Radiology
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Purpose To quantify liver fat and liver iron content by measurement of confounder-corrected proton density fat fraction (PDFF) and R2* and to identify clinical associations for fatty liver disease and liver iron overload and their prevalence in a large-scale population-based study. Materials and Methods From 2008 to 2013, 2561 white participants (1336 women; median age, 52 years; 25th and 75th quartiles, 42 and 62 years) were prospectively recruited to the Study of Health in Pomerania (SHIP). Complex chemical shift-encoded magnetic resonance (MR) examination of the liver was performed, from which PDFF and R2* were assessed. ⋯ Liver iron content correlated with mean serum corpuscular hemoglobin, male sex, and age. Conclusion In a white German population, the prevalence of fatty liver diseases and liver iron overload is 42.2% (1082 of 2561) and 17.4% (447 of 2561). Whereas liver fat is associated with predictors related to the metabolic syndrome, liver iron content is mainly associated with mean serum corpuscular hemoglobin. © RSNA, 2017 Online supplemental material is available for this article.
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Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. ⋯ Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted κ of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P < .001). Conclusion An automated machine learning computer system was created to detect, anatomically localize, and categorize vertebral compression fractures at high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. © RSNA, 2017 Online supplemental material is available for this article.