Epidemiology of Schmorl's Node in the Thoracic Spine: A Subtype Analysis.

STUDY DESIGN: Retrospective observational study. OBJECTIVE: To describe the epidemiology of Schmorl's nodes (SN) of primarily developmental cause (SNd) and SN of primarily acquired cause (SNa) separately in the thoracic spine in subjects aged 35-90 years old. SUMMARY OF BACKGROUND DATA: The epidemiology of SN and its relationship with age and gender remain controversial. Based on a pathophysiological hypothesis and the different morphological characteristics, two subtypes of SN may exist and should be considered separately. PATIENTS AND METHODS: Chest CT scans of subjects who came to our institution for health check aged 35-90 years old were retrospectively reviewed. Presence or absence of SN was recorded for each thoracic vertebra. The SNs were further classified into SNd and SNa. The prevalence, location and relationship with age, gender and bone mineral density (BMD) were evaluated separately for the two subtypes. RESULTS: Of the 848 subjects (407 female, mean age, 53±12.2 y) included, 15.7% had SNs. Of the 303 SNs, 49.2% were SNd and 48.5% were SNa. Aging increased the prevalence of SNa while it was not related to the prevalence of SNd. Males had significantly more SNd than females (11.3% vs 4.7%, P<0.001), while the prevalence of SNa was not different between the two genders (10.2% vs 9.1%, P=0.666). A similar distribution of SNd and SNa among thoracic vertebral levels was appreciated, with T9 most frequently involved. Subjects with SNa had lower lumbar BMD than controls (P=0.006), while no significant difference in BMD was found between subjects with SNd and controls (P=0.166). CONCLUSIONS: The clinical characteristics of SN differ based on the developmental and acquired subtype, including the relationship with age, gender and BMD. The subtypes may be considered as distinct clinical entities as a result.

Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study.

Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease worldwide, yet detection has remained largely based on surrogate serum biomarkers, elastography or biopsy. In this study, we used a total of 2959 participants from the UK biobank cohort and established the association of dual-energy X-ray absorptiometry (DXA)-derived body composition parameters and leveraged machine learning models to predict NAFLD. Hepatic steatosis reference was based on MRI-PDFF which has been extensively validated previously. We found several significant associations with traditional measurements such as abdominal obesity, as defined by waist-to-hip ratio (OR = 2.50 (male), 3.35 (female)), android-gynoid ratio (OR = 3.35 (male), 6.39 (female)) and waist circumference (OR = 1.79 (male), 3.80 (female)) with hepatic steatosis. Similarly, A Body Shape Index (Quantile 4 OR = 1.89 (male), 5.81 (female)), and for fat mass index, both overweight (OR = 6.93 (male), 2.83 (female)) and obese (OR = 14.12 (male), 5.32 (female)) categories were likewise significantly associated with hepatic steatosis. DXA parameters were shown to be highly associated such as visceral adipose tissue mass (OR = 8.37 (male), 19.03 (female)), trunk fat mass (OR = 8.64 (male), 25.69 (female)) and android fat mass (OR = 7.93 (male), 21.77 (female)) with NAFLD. We trained machine learning classifiers with logistic regression and two histogram-based gradient boosting ensembles for the prediction of hepatic steatosis using traditional body composition indices and DXA parameters which achieved reasonable performance (AUC = 0.83-0.87). Based on SHapley Additive exPlanations (SHAP) analysis, DXA parameters that had the largest contribution to the classifiers were the features predicted with significant association with NAFLD. Overall, this study underscores the potential utility of DXA as a practical and potentially opportunistic method for the screening of hepatic steatosis.

Emerging role of radiogenomics in genetically triggered thoracic aortic aneurysm and dissection: a narrative review.

Background and Objective: Thoracic aortic aneurysm and dissection (TAAD) and its complications are life-threatening conditions. Hypertension and atherosclerosis had all along been recognized as the predominant risk factors for the development of TAAD. However, it was increasingly reported that genetic factors, such as single nucleotide polymorphisms (SNPs), are playing an important role in the disease development. The development of next-generation sequencing (NGS) and the rapid growth in radiomics provide a promising new platform to evaluate genetically triggered thoracic aortic aneurysm and dissection (GTAAD) from a new angle. This review is to present an overview of currently available knowledge regarding the use of radiomics and radiogenomics in GTAAD. Methods: We performed literature searches in PubMed, EMBASE and Cochrane database from 2012 to 2022 regarding the use of radiomics and radiogenomics in GTAAD. Key Content and Findings: There were only 13 studies on radiomics and 4 studies on radiogenomics integration retrieved from the search and it signifies there is still a significant knowledge gap in this field of translational medicine. An overview of the current knowledge of GTAAD, the workflow and role of radiomics, the radiogenomics integration for GTAAD including its potential role in the development of polygenic scores, as well as the implications, challenges, and limitations of radiogenomics research were discussed. Conclusions: In the contemporary era, radiogenomics has been emerging as a state-of-the art approach to establish statistical correlation with radiomics features with genomic information in diagnosis, risk modeling and prediction and treatment decision in TAAD.