Contemporary Use of Prophylactic Inferior Vena Cava Filters in Patients With Severe Traumatic Injuries and High Thromboembolic Event Risk.

PURPOSE: The aim of this study was to evaluate the relationship between prophylactic inferior vena cava filter (IVCF) implantation and in-hospital deep vein thrombosis (DVT), pulmonary embolism (PE), and mortality among adults with intracranial, pelvic or lower extremity, and spinal cord injuries. METHODS: Patients 18 years and older with severe intracranial, pelvic or lower extremity, or spinal cord injuries captured by the Trauma Quality Improvement Program (2010-2019) were identified. IVCFs implanted ≤72 hours after hospital presentation and before performance of lower extremity ultrasonography were defined as prophylactic. Patients were stratified by pharmacologic venous thromboembolism (VTE) prophylaxis status. Logistic regression models estimated prophylactic inferior vena cava (IVC) filtration's effect on selected outcomes and identified attributes associated with prophylactic IVCF implantation. RESULTS: Of 544,739 included patients, 1.3% (n = 7,247) underwent prophylactic IVCF implantation. Among patients who received pharmacologic VTE prophylaxis, prophylactic IVC filtration compared with expectant management was positively associated with DVT (odds ratio [OR], 4.30; P < .001) and PE (OR, 4.30; P < .001) but not associated with mortality (OR, 0.92; P = .43). Among patients who received no pharmacologic prophylaxis, prophylactic IVC filtration was positively associated with DVT (OR, 4.63; P < .001) and PE (OR, 5.02; P < .001) but negatively associated with mortality (OR, 0.43; P < .001). CONCLUSIONS: Prophylactic IVC filtration was associated with increased likelihood of VTE among all adults with severe intracranial, pelvic or lower extremity, and spinal cord injuries. In patients who received no pharmacologic VTE prophylaxis, prophylactic IVC filtration was associated with decreased likelihood of in-hospital mortality.

Prophylactic IVC filter placement in patients with severe intracranial, spinal cord, and orthopedic injuries at high thromboembolic event risk: A utilization and outcomes analysis of the National Trauma Data Bank.

PURPOSE: To determine relationships between prophylactic inferior vena cava filter (IVCF) insertion and pulmonary embolism (PE), deep venous thrombosis (DVT), and in-hospital mortality outcomes in patients with severe traumatic pelvic/lower extremity, intracranial, and spinal cord injuries. METHODS: Adult patients with severe traumatic pelvic/lower extremity, intracranial, and spinal cord injuries admitted to level I-IV trauma centers were selected from the National Trauma Data Bank (NTDB). IVCFs that were inserted both ≤48 h after admission and before a lower extremity venous ultrasound were defined as prophylactic. Associations between prophylactic IVCF insertion and PE, DVT, and overall mortality outcomes during admission were estimated using logistic regression models after propensity score matching. Additionally, factors predictive of prophylactic IVCF insertion were estimated using multivariate logistic regression. RESULTS: Of 462,838 patients, 11,938 (2.6%) underwent prophylactic IVCF insertion. Prophylactic IVCF utilization decreased over time (6.3% in 2008 to 1.8% in 2015). Factors associated with prophylactic IVCF placement were injury pattern, trauma center level/region, Injury Severity Score, and race. Prophylactic IVCF placement was positively associated with PE (Odds Ratio (OR): 5.25, p < 0.01) and DVT (OR: 5.55, p < 0.01), but negatively associated with in-hospital mortality compared to the propensity score-matched control group (OR: 0.46, p < 0.01). CONCLUSION: Prophylactic IVCF insertion in adult patients with severe pelvic/lower extremity fractures, intracranial injuries, and spinal cord injuries was negatively associated with in-hospital mortality, but positively associated with VTE. Further research evaluating the use of prophylactic IVCF placement in trauma patients with these specific severe injury types may be warranted.

