Faculty Bibliography

PURPOSE/OBJECTIVE:To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures. MATERIALS AND METHODS/METHODS:Standard CT scans of 58 patients with insufficiency fractures of the spine, performed between 2006 and 2013, were analyzed retrospectively. Every included patient had at least two CT scans. Intact vertebrae in a first scan that either fractured ("unstable") or remained intact ("stable") in the consecutive scan were manually segmented on mid-sagittal reformations. TA features for all vertebrae were extracted using open-source software (MaZda). In a paired control study, all vertebrae of the study cohort "cases" and matched controls were classified using ROC analysis of Hounsfield unit (HU) measurements and supervised ML techniques. In a within-subject vertebra comparison, vertebrae of the cases were classified into "unstable" and "stable" using identical techniques. RESULTS:One hundred twenty vertebrae were included. Classification of cases/controls using ROC analysis of HU measurements showed an AUC of 0.83 (95% confidence interval [CI], 0.77-0.88), and ML-based classification showed an AUC of 0.97 (CI, 0.97-0.98). Classification of unstable/stable vertebrae using ROC analysis showed an AUC of 0.52 (CI, 0.42-0.63), and ML-based classification showed an AUC of 0.64 (CI, 0.61-0.67). CONCLUSION/CONCLUSIONS:TA combined with ML allows to identifying patients who will suffer from vertebral insufficiency fractures in standard CT scans with high accuracy. However, identification of single vertebra at risk remains challenging. KEY POINTS/CONCLUSIONS:• Bone texture analysis combined with machine learning allows to identify patients at risk for vertebral body insufficiency fractures on standard CT scans with high accuracy. • Compared to mere Hounsfield unit measurements on CT scans, application of bone texture analysis combined with machine learning improve fracture risk prediction. • This analysis has the potential to identify vertebrae at risk for insufficiency fracture and may thus increase diagnostic value of standard CT scans.

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.

OBJECTIVES/OBJECTIVE:To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS). METHODS AND MATERIALS/METHODS:One thousand nineteen breast ultrasound images from 582 patients (age 56.3 ± 11.5 years) were linked to the corresponding radiological report. Lesions were categorized into the following classes: no tissue, normal breast tissue, BI-RADS 2 (cysts, lymph nodes), BI-RADS 3 (non-cystic mass), and BI-RADS 4-5 (suspicious). To test the accuracy of the dCNN, one internal dataset (101 images) and one external test dataset (43 images) were evaluated by the dCNN and two independent readers. Radiological reports, histopathological results, and follow-up examinations served as reference. The performances of the dCNN and the humans were quantified in terms of classification accuracies and receiver operating characteristic (ROC) curves. RESULTS:In the internal test dataset, the classification accuracy of the dCNN differentiating BI-RADS 2 from BI-RADS 3-5 lesions was 87.1% (external 93.0%) compared with that of human readers with 79.2 ± 1.9% (external 95.3 ± 2.3%). For the classification of BI-RADS 2-3 versus BI-RADS 4-5, the dCNN reached a classification accuracy of 93.1% (external 95.3%), whereas the classification accuracy of humans yielded 91.6 ± 5.4% (external 94.1 ± 1.2%). The AUC on the internal dataset was 83.8 (external 96.7) for the dCNN and 84.6 ± 2.3 (external 90.9 ± 2.9) for the humans. CONCLUSION/CONCLUSIONS:dCNNs may be used to mimic human decision-making in the evaluation of single US images of breast lesion according to the BI-RADS catalog. The technique reaches high accuracies and may serve for standardization of highly observer-dependent US assessment. KEY POINTS/CONCLUSIONS:• Deep convolutional neural networks could be used to classify US breast lesions. • The implemented dCNN with its sliding window approach reaches high accuracies in the classification of US breast lesions. • Deep convolutional neural networks may serve for standardization in US BI-RADS classification.

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.

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.

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.

BACKGROUND: Inflammation plays a fundamental role in mediating all stages of atherosclerosis. Given the higher prevalence of inflammatory rheumatologic conditions in women and the female propensity towards worse cardiovascular outcomes, refined strategies are needed to better identify the high-risk female cardiovascular phenotype. OBJECTIVES/OBJECTIVE: This article aims to assess sex-specific links between inflammatory processes and the development and progression of ischemic heart disease. PATIENTS AND METHODS/METHODS:Tc-tetrofosmin single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). RESULTS: = 0.014). CONCLUSION/CONCLUSIONS: A strong link between bone marrow metabolism and impaired myocardial function and perfusion was observed in women, but not in men. Our data suggest that novel biomarkers of inflammation might help to identify women at risk for ischemic cardiomyopathy and to tailor disease management to the female cardiovascular phenotype.

