Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. METHODS: A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. RESULTS: In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. CONCLUSIONS: Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.

Mycobacterium vaccae alleviates allergic airway inflammation and airway hyper-responsiveness in asthmatic mice by altering intestinal microbiota.

Host immunity can influence the composition of the gut microbiota and consequently affect disease progression. Previously, we reported that a Mycobacterium vaccae vaccine could ameliorate allergic inflammation in asthmatic mice by regulating inflammatory immune processes. Here, we investigated the anti-inflammatory effects of M. vaccae on allergic asthma via gut microbiota modulation. An ovalbumin (OVA)-induced asthmatic murine model was established and treated with M. vaccae. Gut microbiota profiles were determined in 18 BALB/c mice using 16S rDNA gene sequencing and metabolomic profiling was performed using liquid chromatography quadrupole time-of-flight mass spectrometry. Mycobacterium vaccae alleviated airway hyper-reactivity and inflammatory infiltration in mice with OVA-induced allergic asthma. The microbiota of asthmatic mice is disrupted and that this can be reversed with M. vaccae. Additionally, a total of 24 differential metabolites were screened, and the abundance of PI(14:1(9Z)/18:0), a glycerophospholipid, was found to be correlated with macrophage numbers (r = 0.52, p = 0.039). These metabolites may affect chemokine (such as macrophage chemoattractant protein-1) concentrations in the serum, and ultimately affect pulmonary macrophage recruitment. Our data demonstrated that M. vaccae might alleviate airway inflammation and hyper-responsiveness in asthmatic mice by reversing imbalances in gut microbiota. These novel mechanistic insights are expected to pave the way for novel asthma therapeutic strategies.

Interpretation, Reporting, and Clinical Applications of Liver MR Elastography.

Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along with thresholds for diagnosis and staging of liver fibrosis; discusses current and emerging clinical applications; and describes the latest technical developments.

LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis.

Background The independent contribution of each Liver Imaging Reporting and Data System (LI-RADS) CT or MRI ancillary feature (AF) has not been established. Purpose To evaluate the association of LI-RADS AFs with hepatocellular carcinoma (HCC) and malignancy while adjusting for LI-RADS major features through an individual participant data (IPD) meta-analysis. Materials and Methods Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched from January 2014 to January 2022 for studies evaluating the diagnostic accuracy of CT and MRI for HCC using LI-RADS version 2014, 2017, or 2018. Using a one-step approach, IPD across studies were pooled. Adjusted odds ratios (ORs) and 95% CIs were derived from multivariable logistic regression models of each AF combined with major features except threshold growth (excluded because of infrequent reporting). Liver observation clustering was addressed at the study and participant levels through random intercepts. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2. Results Twenty studies comprising 3091 observations (2456 adult participants; mean age, 59 years ± 11 [SD]; 1849 [75.3%] men) were included. In total, 89% (eight of nine) of AFs favoring malignancy were associated with malignancy and/or HCC, 80% (four of five) of AFs favoring HCC were associated with HCC, and 57% (four of seven) of AFs favoring benignity were negatively associated with HCC and/or malignancy. Nonenhancing capsule (OR = 3.50 [95% CI: 1.53, 8.01]) had the strongest association with HCC. Diffusion restriction (OR = 14.45 [95% CI: 9.82, 21.27]) and mild-moderate T2 hyperintensity (OR = 10.18 [95% CI: 7.17, 14.44]) had the strongest association with malignancy. The strongest negative associations with HCC were parallels blood pool enhancement (OR = 0.07 [95% CI: 0.01, 0.49]) and marked T2 hyperintensity (OR = 0.18 [95% CI: 0.07, 0.45]). Seventeen studies (85%) had a high risk of bias. Conclusion Most LI-RADS AFs were independently associated with HCC, malignancy, or benignity as intended when adjusting for major features. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Crivellaro in this issue.

Risk Factors for Hospitalization Duration Longer Than 24 Hours Following Percutaneous Radiofrequency Ablation of Liver Tumours.