COVID-19 pneumonia chest radiographic severity score: variability assessment among experienced and in-training radiologists and creation of a multireader composite score database for artificial intelligence algorithm development.

OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

Evaluating the Performance of a Convolutional Neural Network Algorithm for Measuring Thoracic Aortic Diameters in a Heterogeneous Population.

The purpose of this work was to assess the performance of a convolutional neural network (CNN) for automatic thoracic aortic measurements in a heterogeneous population. From June 2018 to May 2019, this study retrospectively analyzed 250 chest CT scans with or without contrast enhancement and electrocardiographic gating from a heterogeneous population with or without aortic pathologic findings. Aortic diameters at nine locations and maximum aortic diameter were measured manually and with an algorithm (Artificial Intelligence Rad Companion Chest CT prototype, Siemens Healthineers) by using a CNN. A total of 233 examinations performed with 15 scanners from three vendors in 233 patients (median age, 65 years [IQR, 54-72 years]; 144 men) were analyzed: 68 (29%) without pathologic findings, 72 (31%) with aneurysm, 51 (22%) with dissection, and 42 (18%) with repair. No evidence of a difference was observed in maximum aortic diameter between manual and automatic measurements (P = .48). Overall measurements displayed a bias of -1.5 mm and a coefficient of repeatability of 8.0 mm at Bland-Altman analyses. Contrast enhancement, location, pathologic finding, and positioning inaccuracy negatively influenced reproducibility (P < .003). Sites with dissection or repair showed lower agreement than did sites without. The CNN performed well in measuring thoracic aortic diameters in a heterogeneous multivendor CT dataset. Keywords: CT, Vascular, Aorta © RSNA, 2022.

Cardiac Magnetic Resonance Tissue Characterization in Ischemic Cardiomyopathy.

Ischemic cardiomyopathy (ICM) is one of the most common causes of congestive heart failure. In patients with ICM, tissue characterization with cardiac magnetic resonance imaging (CMR) allows for evaluation of myocardial abnormalities in acute and chronic settings. Myocardial edema, microvascular obstruction (MVO), intracardiac thrombus, intramyocardial hemorrhage, and late gadolinium enhancement of the myocardium are easily depicted using standard CMR sequences. In the acute setting, tissue characterization is mainly focused on assessment of ventricular thrombus and MVO, which are associated with poor prognosis. Conversely, in chronic ICM, it is important to depict late gadolinium enhancement and myocardial ischemia using stress perfusion sequences. Overall, with CMR's ability to accurately characterize myocardial tissue in acute and chronic ICM, it represents a valuable diagnostic and prognostic imaging method for treatment planning. In particular, tissue characterization abnormalities in the acute setting can provide information regarding the patients that may develop major adverse cardiac event and show the presence of ventricular thrombus; in the chronic setting, evaluation of viable myocardium can be fundamental for planning myocardial revascularization. In this review, the main findings on tissue characterization are illustrated in acute and chronic settings using qualitative and quantitative tissue characterization.

Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms.

OBJECTIVE: Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms. MATERIALS AND METHODS: International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT. After patients with insufficient clinical data for diagnosis were excluded, the final cohort consisted of 7785 adults (mean age, 57.0 years; 4361 women and 3424 men). Previously validated and fully automated CT-based algorithms for quantifying muscle, visceral and subcutaneous fat, liver fat, and abdominal aortic calcification were applied to this final cohort. RESULTS: A total of 738 subjects (9.5% of all subjects; mean age, 56.7 years; 372 women and 366 men) met the clinical criteria for metabolic syndrome. Subsequent major cardiovascular events occurred more frequently in the cohort with metabolic syndrome (p < 0.001). Significant differences were observed between the two groups for all CT-based biomarkers (p < 0.001). Univariate L1-level total abdominal fat (area under the ROC curve [AUROC] = 0.909; odds ratio [OR] = 27.2), L3-level skeletal muscle index (AUROC = 0.776; OR = 5.8), and volumetric liver attenuation (AUROC = 0.738; OR = 5.1) performed well when compared with abdominal aortic calcification scoring (AUROC = 0.578; OR = 1.6). An L1-level total abdominal fat threshold of 460.6 cm2 was 80.1% sensitive and 85.4% specific for metabolic syndrome. For women, the AUROC was 0.930 when fat and muscle measures were combined. CONCLUSION: Fully automated quantitative tissue measures of fat, muscle, and liver derived from abdominal CT scans can help identify individuals who are at risk for metabolic syndrome. These visceral measures can be opportunistically applied to CT scans obtained for other clinical indications, and they may ultimately provide a more direct and useful definition of metabolic syndrome.

