Correlation of HbA1c levels with CT-based body composition biomarkers in diabetes mellitus and metabolic syndrome.

Diabetes mellitus and metabolic syndrome are closely linked with visceral body composition, but clinical assessment is limited to external measurements and laboratory values including hemoglobin A1c (HbA1c). Modern deep learning and AI algorithms allow automated extraction of biomarkers for organ size, density, and body composition from routine computed tomography (CT) exams. Comparing visceral CT biomarkers across groups with differing glycemic control revealed significant, progressive CT biomarker changes with increasing HbA1c. For example, in the unenhanced female cohort, mean changes between normal and poorly-controlled diabetes showed: 53% increase in visceral adipose tissue area, 22% increase in kidney volume, 24% increase in liver volume, 6% decrease in liver density (hepatic steatosis), 16% increase in skeletal muscle area, and 21% decrease in skeletal muscle density (myosteatosis) (all p < 0.001). The multisystem changes of metabolic syndrome can be objectively and retrospectively measured using automated CT biomarkers, with implications for diabetes, metabolic syndrome, and GLP-1 agonists.

Intrapatient Changes in CT-Based Body Composition After Initiation of Semaglutide (Glucagon-Like Peptide-1 Agonist) Therapy.

Background: The long-acting glucagon-like peptide-1 receptor agonist semaglutide is used to treat type 2 diabetes or obesity in adults. Clinical trials have observed associations of semaglutide with weight loss, improved diabetic control, and cardiovascular risk reduction. Objective: To evaluate intrapatient changes in body composition after initiation of semaglutide therapy by applying an automated suite of CT-based artificial intelligence (AI) body composition tools. Methods: This retrospective study included adult patients with semaglutide treatment who underwent abdominopelvic CT both within 5 years before and within 5 years after semaglutide initiation, between January 2016 and November 2023. An automated suite of previously validated CT-based AI body composition tools was applied to pre-semaglutide and post-semaglutide scans to quantify visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) area, skeletal muscle area and attenuation, intermuscular adipose tissue (IMAT) area, liver volume and attenuation, and trabecular bone mineral density (BMD). Patients with ≥5-kg weight loss and ≥5-kg weight gain between scans were compared. Results: The study included 241 patients (mean age, 60.4±12.4 years; 151 women, 90 men). In the weight-loss group (n=67), the post-semaglutide scan, versus pre-semaglutide scan, showed decrease in VAT area (341.1 vs 309.4 cm2, p<.001), SAT area (371.4 vs 410.7 cm2, p<.001), muscle area (179.2 vs 193.0, p<.001), and liver volume (2379.0 vs 2578 HU, p=.009), and increase in liver attenuation (74.5 vs 67.6 HU, p=.03). In the weight-gain group (n=48), the post-semaglutide scan, versus pre-semaglutide scan, showed increase in VAT area (334.0 vs 312.8, p=.002), SAT area (485.8 vs 488.8 cm2, p=.01), and IMAT area (48.4 vs 37.6, p=.009), and decrease in muscle attenuation (5.9 vs 13.1, p<.001). Other comparisons were not significant (p>.05). Conclusion: Patients using semaglutide who lost versus gained weight demonstrated distinct patterns of changes in CT-based body composition measures. Those with weight loss exhibited overall favorable shifts in measures related to cardiometabolic risk. Muscle attenuation decrease in those with weight gain is consistent with decreased muscle quality. Clinical Impact: Automated CT-based AI tools provide biomarkers of body composition changes in patients using semaglutide beyond that which is evident by standard clinical measures.

Regulation of Zfp36 by ISGF3 and MK2 restricts the expression of inflammatory cytokines during necroptosis stimulation.

