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.

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.

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.

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.

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.

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.

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.

A cell wall reference profile for Miscanthus bioenergy crops highlights compositional and structural variations associated with development and organ origin.

Miscanthus spp. are promising lignocellulosic energy crops, but cell wall recalcitrance to deconstruction still hinders their widespread use as bioenergy and biomaterial feedstocks. Identification of cell wall characteristics desirable for biorefining applications is crucial for lignocellulosic biomass improvement. However, the task of scoring biomass quality is often complicated by the lack of a reference for a given feedstock. A multidimensional cell wall analysis was performed to generate a reference profile for leaf and stem biomass from several miscanthus genotypes harvested at three developmentally distinct time points. A comprehensive suite of 155 monoclonal antibodies was used to monitor changes in distribution, structure and extractability of noncellulosic cell wall matrix glycans. Glycan microarrays complemented with immunohistochemistry elucidated the nature of compositional variation, and in situ distribution of carbohydrate epitopes. Key observations demonstrated that there are crucial differences in miscanthus cell wall glycomes, which may impact biomass amenability to deconstruction. For the first time, variations in miscanthus cell wall glycan components were comprehensively characterized across different harvests, organs and genotypes, to generate a representative reference profile for miscanthus cell wall biomass. Ultimately, this portrait of the miscanthus cell wall will help to steer breeding and genetic engineering strategies for the development of superior energy crops.

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.

Prevalence of Vertebral Compression Fractures on Routine CT Scans According to L1 Trabecular Attenuation: Determining Relevant Thresholds for Opportunistic Osteoporosis Screening.

OBJECTIVE: Radiologists interpreting body CT scans may be the first to identify osteoporosis and associated vertebral fractures. This study correlates L1 trabecular attenuation measurements with prevalent vertebral body fractures in older adults undergoing routine CT. MATERIALS AND METHODS: Mean L1 trabecular attenuation was measured at thoracoabdominal CT in 1966 consecutive adults (983 men and 983 women) 65 years and older (mean age, 74.1 ± 6.6 [SD] years). Sagittal reconstructions and lateral scouts were analyzed for moderate or severe thoracolumbar vertebral compression fractures according to the Genant semiquantitative assessment method. The diagnostic performance of L1 attenuation for the evaluation of prevalent vertebral fractures was assessed, including ROC curve analysis. RESULTS: A total of 162 (8.2%) individuals (mean age, 78.3 years; 66 men, 96 women) had at least one moderate or severe vertebral fracture. The mean L1 attenuation was 70.2 HU among patients with a prevalent fracture, whereas it was 132.3 HU among patients without fracture (p < 0.001). The prevalence of moderate or severe vertebral compression fractures was 32.5% when L1 attenuation was ≤ 90 HU. Prevalence increased to 49.2% with L1 attenuation of ≤ 50 HU. ROC curve analysis determined an optimal threshold of 90 HU (sensitivity = 86.9%, specificity = 83.9%), with a corresponding AUC of 0.895. The odds ratio of having a moderate or severe vertebral compression fracture was 31.9 for L1 attenuation ≤ 90 HU (95% CI, 20.2-50.5; p < 0.001). CONCLUSION: Patients with moderate or severe vertebral compression fractures have significantly lower L1 attenuation values than patients who do not. L1 attenuation ≤ 90 HU may represent an optimal threshold for determining risk for osteoporotic vertebral fractures.

Direct Image-Based Enumeration of Clostridium phytofermentans Cells on Insoluble Plant Biomass Growth Substrates.

A dual-fluorescent-dye protocol to visualize and quantify Clostridium phytofermentans ISDg (ATCC 700394) cells growing on insoluble cellulosic substrates was developed by combining calcofluor white staining of the growth substrate with cell staining using the nucleic acid dye Syto 9. Cell growth, cell substrate attachment, and fermentation product formation were investigated in cultures containing either Whatman no. 1 filter paper, wild-type Sorghum bicolor, or a reduced-lignin S. bicolor double mutant (bmr-6 bmr-12 double mutant) as the growth substrate. After 3 days of growth, cell numbers in cultures grown on filter paper as the substrate were 6.0- and 2.2-fold higher than cell numbers in cultures with wild-type sorghum and double mutant sorghum, respectively. However, cells produced more ethanol per cell when grown with either sorghum substrate than with filter paper as the substrate. Ethanol yields of cultures were significantly higher with double mutant sorghum than with wild-type sorghum or filter paper as the substrate. Moreover, ethanol production correlated with cell attachment in sorghum cultures: 90% of cells were directly attached to the double mutant sorghum substrate, while only 76% of cells were attached to wild-type sorghum substrate. With filter paper as the growth substrate, ethanol production was correlated with cell number; however, with either wild-type or mutant sorghum, ethanol production did not correlate with cell number, suggesting that only a portion of the microbial cell population was active during growth on sorghum. The dual-staining procedure described here may be used to visualize and enumerate cells directly on insoluble cellulosic substrates, enabling in-depth studies of interactions of microbes with plant biomass.