Artificial intelligence in cardiac radiology.

Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.

Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study.

Background: Body CT scans are frequently performed for a wide variety of clinical indications, but potentially valuable biometric information typically goes unused. We investigated the prognostic ability of automated CT-based body composition biomarkers derived from previously-developed deep-learning and feature-based algorithms for predicting major cardiovascular events and overall survival in an adult screening cohort, compared with clinical parameters. Methods: Mature and fully-automated CT-based algorithms with pre-defined metrics for quantifying aortic calcification, muscle density, visceral/subcutaneous fat, liver fat, and bone mineral density (BMD) were applied to a generally-healthy asymptomatic outpatient cohort of 9223 adults (mean age, 57.1 years; 5152 women) undergoing abdominal CT for routine colorectal cancer screening. Longitudinal clinical follow-up (median, 8.8 years; IQR, 5.1-11.6 years) documented subsequent major cardiovascular events or death in 19.7% (n=1831). Predictive ability of CT-based biomarkers was compared against the Framingham Risk Score (FRS) and body mass index (BMI). Findings: Significant differences were observed for all five automated CT-based body composition measures according to adverse events (p<0.001). Univariate 5-year AUROC (with 95% CI) for automated CT-based aortic calcification, muscle density, visceral/subcutaneous fat ratio, liver density, and vertebral density for predicting death were 0.743(0.705-0.780)/0.721(0.683-0.759)/0.661(0.625-0.697)/0.619 (0.582-0.656)/0.646(0.603-0.688), respectively, compared with 0.499(0.454-0.544) for BMI and 0.688(0.650-0.727) for FRS (p<0.05 for aortic calcification vs. FRS and BMI); all trends were similar for 2-year and 10-year ROC analyses. Univariate hazard ratios (with 95% CIs) for highest-risk quartile versus others for these same CT measures were 4.53(3.82-5.37) /3.58(3.02-4.23)/2.28(1.92-2.71)/1.82(1.52-2.17)/2.73(2.31-3.23), compared with 1.36(1.13-1.64) and 2.82(2.36-3.37) for BMI and FRS, respectively. Similar significant trends were observed for cardiovascular events. Multivariate combinations of CT biomarkers further improved prediction over clinical parameters (p<0.05 for AUROCs). For example, by combining aortic calcification, muscle density, and liver density, the 2-year AUROC for predicting overall survival was 0.811 (0.761-0.860). Interpretation: Fully-automated quantitative tissue biomarkers derived from CT scans can outperform established clinical parameters for pre-symptomatic risk stratification for future serious adverse events, and add opportunistic value to CT scans performed for other indications.

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults.

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.

Gradual polyploid genome evolution revealed by pan-genomic analysis of Brachypodium hybridum and its diploid progenitors.

Our understanding of polyploid genome evolution is constrained because we cannot know the exact founders of a particular polyploid. To differentiate between founder effects and post polyploidization evolution, we use a pan-genomic approach to study the allotetraploid Brachypodium hybridum and its diploid progenitors. Comparative analysis suggests that most B. hybridum whole gene presence/absence variation is part of the standing variation in its diploid progenitors. Analysis of nuclear single nucleotide variants, plastomes and k-mers associated with retrotransposons reveals two independent origins for B. hybridum, ~1.4 and ~0.14 million years ago. Examination of gene expression in the younger B. hybridum lineage reveals no bias in overall subgenome expression. Our results are consistent with a gradual accumulation of genomic changes after polyploidization and a lack of subgenome expression dominance. Significantly, if we did not use a pan-genomic approach, we would grossly overestimate the number of genomic changes attributable to post polyploidization evolution.