Necrosome activation following TLR- or cytokine receptor-signaling results in cell death by necroptosis which is characterized by the rupture of cell membranes and the consequent release of intracellular contents to the extracellular milieu. While necroptosis exacerbates various inflammatory diseases, the mechanisms through which the inflammatory responses are regulated are not clear. We show that the necrosome activation of macrophages results in an upregulation of various pathways, including the mitogen-activated protein kinase (MAPK) cascade, which results in an elevation of the inflammatory response and consequent expression of several cytokines and chemokines. Programming for this upregulation of inflammatory response occurs during the early phase of necrosome activation and proceeds independently of cell death but depends on the activation of the receptor-interacting protein kinase-1 (RipK1). Interestingly, necrosome activation also results in an upregulation of IFNß, which in turn exerts an inhibitory effect on the maintenance of inflammatory response through the repression of MAPK-signaling and an upregulation of Zfp36. Activation of the interferon-induced gene factor-3 (ISGF3) results in the expression of ZFP36 (TTP), which induces the post-transcriptional degradation of mRNAs of various inflammatory cytokines and chemokines through the recognition of AU-rich elements in their 3'UTR. Furthermore, ZFP-36 inhibits IFNß-, but not TNFα- induced necroptosis. Overall, these results reveal the molecular mechanism through which IFNß, a pro-inflammatory cytokine, induces the expression of ZFP-36, which in turn inhibits necroptosis and halts the maintenance of the inflammatory response.

Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue Elasticity in CT Images.

The elasticity of soft tissues has been widely considered a characteristic property for differentiation of healthy and lesions and, therefore, motivated the development of several elasticity imaging modalities, for example, ultrasound elastography, magnetic resonance elastography, and optical coherence elastography to directly measure the tissue elasticity. This paper proposes an alternative approach of modeling the elasticity for prior knowledge-based extraction of tissue elastic characteristic features for machine learning (ML) lesion classification using computed tomography (CT) imaging modality. The model describes a dynamic non-rigid (or elastic) soft tissue deformation in differential manifold to mimic the tissues' elasticity under wave fluctuation in vivo. Based on the model, a local deformation invariant is formulated using the 1st and 2nd order derivatives of the lesion volumetric CT image and used to generate elastic feature map of the lesion volume. From the feature map, tissue elastic features are extracted and fed to ML to perform lesion classification. Two pathologically proven image datasets of colon polyps and lung nodules were used to test the modeling strategy. The outcomes reached the score of area under the curve of receiver operating characteristics of 94.2% for the polyps and 87.4% for the nodules, resulting in an average gain of 5 to 20% over several existing state-of-the-art image feature-based lesion classification methods. The gain demonstrates the importance of extracting tissue characteristic features for lesion classification, instead of extracting image features, which can include various image artifacts and may vary for different protocols in image acquisition and different imaging modalities.

Transition State Stabilizing Effects of Oxygen and Sulfur Chalcogen Bond Interactions.

Non-covalent chalcogen bond (ChB) interactions have found utility in many fields, including catalysis, organic semiconductors, and crystal engineering. In this study, the transition stabilizing effects of ChB interactions of oxygen and sulfur were experimentally measured using a series of molecular rotors. The rotors were designed to form ChB interactions in their bond rotation transition states. This enabled the kinetic influences to be assessed by monitoring changes in the rotational barriers. Despite forming weaker ChB interactions, the smaller chalcogens were able to stabilize transition states and had measurable kinetic effects on the rotational barriers. Sulfur stabilized the bond rotation transition state by as much as -7.2 kcal/mol without electron-withdrawing groups. The key was to design a system where the sulfur σ ${\sigma }$ -hole was aligned with the lone pairs of the chalcogen bond acceptor. Oxygen rotors also could form transition state stabilizing ChB interactions but required electron-withdrawing groups. For both oxygen and sulfur ChB interactions, a strong correlation was observed between transition state stabilizing abilities and electrostatic potential (ESP) of the chalcogen, providing a useful predictive parameter for the rational design of future ChB systems.

Initial validation of the Turkish version of the defense mechanisms rating scales-self-report-30.

The Defense Mechanisms Rating Scales-Self Report-30 (DMRS-SR-30) was recently developed to add a self-report alternative to the assessment of defenses, reflecting their generally accepted hierarchical organization. In this study, we aimed to examine psychometric properties and factor structure of the Turkish language version of the DMRS-SR-30. The sample consisted of 1.002 participants who filled out a survey comprising the DMRS-SR-30, the Brief Symptom Inventory, and the Inventory of Personality Organization through Qualtrics. Confirmatory Factor Analysis indicated a three-factor structure (CFI = 0.89, RMSEA = 0.05) that confirms the DMRS theoretical frame with a relatively acceptable fit. Defensive categories and total scale scores showed good to excellent reliability (α values ranging from 0.64 to 0.89). Correlations between defenses, symptoms, and personality functioning demonstrated good convergent and discriminant validity. The individuals with clinically significant BSI scores (T-score ≥ 63) differed on the DMRS-SR-30 scores from the individuals in the non-clinical range. The Turkish version of the DMRS-SR-30 is a reliable and valid instrument to self-assess the hierarchy of defense mechanisms and overall defensive functioning. Moreover, the current study supports the validity of the tripartite model of defenses in a language and culture different from the origins of the DMRS and DMRS-SR-30.

