) the radiological reports and ii) the consensus decision of two human readers. None of the test datasets was part of the dataset used for the training and validation of the algorithm. RESULTS:The optimal number of epochs was 91 for medio-lateral oblique (MLO) projections and 94 for cranio-caudal projections (CC), respectively. Accuracy for MLO projections obtained on the validation dataset was 90.9% (CC: 90.1%). Tested on the first test dataset of mammographies (850 MLO and 880 CC), the algorithm showed an accordance with the corresponding radiological reports of 71.7% for MLO and of 71.0% for CC. The agreement with the radiological reports improved in the differentiation between dense and fatty breast for both projections (MLO = 88.6% and CC = 89.9%). In the second test dataset of 200 mammographies, a good accordance was found between the consensus decision of the two readers on both, the MLO-model (92.2%) and the right craniocaudal-model (87.4%). In the differentiation between fatty (ACR A/B) and dense breasts (ACR C/D), the agreement reached 99% for the MLO and 96% for the CC projections, respectively. CONCLUSIONS:The dCNN allows for accurate classification of breast density based on the ACR BI-RADS system. The proposed technique may allow accurate, standardized, and observer independent breast density evaluation of mammographies. ADVANCES IN KNOWLEDGE/CONCLUSIONS:Standardized classification of mammographies by a dCNN could lead to a reduction of falsely classified breast densities, thereby allowing for a more accurate breast cancer risk assessment for the individual patient and a more reliable decision, whether additional ultrasound is recommended.

OBJECTIVES/OBJECTIVE:To define normal signal intensity values of amide proton transfer-weighted (APTw) magnetic resonance (MR) imaging in different brain regions. MATERIALS AND METHODS/METHODS:= 2 μT, duration 2 s, 100% duty cycle) and 2D T2-weighted turbo spin echo (TSE) images were acquired. Postprocessing (image fusion, ROI measurements of APTw intensity values in 22 different brain regions) was performed and controlled by two independent neuroradiologists. Values were measured separately for each brain hemisphere. A subject was scanned both in prone and supine position to investigate differences between hemispheres. A mixed model on a 5% significance level was used to assess the effect of gender, brain region and side on APTw intensity values. RESULTS:= 0.24). APTw intensity values between the left and the right side were partially reversed after changing the position of one subject from supine to prone. CONCLUSION/CONCLUSIONS:We determined normal baseline APTw intensity values in different anatomical localizations in healthy subjects. APTw intensity values differed both between anatomical regions and between left and right brain hemisphere.