Artificial intelligence from A to Z: From neural network to legal framework.

Artificial intelligence (AI) will continue to cause substantial changes within the field of radiology, and it will become increasingly important for clinicians to be familiar with several concepts behind AI algorithms in order to effectively guide their clinical implementation. This review aims to give medical professionals the basic information needed to understand AI development and research. The general concepts behind several AI algorithms, including their data requirements, training, and evaluation methods are explained. The potential legal implications of using AI algorithms in clinical practice are also discussed.

A Scalable Natural Language Processing for Inferring BT-RADS Categorization from Unstructured Brain Magnetic Resonance Reports.

The aim of this study is to develop an automated classification method for Brain Tumor Reporting and Data System (BT-RADS) categories from unstructured and structured brain magnetic resonance imaging (MR) reports. This retrospective study included 1410 BT-RADS structured reports dated from January 2014 to December 2017 and a test set of 109 unstructured brain MR reports dated from January 2010 to December 2014. Text vector representations and semantic word embeddings were generated from individual report sections (i.e., "History," "Findings," etc.) using Tf-idf statistics and a fine-tuned word2vec model, respectively. Section-wise ensemble models were trained using gradient boosting (XGBoost), elastic net regularization, and random forests, and classification accuracy was evaluated on an independent test set of unstructured brain MR reports and a validation set of BT-RADS structured reports. Section-wise ensemble models using XGBoost and word2vec semantic word embeddings were more accurate than those using Tf-idf statistics when classifying unstructured reports, with an f1 score of 0.72. In contrast, models using traditional Tf-idf statistics outperformed the word2vec semantic approach for categorization from structured reports, with an f1 score of 0.98. Proposed natural language processing pipeline is capable of inferring BT-RADS report scores from unstructured reports after training on structured report data. Our study provides a detailed experimentation process and may provide guidance for the development of RADS-focused information extraction (IE) applications from structured and unstructured radiology reports.

Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20 000 Adults.

Background Abdominal and thoracic CT provide a valuable opportunity for osteoporosis screening regardless of the clinical indication for imaging. Purpose To establish reference normative ranges for first lumbar vertebra (L1) trabecular attenuation values across all adult ages to measure bone mineral density (BMD) at routine CT. Materials and Methods Reference data were constructed from 20 374 abdominal and/or thoracic CT examinations performed at 120 kV. Data were derived from adults (mean age, 60 years ± 12 [standard deviation]; 56.1% [11 428 of 20 374] women). CT examinations were performed with (n = 4263) or without (n = 16 111) intravenous contrast agent administration for a variety of unrelated clinical indications between 2000 and 2018. L1 Hounsfield unit measurement was obtained either with a customized automated tool (n = 11 270) or manually by individual readers (n = 9104). The effects of patient age, sex, contrast agent, and manual region-of-interest versus fully automated L1 Hounsfield unit measurement were assessed using multivariable logistic regression analysis. Results Mean L1 attenuation decreased linearly with age at a rate of 2.5 HU per year, averaging 226 HU ± 44 for patients younger than 30 years and 89 HU ± 38 for patients 90 years or older. Women had a higher mean L1 attenuation compared with men (P < .008) until menopause, after which both groups had similar values. Administration of intravenous contrast agent resulted in negligible differences in mean L1 attenuation values except in patients younger than 40 years. The fully automated method resulted in measurements that were average 21 HU higher compared with manual measurement (P < .004); at intrapatient subanalysis, this difference was related to the level of transverse measurement used (midvertebra vs off-midline level). Conclusion Normative ranges of L1 vertebra trabecular attenuation were established across all adult ages, and these can serve as a quick reference at routine CT to identify adults with low bone mineral density who are at risk for osteoporosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Smith in this issue.