Redox-Active Carboranyl Diphosphine as an Electron and Proton Transfer Agent.

In this work, we report the first example of the PCET reactivity for a boron cluster compound, the zwitterionic nido-carboranyl diphosphonium derivative 7-P(H)tBu2-10-P(H)iPr2-nido-C2B10H10. This main-group reagent efficiently transfers two electrons and two protons to quinones to yield hydroquinones and regenerate a neutral closo-carboranyl diphosphine, 1-PtBu2-2-PiPr2-closo-C2B10H10. As we have previously reported the conversion of this closo-carboranyl diphosphine into the zwitterionic nido- derivative upon reaction with main group hydrides, the transformation reported herein represents a complete synthetic cycle for the metal-free reduction of quinones, with the redox-active carboranyl diphosphine scaffold acting as a mediator. The proposed mechanism of this reduction, based on pKa determination, electrochemical studies, and kinetic isotope effect determination, involves the electron transfer from the nido- cluster to the quinone coupled with the delivery of protons.

Rabies knowledge gaps and risk behaviour in Dutch travellers: An observational cohort study.

BACKGROUND: Travellers visiting rabies-endemic countries are at risk of rabies infection. Assessing travellers' knowledge and risk perception of rabies and risk behaviour during travel can help identify knowledge gaps and improve pre-travel risk education. METHODS: Cohort study in Dutch adult travellers, using two surveys: one before travel to assess knowledge and perception of rabies, and one after return to identify risk behaviour during travel. RESULTS: The pre-travel and post-travel survey were completed by 301 and 276 participants, respectively. 222 participants had travelled to a high-risk rabies-endemic country. 21.6 % of the participants scored their rabies knowledge as poor. Some participants were unaware cats or bats can transmit rabies (26.6 % and 13.6 %, respectively), or that post-exposure prophylaxis (PEP) is required for certain exposures such as skin abrasions without bleeding or licks on damaged skin (35.5 % and 18.9 %, respectively), while 27.9 % of participants did not know PEP needs to be administered within one day. 115 participants (51.8 %) reported any form of contact with any animal during travel. Two participants reported animal exposure, of which one took adequate PEP measures. Risk factors for animal contact abroad were regularly touching cats or dogs at home or abroad, longer travel duration, having pets during childhood and being an animal lover. CONCLUSIONS: Pre-travel rabies risk education currently does not meet travellers' needs, which is reflected in knowledge gaps and engagement in risk behaviour during travel. During pre-travel health advice, avoiding animal contact abroad should be emphasized, and additional education is required about indications for PEP.

AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults.

OBJECTIVES: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population. METHODS: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile. RESULTS: A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT. CONCLUSION: Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures. CLINICAL RELEVANCE STATEMENT: Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients. KEY POINTS: Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.

Grasp Posture Variability Leads to Greater Ipsilateral Sensorimotor Beta Activation During Simulated Prosthesis Use.

Motor behaviour using upper-extremity prostheses of different levels is greatly variable, leading to challenges interpreting ideal rehabilitation strategies. Elucidating the underlying neural control mechanisms driving variability benefits our understanding of adaptation after limb loss. In this follow-up study, non-amputated participants completed simple and complex reach-to-grasp motor tasks using a body-powered transradial or partial-hand prosthesis simulator. We hypothesised that under complex task constraints, individuals employing variable grasp postures will show greater sensorimotor beta activation compared to individuals relying on uniform grasping, and activation will occur later in variable compared to uniform graspers. In the simple task, partial-hand variable and transradial users showed increased neural activation from the early to late phase of the reach, predominantly in the hemisphere ipsilateral to device use. In the complex task, only partial-hand variable graspers showed a significant increase in neural activation of the sensorimotor cortex from the early to the late phase of the reach. These results suggest that grasp variability may be a crucial component in the mechanism of neural adaptation to prosthesis use, and may be mediated by device level and task complexity, with implications for rehabilitation after amputation.