PURPOSE: Prior studies have described complications of radiofrequency ablation (RFA) of liver tumours. The aim of this study was to identify risk factors for hospitalization duration longer than 24 hours following RFA of liver tumours. METHODS: This retrospective, single-centre study included patients with liver tumours undergoing RFA between October 2017 and July 2020. Medical records were reviewed to collect patient, tumours, and procedure characteristics for each RFA session. The association between potential risk factors and duration of hospitalization (less than or more than 24 hours) was analyzed using univariate and multivariate logistic regressions. RESULTS: Our study included 291 patients (mean age: 65.2 ± 11.2 [standard deviation]; 201 men) undergoing 324 RFA sessions. Sixty-eight sessions (21.0%) resulted in hospitalization of more than 24 hours. Multivariate analysis identified each additional needle insertion per session (OR 1.4; 95% CI [1.1-1.9]; P = .02), RFA performed in segment V (OR 2.8; 95% CI [1.4-5.7]; P = .004), and use of artificial pneumothorax (OR 14.5; 95% CI [1.4-146.0]; P = .02) as potential risk factors. A history of hepatic encephalopathy (OR 2.6; 95% CI [1.1-6.0]; P = .03) was only significant in univariate analysis. Post-hoc, subgroup analysis of patients with hepatocellular carcinoma (69.8%) did not identify other risk factors. CONCLUSION: Risk factors for a hospitalization duration longer than 24 hours include a higher number of needle insertions per session, radiofrequency ablation in segment V, and use of an artificial pneumothorax.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever­growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi­society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Low-Background CRISPR/Cas12a Sensors for Versatile Live-Cell Biosensing.

The trans-cleavage activity of CRISPR/Cas12a has been widely used in biosensing. However, many CRISPR/Cas12a-based biosensors, especially those that work in "on-off-on" mode, usually suffer from high background and thus impossible intracellular application. Herein, this problem is efficiently overcome by elaborately designing the activator strand (AS) of CRISPR/Cas12a using the "RESET" effect found by our group. The activation ability of the as-designed AS to CRISPR/Cas12a can be easily inhibited, thus assuring a low background for subsequent biosensing applications, which not only benefits the detection sensitivity improvement of CRISPR/Cas12a-based biosensors but also promotes their applications in live cells as well as makes it possible to design high-performance biosensors with greatly improved flexibility, thus achieving the analysis of a wide range of targets. As examples, by using different strategies such as strand displacement, strand cleavage, and aptamer-substrate interaction to reactivate the inhibited enzyme activity, several CRISPR/Cas12a-based biosensing systems are developed for the sensitive and specific detection of different targets, including nucleic acid (miR-21), biological small molecules (ATP), and enzymes (hOGG1), giving the detection limits of 0.96 pM, 8.6 µM, and 8.3 × 10-5 U/mL, respectively. Thanks to the low background, these biosensors are demonstrated to work well for the accurate imaging analysis of different biomolecules in live cells. Moreover, we also demonstrate that these sensing systems can be easily combined with lateral flow assay (LFA), thus holding great potential in point-of-care testing, especially in poorly equipped or nonlaboratory environments.

LI-RADS: Looking Back, Looking Forward.

Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.

Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis.

Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.

Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis.

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

A Multicenter Assessment of Interreader Reliability of LI-RADS Version 2018 for MRI and CT.

Background Various limitations have impacted research evaluating reader agreement for Liver Imaging Reporting and Data System (LI-RADS). Purpose To assess reader agreement of LI-RADS in an international multicenter multireader setting using scrollable images. Materials and Methods This retrospective study used deidentified clinical multiphase CT and MRI and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted. Examination dates were October 2017 to August 2018 at the coordinating center. One untreated observation per examination was randomly selected using observation identifiers, and its clinically assigned features were extracted from the report. The corresponding LI-RADS version 2018 category was computed as a rescored clinical read. Each examination was randomly assigned to two of 43 research readers who independently scored the observation. Agreement for an ordinal modified four-category LI-RADS scale (LR-1, definitely benign; LR-2, probably benign; LR-3, intermediate probability of malignancy; LR-4, probably hepatocellular carcinoma [HCC]; LR-5, definitely HCC; LR-M, probably malignant but not HCC specific; and LR-TIV, tumor in vein) was computed using intraclass correlation coefficients (ICCs). Agreement was also computed for dichotomized malignancy (LR-4, LR-5, LR-M, and LR-TIV), LR-5, and LR-M. Agreement was compared between research-versus-research reads and research-versus-clinical reads. Results The study population consisted of 484 patients (mean age, 62 years ± 10 [SD]; 156 women; 93 CT examinations, 391 MRI examinations). ICCs for ordinal LI-RADS, dichotomized malignancy, LR-5, and LR-M were 0.68 (95% CI: 0.61, 0.73), 0.63 (95% CI: 0.55, 0.70), 0.58 (95% CI: 0.50, 0.66), and 0.46 (95% CI: 0.31, 0.61) respectively. Research-versus-research reader agreement was higher than research-versus-clinical agreement for modified four-category LI-RADS (ICC, 0.68 vs 0.62, respectively; P = .03) and for dichotomized malignancy (ICC, 0.63 vs 0.53, respectively; P = .005), but not for LR-5 (P = .14) or LR-M (P = .94). Conclusion There was moderate agreement for LI-RADS version 2018 overall. For some comparisons, research-versus-research reader agreement was higher than research-versus-clinical reader agreement, indicating differences between the clinical and research environments that warrant further study. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Johnson and Galgano and Smith in this issue.