Population-based opportunistic osteoporosis screening: Validation of a fully automated CT tool for assessing longitudinal BMD changes.

METHODS:: The automated BMD tool was retrospectively applied to non-contrast abdominal CT scans in 1603 consecutive asymptomatic adults (mean age, 55.9 years; 770 M/833 F) undergoing longitudinal screening (mean interval, 5.7 years; range, 1.0-12.3 years). The spinal column was automatically segmented, with standardized L1 and L2 anterior trabecular ROI placement. Automated and manual L1 HU values were compared, as were automated supine-prone measures. L1-L2 CT attenuation values were converted to BMD values through a linear regression model. BMD values and changes were assessed according to age and gender. RESULTS:: Success rate of the automated BMD tool was 99.8 % (four failed cases). Both automated supine vs prone and manual vs automated L1 attenuation measurements showed good agreement. Overall mean annual rate of bone loss was greater in females than males (-2.0% vs -1.0%), but the age-specific rate declined faster in females from age 50 (-2.1%) to age 65 (-0.3%) compared with males (-0.9% to -0.5%). Mean BMD was higher in females than males at age 50 (143.6 vs 135.1 mg cm-3), but post-menopausal bone loss in females reversed this relationship beyond age 60. By age 70, mean BMD in females and males was 100.8 and 107.7 mg cm-3 , respectively. CONCLUSION:: This robust, fully automated CT BMD tool allows for both individualized and population-based assessment. Mean BMD was lower in men than women aged 50-60, but accelerated post-menopausal bone loss in women resulted in lower values beyond age 60. ADVANCES IN KNOWLEDGE:: This fully automated tool can be applied to routine abdominal CT scans for prospective or retrospective opportunistic BMD assessment, including change over time. Mean BMD was lower in men compared with women aged 50-60 years, but accelerated bone loss in women during this early post-menopausal period resulted in lower BMD values for women beyond age 60.

SECONDARY WALL ASSOCIATED MYB1 is a positive regulator of secondary cell wall thickening in Brachypodium distachyon and is not found in the Brassicaceae.

Grass biomass is comprised chiefly of secondary walls that surround fiber and xylem cells. A regulatory network of interacting transcription factors in part regulates cell wall thickening. We identified Brachypodium distachyon SECONDARY WALL ASSOCIATED MYB1 (SWAM1) as a potential regulator of secondary cell wall biosynthesis based on gene expression, phylogeny, and transgenic plant phenotypes. SWAM1 interacts with cellulose and lignin gene promoters with preferential binding to AC-rich sequence motifs commonly found in the promoters of cell wall-related genes. SWAM1 overexpression (SWAM-OE) lines had greater above-ground biomass with only a slight change in flowering time while SWAM1 dominant repressor (SWAM1-DR) plants were severely dwarfed with a striking reduction in lignin of sclerenchyma fibers and stem epidermal cell length. Cellulose, hemicellulose, and lignin genes were significantly down-regulated in SWAM1-DR plants and up-regulated in SWAM1-OE plants. There was no reduction in bioconversion yield in SWAM1-OE lines; however, it was significantly increased for SWAM1-DR samples. Phylogenetic and syntenic analyses strongly suggest that the SWAM1 clade was present in the last common ancestor between eudicots and grasses, but is not in the Brassicaceae. Collectively, these data suggest that SWAM1 is a transcriptional activator of secondary cell wall thickening and biomass accumulation in B. distachyon.

Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort.