Effects of Breast Surgery on Interoceptive Awareness in Females.

Background: The driving force for many seeking plastic surgery is comfort in one's body. Along with comfort come satisfaction, improved self-awareness, and potential change in interoceptive awareness-a term defined as the conscious perception of one's body. Although conscious perception of bodily signals is influenced by many factors, sense of self and body image play significant roles. Studies show diminished interoceptive awareness in those with negative body image, but no research has assessed the impact of change in body image on interoceptive awareness. Objectives: The purpose of this study is to investigate how interoceptive awareness changes following elective breast surgery. Methods: The Multidimensional Assessment of Interoceptive Awareness Version 2 (MAIA-2) was administered to females undergoing breast surgery. A baseline survey was administered preoperatively, with follow-up surveys at 1 week, 1 month, and 3 months postoperatively. Results: Data were collected from 39 females and analyzed using paired t-tests to compare MAIA-2 overall and subscores over time. Significance was seen at 1 week for subcategories of "not distracting" and "trust," at 1 month for "trust," and 3 months for "not worrying," "emotional awareness," "self-regulation," and "trust." Overall survey averages were significantly increased at all postoperative intervals. Conclusions: From this study, it can be concluded that breast surgery positively impacts interoceptive awareness. These findings are clinically relevant as they offer providers' insight into the psychological effects of breast procedures. A comprehensive understanding of procedure outcomes enables providers to educate patients on both anticipated physical results and changes in sense of self.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age [±SD], 60 years ± 11; 143 female), was tested on two internal datasets (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the F1/Dice coefficient, SDs, and 95% CIs, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved F1/Dice scores of 85.5% ± 6.1 (95% CI: 83.1, 87.8) and 82.6% ± 15.3 (95% CI: 76.4, 88.7), with median volume estimation errors of 19.6% (IQR, 13.2%-29.0%) and 5.3% (IQR: 2.4%-9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a F1/Dice score of 83.0% ± 10.7 (95% CI: 79.8, 86.3) and median volume estimation error of 9.7% (IQR, 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in patients with cirrhosis and those with ovarian cancer, in concordance with expert radiologist assessments. Keywords: Abdomen/GI, Cirrhosis, Deep Learning, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also commentary by Aisen and Rodrigues in this issue.

GM-CSF-dependent CD301b+ lung dendritic cells confer tolerance to inhaled allergens.

The severity of allergic asthma is driven by the balance between allergen-specific T regulatory (Treg) and T helper (Th)2 cells. However, it is unclear whether specific subsets of conventional dendritic cells (cDCs) promote the differentiation of these two T cell lineaeges. We have identified a subset of lung resident type 2 cDCs (cDC2s) that display high levels of CD301b and have potent Treg-inducing activity ex vivo. Single cell RNA sequencing and adoptive transfer experiments show that during allergic sensitization, many CD301b+ cDC2s transition in a stepwise manner to CD200+ cDC2s that selectively promote Th2 differentiation. GM-CSF augments the development and maintenance of CD301b+ cDC2s in vivo, and also selectively expands Treg-inducing CD301b+ cDC2s derived from bone marrow. Upon their adoptive transfer to recipient mice, lung-derived CD301b+ cDC2s confer immunological tolerance to inhaled allergens. Thus, GM-CSF maintains lung homeostasis by increasing numbers of Treg-inducing CD301b+ cDC2s.

A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models.