Background Recent studies have reported the additive value of combined gallium 68 (⁶⁸Ga)-labeled Glu-urea-Lys (Ahx)-HBED-CC ligand targeting the prostate-specific membrane antigen (PSMA) (hereafter called ⁶⁸Ga-PSMA-11) PET/MRI for the detection and localization of primary prostate cancer compared with multiparametric MRI. Purpose To compare the diagnostic accuracy and interrater agreement of multiparametric MRI and ⁶⁸Ga-PSMA-11 PET/MRI for the detection of extracapsular extension (ECE) and seminal vesicle infiltration (SVI) in patients with prostate cancer. Materials and Methods Retrospective analysis of 40 consecutive men who underwent multiparametric MRI and ⁶⁸Ga-PSMA-11 PET/MRI within 6 months for suspected prostate cancer followed by radical prostatectomy between April 2016 and July 2018. Four readers blinded to clinical and histopathologic findings rated the probability of ECE and SVI at multiparametric MRI and PET/MRI by using a five-point Likert-type scale. The prostatectomy specimen served as the reference standard. Accuracy was assessed with a multireader multicase analysis and by calculating reader-average areas under the receiver operating characteristics curve (AUCs), sensitivity, and specificity for ordinal and dichotomized data in a region-specific and patient-specific approach. Interrater agreement was assessed with the Fleiss multirater κ. Results For multiparametric MRI versus PET/MRI in ECE detection, respectively, AUC, sensitivity, and specificity in the region-specific analysis were 0.67 and 0.75 (P = .07), 28% (21 of 76) and 47% (36 of 76) (P = .09), and 94% (529 of 564) and 90% (509 of 564) (P = .007). For the patient-specific analysis, AUC, sensitivity, and specificity were 0.66 and 0.73 (P = .19), 46% (22 of 48) and 69% (33 of 48) (P = .04), and 75% (84 of 112) and 67% (75 of 112) (P = .19), respectively. For multiparametric MRI versus PET/MRI in SVI detection, respectively, AUC, sensitivity, and specificity of the region-specific analysis were 0.66 and 0.74 (P = .21), 35% (seven of 20) and 50% (10 of 20) (P = .25), and 98% (295 of 300) and 94% (282 of 300) (P < .001). For the patient-specific analysis, AUC, sensitivity, and specificity were 0.65 and 0.79 (P = .25), 35% (seven of 20) and 55% (11 of 20) (P = .20), and 98% (137 of 140) and 94% (131 of 140) (P = .07), respectively. Interrater reliability for multiparametric MRI versus PET/MRI did not differ for ECE (κ, 0.46 vs 0.40; P = .24) and SVI (κ, 0.23 vs 0.33; P = .39). Conclusion Our results suggest that gallium 68 (⁶⁸Ga)-labeled Glu-urea-Lys (Ahx)-HBED-CC ligand targeting the prostate-specific membrane antigen (PSMA) (⁶⁸Ga-PSMA-11) PET/MRI and multiparametric MRI perform similarly for local staging of prostate cancer in patients with intermediate-to-high-risk prostate cancer. The increased sensitivity of ⁶⁸Ga-PSMA-11 PET/MRI for the detection of extracapsular disease comes at the cost of a slightly reduced specificity. © RSNA, 2019.

PURPOSE/OBJECTIVE:To train a CycleGAN on downscaled versions of mammographic data to artificially inject or remove suspicious features, and to determine whether these AI-mediated attacks can be detected by radiologists. MATERIAL AND METHODS/METHODS:From two publicly available datasets, BCDR and INbreast, we selected 680 images with and without lesions as training data. An internal dataset (n = 302 cancers, n = 590 controls) served as test data. We ran two experiments (256 × 256 px and 512 × 408 px) and applied the trained model to the test data. Three radiologists read a set of images (modified and originals) and rated the presence of suspicious lesions on a scale from 1 to 5 and the likelihood of the image being manipulated. The readout was evaluated by multiple reader multiple case receiver operating characteristics (MRMC-ROC) analysis using the area under the curve (AUC). RESULTS:At the lower resolution, the overall performance was not affected by the CycleGAN modifications (AUC 0.70 vs. 0.76, p = 0.67). However, one radiologist exhibited lower detection of cancer (0.85 vs 0.63, p = 0.06). The radiologists could not discriminate between original and modified images (0.55, p = 0.45). At the higher resolution, all radiologists showed significantly lower detection rate of cancer in the modified images (0.80 vs. 0.37, p < 0.001), however, they were able to detect modified images due to better visibility of artifacts (0.94, p < 0.0001). CONCLUSION/CONCLUSIONS:Our proof-of-concept study shows that CycleGAN can implicitly learn suspicious features and artificially inject or remove them in existing images. The applicability of the method is currently limited by the small image size and introduction of artifacts.

PURPOSE/OBJECTIVE:To compare the correlation of transition zone prostate-specific antigen density (TZPSAD) versus whole gland (WG) density (PSAD) with Gleason score. METHODS:In this single-center, retrospective cohort study, men undergoing MRI of the prostate in 2015 and 2016 who had a transperineal template saturation biopsy within 6 months of MRI were included (n = 178; median age 64 y, interquartile range [IQR] 58-68 y; PSA 6.6 ng/ml, 4.6-9.7 ng/ml). The WG and TZ were segmented voxel-wise on T2-weighted transverse planes. The volumes and corresponding PSA-densities were calculated. Correlations with the Gleason score were assessed with Spearman's rho. Optimal thresholds of the PSA densities were computed using the Youden Index of the receiver-operating-characteristics curve. A p-value of ≤ 0.05 was considered statistically significant. RESULTS:) for discrimination between Gleason 3 + 4 and 4 + 3. CONCLUSION/CONCLUSIONS:may help in risk stratification of men with suspected PCa.