OBJECTIVE: To investigate a fully automated CT-based adiposity tool, applying it to a longitudinal adult screening cohort. METHODS: A validated automated adipose tissue segmentation algorithm was applied to non-contrast abdominal CT scans in 8852 consecutive asymptomatic adults (mean age, 57.1 years; 3926 M/4926 F) undergoing colonography screening. The tool was also applied to follow-up CT scans in a subset of 1584 individuals undergoing longitudinal surveillance (mean interval, 5.6 years). Visceral and subcutaneous adipose tissue (VAT and SAT) volumes were segmented at levels T12-L5. Primary adipose results are reported herein for the L1 level as mean cross-sectional area. CT-based adipose measurements at initial CT and change over time were analyzed. RESULTS: Mean VAT values were significantly higher in males (205.8 ± 107.5 vs 108.1 ± 82.4 cm2; p < 0.001), whereas mean SAT values were significantly higher in females (171.3 ± 111.3 vs 124.3 ± 79.7 cm2; p < 0.001). The VAT/SAT ratio at L1 was three times higher in males (1.8 ± 0.7 vs 0.6 ± 0.4; p < 0.001). At longitudinal follow-up CT, mean VAT/SAT ratio change was positive in males, but negative in females. Among the 502 individuals where the VAT/SAT ratio increased at follow-up CT, 333 (66.3%) were males. Half of patients (49.6%; 786/1585) showed an interval increase in both VAT and SAT at follow-up CT. CONCLUSION: This robust, fully automated CT adiposity tool allows for both individualized and population-based assessment of visceral and subcutaneous abdominal fat. Such data could be automatically derived at abdominal CT regardless of the study indication, potentially allowing for opportunistic cardiovascular risk stratification. Advances in knowledge: The CT-based adiposity tool described herein allows for fully automated measurement of visceral and subcutaneous abdominal fat, which can be used for assessing cardiovascular risk, metabolic syndrome, and for change over time.

Future Osteoporotic Fracture Risk Related to Lumbar Vertebral Trabecular Attenuation Measured at Routine Body CT.

We sought to determine if vertebral trabecular attenuation values measured on routine body computed tomography (CT) scans obtained for a variety of unrelated indications can predict future osteoporotic fractures at multiple skeletal sites. For this Health Insurance Portability and Accountability Act (HIPAA)-compliant and Institutional Review Board (IRB)-approved retrospective cohort study, trabecular attenuation of the first lumbar vertebra was measured in 1966 consecutive older adults who underwent chest and/or abdominal CT at a single institution over the course of 1 year. New pathologic fragility fractures that occurred after a patient's CT study date were identified through an electronic health record database query using International Classification of Diseases (ICD)-9 codes for vertebral, hip, and extremity fractures. Univariate and multivariate Cox proportional hazards regression were performed to determine the effect of L1 trabecular attenuation on fracture-free survival. Age at CT, sex, and presence of a prior fragility fracture were included as confounders in multivariate survival analysis. Model discriminative capability was assessed through calculation of an optimism-corrected concordance index. A total of 507 patients (mean age 73.4 ± 6.3 years; 277 women, 230 men) were included in the final analysis. The median post-CT follow-up interval was 5.8 years (interquartile range 2.1-11.0 years). Univariate analysis showed that L1 attenuation values ≤90 Hounsfield units (HU) are significantly associated with decreased fracture-free survival (p < 0.001 by log-rank test). After adjusting for age, sex, prior fracture, glucocorticoid use, bisphosphonate use, chronic kidney disease, tobacco use, ethanol abuse, cancer history, and rheumatoid arthritis history, multivariate analysis demonstrated a persistent modest effect of L1 attenuation on fracture-free survival (hazard ratio [HR] = 0.63 per 10-unit increase; 95% confidence interval [CI] 0.47-0.85). The model concordance index was 0.700. Ten-year probabilities for major osteoporosis-related fractures straddled the treatment threshold for most subcohorts over the observed L1 HU range. In conclusion, for patients undergoing body CT scanning for any indication, L1 vertebral trabecular attenuation is a simple measure that, when ≤90 HU, identifies patients with a significant decrease in fracture-free survival. © 2018 American Society for Bone and Mineral Research.