RATIONALE AND OBJECTIVES: Pancreas segmentation accuracy at CT is critical for the identification of pancreatic pathologies and is essential for the development of imaging biomarkers. Our objective was to benchmark the performance of five high-performing pancreas segmentation models across multiple metrics stratified by scan and patient/pancreatic characteristics that may affect segmentation performance. MATERIALS AND METHODS: In this retrospective study, PubMed and ArXiv searches were conducted to identify pancreas segmentation models which were then evaluated on a set of annotated imaging datasets. Results (Dice score, Hausdorff distance [HD], average surface distance [ASD]) were stratified by contrast status and quartiles of peri-pancreatic attenuation (5 mm region around pancreas). Multivariate regression was performed to identify imaging characteristics and biomarkers (n = 9) that were significantly associated with Dice score. RESULTS: Five pancreas segmentation models were identified: Abdomen Atlas [AAUNet, AASwin, trained on 8448 scans], TotalSegmentator [TS, 1204 scans], nnUNetv1 [MSD-nnUNet, 282 scans], and a U-Net based model for predicting diabetes [DM-UNet, 427 scans]. These were evaluated on 352 CT scans (30 females, 25 males, 297 sex unknown; age 58 ± 7 years [ ± 1 SD], 327 age unknown) from 2000-2023. Overall, TS, AAUNet, and AASwin were the best performers, Dice= 80 ± 11%, 79 ± 16%, and 77 ± 18%, respectively (pairwise Sidak test not-significantly different). AASwin and MSD-nnUNet performed worse (for all metrics) on non-contrast scans (vs contrast, P < .001). The worst performer was DM-UNet (Dice=67 ± 16%). All algorithms except TS showed lower Dice scores with increasing peri-pancreatic attenuation (P < .01). Multivariate regression showed non-contrast scans, (P < .001; MSD-nnUNet), smaller pancreatic length (P = .005, MSD-nnUNet), and height (P = .003, DM-UNet) were associated with lower Dice scores. CONCLUSION: The convolutional neural network-based models trained on a diverse set of scans performed best (TS, AAUnet, and AASwin). TS performed equivalently to AAUnet and AASwin with only 13% of the training set size (8488 vs 1204 scans). Though trained on the same dataset, a transformer network (AASwin) had poorer performance on non-contrast scans whereas its convolutional network counterpart (AAUNet) did not. This study highlights how aggregate assessment metrics of pancreatic segmentation algorithms seen in other literature are not enough to capture differential performance across common patient and scanning characteristics in clinical populations.

Abdominal CT findings characteristic of Castleman disease: multi-centre review of 76 adult cases with abdominopelvic nodal involvement.

OBJECTIVE: Characterize the CT findings of abdominopelvic Castleman disease, including a new observation involving the perinodal fat. METHODS: Multi-centre search at 5 institutions yielded 76 adults (mean age, 42.1 ± 14.3 years; 38 women/38 men) meeting inclusion criteria of histopathologically proven Castleman disease with nodal involvement at abdominopelvic CT. Retrospective review of the dominant nodal mass was assessed for size, attenuation, and presence of calcification, and for prominence and soft-tissue infiltration of the perinodal fat. Hypervascular nodal enhancement was based on both subjective and objective comparison with aortic blood pool attenuation. RESULTS: Abdominal involvement was unicentric in 48.7% (37/76) and multicentric in 51.3% (39/76), including 31 cases with extra-abdominal involvement. Histopathologic subtypes included hyaline vascular variant (HVV), plasma cell variant (PCV), mixed HVV/PCV, and HHV-8 variant in 39, 25, 3 and 9 cases, respectively. The dominant nodal mass measured 4.4 ± 1.9 cm and 3.2 ± 1.7 cm in mean long- and short-axis, respectively, and appeared hypervascular in 58.6% (41/70 with IV contrast). Internal calcification was seen in 22.4% (17/76). Infiltration of the perinodal fat, with or without hypertrophy, was present in 56.6% (43/76), more frequent with hypervascular vs non-hypervascular nodal masses (80.5% vs 20.7%; P < .001). Among HVV cases, 76.9% were unicentric, 71.1% appeared hypervascular, and 69.2% demonstrated perinodal fat infiltration. CONCLUSION: Hypervascular nodal masses demonstrating prominence and infiltration of perinodal fat at CT can suggest the specific diagnosis of Castleman disease, especially the HVV. ADVANCES IN KNOWLEDGE: Abdominopelvic nodal masses that demonstrate hypervascular enhancement and prominent infiltration of the perinodal fat at CT can suggest the diagnosis of Castleman disease, but nonetheless requires tissue sampling.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening.

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.

Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis.

OBJECTIVE: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. MATERIALS AND METHODS: A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. RESULTS: The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). CONCLUSION: Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. CLINICAL RELEVANCE STATEMENT: Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. KEY POINTS: The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.