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases.

BACKGROUND: Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance. METHODS: We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set. RESULTS: TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020). CONCLUSIONS: Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

Isolation and characterization of the new isolated bacteriophage YZU-L1 against Citrobacter freundii from a package-swelling of meat product.

Citrobacter freundii is an important foodborne pathogen that can cause urethritis, bacteremia, necrotizing abscess, and meningitis in infants. In this study, a gas-producing isolate from vacuum-packed meat products was identified as C. freundii by 16S rDNA. In addition, a new virulent phage YZU-L1, which could specifically lyse C. freundii, was isolated from sewage samples in Yangzhou. Transmission electron microscopy showed that phage YZU-L1 had a polyhedral head of 73.51 nm in diameter and a long tail of 161.15 nm in length. According to phylogenetic analysis employing the terminase large subunit, phage YZU-L1 belonged to the Demerecviridae family and the Markadamsvirinae subfamily. The burst size was 96 PFU/cell after 30 min of latent period and 90 min of rising period. Phage YZU-L1 could maintain high activity at pH of 4-13, and resist 50 °C for up to 60 min. The complete genome of YZU-L1 was 115,014 bp double-stranded DNA with 39.94% G + C content, encoding 164 open reading frames (ORFs), without genes encoding for virulence, antibiotic resistance, or lysogenicity. Phage YZU-L1 treatment significantly reduced the viable bacterial count of C. freundii in a sterile fish juice model, which is expected to be a natural agent for the biocontrol of C. freundii in foods.

CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture.

BACKGROUND: Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression. METHODOLOGY: In this retrospective study, 80 patients treated for a ruptured AAA between January 2001 and August 2018 were matched with 80 non-ruptured patients based on maximal AAA diameter, age, and sex. Calcification volume and dispersion, morphologic, and clinical variables were compared between both groups using a univariable analysis with p = 0.05 and multivariable analysis through machine learning and LASSO regression. We used AUC for machine learning and odds ratios for regression to measure performance. RESULTS: Mean age of patients was 74.0 ± 8.4 years and 89% were men. AAA diameters were equivalent in both groups (80.9 ± 17.5 vs 79.0 ± 17.3 mm, p = 0.505). Ruptured aneurysms contained a smaller number of calcification aggregates (18.0 ± 17.9 vs 25.6 ± 18.9, p = 0.010) and were less likely to have a proximal neck (45.0% vs 76.3%, p < 0.001). In the machine learning analysis, 5 variables were associated to AAA rupture: proximal neck, antiplatelet use, calcification number, Euclidian distance between calcifications, and standard deviation of the Euclidian distance. A follow-up LASSO regression was concomitant with the findings of the machine learning analysis regarding calcification dispersion but discordant on calcification number. CONCLUSION: There might be more to AAA calcifications that what is known in the present literature. We need larger prospective studies to investigate if indeed, calcification dispersion affects rupture risk. CLINICAL RELEVANCE STATEMENT: Ruptured aneurysms are possibly more likely to have their calcification volume concentrated in a smaller geographical area. KEY POINTS: • Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. • For a given calcification volume, AAAs with well-distributed calcification clusters could be less likely to rupture. • A machine learning model including AAA calcifications better predicts rupture compared to a model based solely on maximal diameter and sex alone, although it might be prone to overfitting.

Semiparametric normal transformation joint model of multivariate longitudinal and bivariate time-to-event data.

Joint models for longitudinal and survival data (JMLSs) are widely used to investigate the relationship between longitudinal and survival data in clinical trials in recent years. But, the existing studies mainly focus on independent survival data. In many clinical trials, survival data may be bivariately correlated. To this end, this paper proposes a novel JMLS accommodating multivariate longitudinal and bivariate correlated time-to-event data. Nonparametric marginal survival hazard functions are transformed to bivariate normal random variables. Bayesian penalized splines are employed to approximate unknown baseline hazard functions. Incorporating the Metropolis-Hastings algorithm into the Gibbs sampler, we develop a Bayesian adaptive Lasso method to simultaneously estimate parameters and baseline hazard functions, and select important predictors in the considered JMLS. Simulation studies and an example taken from the International Breast Cancer Study Group are used to illustrate the proposed methodologies.

US Quantification of Liver Fat: Past, Present, and Future.

Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical costs, and may lead to liver-related morbidity and mortality. There is an urgent need for accurate, reproducible, accessible, and noninvasive techniques appropriate for detecting and quantifying liver fat in the general population and for monitoring treatment response in at-risk patients. CT may play a potential role in opportunistic screening, and MRI proton-density fat fraction provides high accuracy for liver fat quantification; however, these imaging modalities may not be suited for widespread screening and surveillance, given the high global prevalence. US, a safe and widely available modality, is well positioned as a screening and surveillance tool. Although well-established qualitative signs of liver fat perform well in moderate and severe steatosis, these signs are less reliable for grading mild steatosis and are likely unreliable for detecting subtle changes over time. New and emerging quantitative biomarkers of liver fat, such as those based on standardized measurements of attenuation, backscatter, and speed of sound, hold promise. Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. For each US-based technique, they describe the concept, measurement method, advantages, and limitations. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

Universal Liver Imaging Lexicon: Imaging Atlas for Research and Clinical Practice.

The use of standardized terms in assessing and reporting disease processes has well-established benefits, such as clear communication between radiologists and other health care providers, improved diagnostic accuracy and reproducibility, and the enhancement and facilitation of research. Recently, the Liver Imaging Reporting and Data System (LI-RADS) Steering Committee released a universal liver imaging lexicon. The current version of the lexicon includes 81 vetted and precisely defined terms that are relevant to acquisition of images using all major liver imaging modalities and contrast agents, as well as lesion- and organ-level features. Most terms in the lexicon are applicable to all patients undergoing imaging of the liver, and only a minority of the terms are strictly intended to be used for patients with high risk factors for hepatocellular carcinoma. This pictorial atlas familiarizes readers with the liver imaging lexicon and includes discussion of general concepts, providing sample definitions, schematics, and clinical examples for a subset of the terms in the liver imaging lexicon. The authors discuss general, technical, and imaging feature terms used commonly in liver imaging, with the goal of illustrating their use for clinical and research applications. Work of the U.S. Government published under an exclusive license with the RSNA. Online supplemental material is available for this article.

Diversity and dynamics of endosymbionts in a single population of sweet potato weevil, Cylas formicarius (Coleoptera: Brentidae): a preliminary study.

Endosymbionts live symbiotically with insect hosts and play important roles in the evolution, growth, development, reproduction, and environmental fitness of hosts. Weevils are one of the most abundant insect groups that can be infected by various endosymbionts, such as Sodalis, Nardonella, and Wolbachia. The sweet potato weevil, Cylas formicarius (Coleoptera: Brentidae), is a notorious pest in sweet potato (Ipomoea batatas L.) cultivation. Currently, little is known about the presence of endosymbionts in C. formicarius. Herein, we assessed the endosymbiont load of a single geographic population of C. formicarius. The results showed that Nardonella and Rickettsia could infect C. formicarius at different rates, which also varied according to the developmental stages of C. formicarius. The relative titer of Nardonella was significantly related to C. formicarius developmental stages. The Nardonella-infecting sweet potato weevils were most closely related to the Nardonella in Sphenophorus levis (Coleoptera, Curculionidae). The Rickettsia be identified in bellii group. These results preliminarily revealed the endosymbionts in C. formicarius and helped to explore the diversity of endosymbionts in weevils and uncover the physiological roles of endosymbionts in weevils.

Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in parallel. Several guidelines have provided reporting standards for publications of AI-based research in medicine and radiology. Yet, there is an unmet need for recommendations on the assessment of AI software before adoption and after commercialization. As the radiology AI ecosystem continues to grow and mature, a formalization of system assessment and evaluation is paramount to ensure patient safety, relevance and support to clinical workflows, and optimal allocation of limited AI development and validation resources before broader implementation into clinical practice. To fulfil these needs, we provide a glossary for AI software types, use cases and roles within the clinical workflow; list healthcare needs, key performance indicators and required information about software prior to assessment; and lay out examples of software performance metrics per software category. This conceptual framework is intended to streamline communication with the AI software industry and provide healthcare decision makers and radiologists with tools to assess the potential use of these software. The proposed software evaluation framework lays the foundation for a radiologist-led prospective validation network of radiology AI software. Learning Points: The rapid expansion of AI applications in radiology requires standardization of AI software specification, classification, and evaluation. The Canadian Association of Radiologists' AI Tech & Apps Working Group Proposes an AI Specification document format and supports the implementation of a clinical expert evaluation process for Radiology AI software.

Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring.

We consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the Metropolis-Hastings algorithm within the Gibbs sampler, we propose a feasible Bayesian method to simultaneously estimate unknown parameters of interest, and to fit baseline survival and censoring functions. Intensive simulation studies are conducted to assess the performance of the proposed method. The use of the proposed method is also illustrated in the analysis of a data set from the International Breast Cancer Study Group.