Secondary Wall Regulating NACs Differentially Bind at the Promoter at a CELLULOSE SYNTHASE A4 Cis-eQTL.

Arabidopsis thaliana CELLULOSE SYNTHASE A4/7/8 (CESA4/7/8) are three non-redundant subunits of the secondary cell wall cellulose synthase complex. Transcript abundance of these genes can vary among genotypes and expression quantitative trait loci (eQTL) were identified in a recombinant population of the accessions Bay-0 and Shahdara. Genetic mapping and analysis of the transcript levels of CESAs between two distinct near isogenic lines (NILs) confirmed a change in CESA4 expression that segregates within that interval. We sequenced the promoters and identified 16 polymorphisms differentiating CESA4Sha and CESA4Bay . In order to determine which of these SNPs could be responsible for this eQTL, we screened for transcription factor protein affinity with promoter fragments of CESA4Bay, CESA4Sha , and the reference genome CESA4Col . The wall thickening activator proteins NAC SECONDARY WALL THICKENING PROMOTING FACTOR2 (NST2) and NST3 exhibited a decrease in binding with the CESA4Sha promoter with a tracheary element-regulating cis-element (TERE) polymorphism. While NILs harboring the TERE polymorphisms exhibited significantly different CESA4 expression, cellulose crystallinity and cell wall thickness were indistinguishable. These results suggest that the TERE polymorphism resulted in differential transcription factor binding and CESA4 expression; yet A. thaliana is able to tolerate this transcriptional variability without compromising the structural elements of the plant, providing insight into the elasticity of gene regulation as it pertains to cell wall biosynthesis and regulation. We also explored available DNA affinity purification sequencing data to resolve a core binding site, C(G/T)TNNNNNNNA(A/C)G, for secondary wall NACs referred to as the VNS element.

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer.

OBJECTIVES: To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases. METHODS: Four filtration-histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression. RESULTS: Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001). CONCLUSIONS: On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development. KEY POINTS: • No correlation between liver texture features and metastasis-free survival was observed. • Liver texture features incorrectly classified patients into risk groups for liver metastases. • Standardised texture analysis workflows need to be developed to improve research reproducibility.

Predicting Future Hip Fractures on Routine Abdominal CT Using Opportunistic Osteoporosis Screening Measures: A Matched Case-Control Study.

OBJECTIVE: Hip fracture is a major consequence of low bone mineral density, which is treatable but underdiagnosed. The purpose of this case-control study is to determine whether lumbar vertebral trabecular attenuation, vertebral compression fractures, and femoral neck T scores readily derived from abdominopelvic CT scans obtained for various indications are associated with future hip fragility fracture. MATERIALS AND METHODS: A cohort of 204 patients with hip fracture (130 women and 74 men; mean age, 74.3 years) who had undergone abdominopelvic CT before fracture occurred (mean interval, 24.8 months) was compared with an age- and sex-matched control cohort without hip fracture. L1 trabecular attenuation, vertebral compression fractures of grades 2 and 3, and femoral neck T scores derived from asynchronous quantitative CT were recorded. The presence of one or more clinical risk factor for fracture was also recorded. Multivariate logistic regression models were used to determine the association of each measurement with the occurrence of hip fracture. RESULTS: The mean L1 trabecular attenuation value, the presence of one or more vertebral compression fracture, and CT-derived femoral neck T scores were all significantly different in patients with hip fracture versus control subjects (p < 0.01). Logistic regression models showed a significant association of all measurements with hip fracture outcome after adjustments were made for age, sex, and the presence of one or more clinical risk factor. L1 trabecular attenuation and CT-derived femoral neck T scores showed moderate accuracy in differentiating case and control patients (AUC, 0.70 and 0.78, respectively). CONCLUSION: L1 trabecular attenuation, CT-derived femoral neck T scores, and the presence of at least one vertebral compression fracture on CT are all associated with future hip fragility fracture in adults undergoing routine abdominopelvic CT for a variety of conditions.