Genotype, development and tissue-derived variation of cell-wall properties in the lignocellulosic energy crop Miscanthus.

BACKGROUND AND AIMS: Species and hybrids of the genus Miscanthus contain attributes that make them front-runners among current selections of dedicated bioenergy crops. A key trait for plant biomass conversion to biofuels and biomaterials is cell-wall quality; however, knowledge of cell-wall composition and biology in Miscanthus species is limited. This study presents data on cell-wall compositional changes as a function of development and tissue type across selected genotypes, and considers implications for the development of miscanthus as a sustainable and renewable bioenergy feedstock. METHODS: Cell-wall biomass was analysed for 25 genotypes, considering different developmental stages and stem vs. leaf compositional variability, by Fourier transform mid-infrared spectroscopy and lignin determination. In addition, a Clostridium phytofermentans bioassay was used to assess cell-wall digestibility and conversion to ethanol. KEY RESULTS: Important cell-wall compositional differences between miscanthus stem and leaf samples were found to be predominantly associated with structural carbohydrates. Lignin content increased as plants matured and was higher in stem tissues. Although stem lignin concentration correlated inversely with ethanol production, no such correlation was observed for leaves. Leaf tissue contributed significantly to total above-ground biomass at all stages, although the extent of this contribution was genotype-dependent. CONCLUSIONS: It is hypothesized that divergent carbohydrate compositions and modifications in stem and leaf tissues are major determinants for observed differences in cell-wall quality. The findings indicate that improvement of lignocellulosic feedstocks should encompass tissue-dependent variation as it affects amenability to biological conversion. For gene-trait associations relating to cell-wall quality, the data support the separate examination of leaf and stem composition, as tissue-specific traits may be masked by considering only total above-ground biomass samples, and sample variability could be mostly due to varying tissue contributions to total biomass.

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.

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR.

Functional characterization of cinnamyl alcohol dehydrogenase and caffeic acid O-methyltransferase in Brachypodium distachyon.

BACKGROUND: Lignin is a significant barrier in the conversion of plant biomass to bioethanol. Cinnamyl alcohol dehydrogenase (CAD) and caffeic acid O-methyltransferase (COMT) catalyze key steps in the pathway of lignin monomer biosynthesis. Brown midrib mutants in Zea mays and Sorghum bicolor with impaired CAD or COMT activity have attracted considerable agronomic interest for their altered lignin composition and improved digestibility. Here, we identified and functionally characterized candidate genes encoding CAD and COMT enzymes in the grass model species Brachypodium distachyon with the aim of improving crops for efficient biofuel production. RESULTS: We developed transgenic plants overexpressing artificial microRNA designed to silence BdCAD1 or BdCOMT4. Both transgenes caused altered flowering time and increased stem count and weight. Downregulation of BdCAD1 caused a leaf brown midrib phenotype, the first time this phenotype has been observed in a C3 plant. While acetyl bromide soluble lignin measurements were equivalent in BdCAD1 downregulated and control plants, histochemical staining and thioacidolysis indicated a decrease in lignin syringyl units and reduced syringyl/guaiacyl ratio in the transgenic plants. BdCOMT4 downregulated plants exhibited a reduction in total lignin content and decreased Maule staining of syringyl units in stem. Ethanol yield by microbial fermentation was enhanced in amiR-cad1-8 plants. CONCLUSION: These results have elucidated two key genes in the lignin biosynthetic pathway in B. distachyon that, when perturbed, may result in greater stem biomass yield and bioconversion efficiency.

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.

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.

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.

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.

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.