Citations

BACKGROUND: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization. METHODS: We included 18 079 patients with cardiac PET/CT from 6 sites. A deep learning model estimated cardiac chamber volumes and left ventricular mass from CT attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis. RESULTS: During a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio [HR], 1.25 [95% CI, 1.19–1.30]), right atrial volume (HR, 1.29 [95% CI, 1.23–1.35]), right ventricular volume (HR, 1.25 [95% CI, 1.20–1.31]), left ventricular volume (HR, 1.27 [95% CI, 1.23–1.35]), and left ventricular mass (HR, 1.25 [95% CI, 1.18–1.32]) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 [95% CI, 1.0–1.19]) and ventricular mass (odds ratio, 1.12 [95% CI, 1.6–1.17]) were independent predictors of reduced myocardial flow reserve. CONCLUSIONS: Deep learning–derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.

Abstract Purpose Gastric cancer is a leading cause of mortality worldwide, with pronounced variations in outcomes across populations. This study investigates U.S. trends in age-adjusted mortality rates (AAMRs) for gastric cancer from 1999 to 2023, examining differences by sex, race, region, and urbanization to uncover critical patterns and inequities. Methods Data were extracted from the CDC WONDER database using ICD-10 code C16 to identify gastric cancer-related deaths in adults aged 25 and older. AAMRs per 1,000,000 population were calculated and stratified by year, sex, race/ethnicity, geographic region, and urban-rural classification. Trends were analyzed using Joinpoint regression to determine annual percentage changes (APC) and 95% confidence intervals (CI). Statistical significance was set at p < 0.05. Results From 1999 to 2023, there were 312,300 gastric cancer deaths (185,391 men, 126,909 women). Overall, AAMRs declined consistently, with APCs of -3.4 for the period 1999 to 2007, -2.2 for 2007 to 2018, and − 1.2 for 2018 to 2023. Men showed a more pronounced decline, with APCs of -3.6 for 1999 to 2009 and − 2.2 for 2009 to 2023, compared to women, who had APCs of -3.1 for 1999 to 2008, -1.9 for 2008 to 2018, and − 0.8 for 2018 to 2023. Non-Hispanic Black populations experienced a decline in AAMR with an APC of -3.3 for 1999 to 2020, while Hispanic/Latino and Asian/Pacific Islander populations showed declines with APCs of -2.8 for 1999 to 2011 and − 3.8 for 1999 to 2020, respectively. Metropolitan areas saw AAMR reductions with APCs of -3.3 for 1999 to 2008 and − 2.2 for 2008 to 2020, while non-metropolitan areas had larger initial declines with an APC of -3.6 for 1999 to 2006 but slower decreases with an APC of -2.0 for 2006 to 2020. Geographically, the Northeast had the highest AAMR (62.9), followed by the West (61.7), South (57.4), and Midwest (50.9). Hawaii recorded the highest state-level AAMR (96.5), while Wyoming had the lowest (36.6). States in the top 90th percentile included Alaska, California, and New York, whereas Utah, Idaho, and Iowa were among the lowest. Conclusion Gastric cancer mortality in the U.S. has declined over the past two decades, but the pace of reduction has slowed in recent years. Significant disparities persist across sex, racial, and geographic groups, underscoring the need for targeted interventions to address these inequities and improve outcomes in vulnerable populations.

Abstract Microbiome sequencing data are inherently sparse and compositional, with excessive zeros arising from biological absence or insufficient sampling. These zeros pose significant challenges for downstream analyses, particularly those that require log-transformation. We introduce BMDD (BiModal Dirichlet Distribution), a novel probabilistic modeling framework for accurate imputation of microbiome sequencing data. Unlike existing imputation approaches that assume unimodal abundance, BMDD captures the bimodal abundance distribution of the taxa via a mixture of Dirichlet priors. It uses variational inference and a scalable expectation-maximization algorithm for efficient imputation. Through simulations and real microbiome datasets, we demonstrate that BMDD outperforms competing methods in reconstructing true abundances and improves the performance of differential abundance analysis. Through multiple posterior samples, BMDD enables robust inference by accounting for uncertainty in zero imputation. Our method offers a principled and computationally efficient solution for analyzing high-dimensional, zero-inflated microbiome sequencing data and is broadly applicable in microbial biomarker discovery and host-microbiome interaction studies. BMDD is available at: https://github.com/zhouhj1994/BMDD . Author Summary Understanding the microbes living in and on our bodies—the microbiome—relies on analyzing complex sequencing data. However, these data often contain many zeros, either because a microbe is truly absent or simply missed due to insufficient sampling. These missing values make it hard to accurately analyze microbial patterns and identify important differences between groups, especially for methods that work on a log scale. To address this, we developed a new method called BMDD that uses a more realistic model to impute the zeros. Unlike existing tools that assume each microbe follows an unimodal abundance distribution, BMDD allows for microbes to follow a bimodal distribution, so they could behave differently in different conditions. It provides not just a single guess, but a range of possible values to better reflect the uncertainty. Our testing shows that BMDD more accurately recovers the true microbial profiles and improves the ability to detect meaningful differences between groups. This method can help researchers gain clearer insights into how the microbiome affects health and disease.

Abstract The 21st Century Cures Act Final Rule (Final Rule) increases patient access to their health records but raises concerns about distress and misunderstanding of automatically released results and documentation. Little is known about genetic counselors' (GCs') experiences with the Final Rule. In Fall 2023, we conducted a cross‐sectional survey of US GCs about the Final Rule's perceived impact on practice and assessed if being the ordering versus non‐ordering provider for genetic testing affected perceptions of Final Rule‐related changes. GCs ( n = 102) reported demographic and workplace characteristics, institutional policy changes, workflow changes, and perceived patient harms and benefits due to the Final Rule. To compare ordering and non‐ordering providers, we conducted Fisher's exact tests for categorical variables and trend tests for ordinal variables. Open‐response questions elicited examples of positive and negative patient impacts, the effects of patient characteristics, and practice changes in response to the Final Rule. Twenty‐seven GCs were ordering providers and 66 were non‐ordering providers. Relative to ordering providers, non‐ordering providers expressed stronger agreement with statements indicating concern about the emotional impact on patients reviewing results or notes without support ( p = 0.002, 33% vs. 51% strongly agree), patients misunderstanding or misinterpreting results ( p = 0.025, 44% vs. 70% strongly agree), and patients contacting the inappropriate party to discuss results ( p = 0.023, 19% vs. 48% strongly agree). Agreement with the statement “patients have more knowledge/context/questions at our disclosure session due to previous results review” also differed by ordering provider status ( p = 0.007). In open responses, GCs expressed concerns about patients' strong emotional reactions, patients misinterpreting results, workflow disruptions, and widening health disparities. Benefits included patients' ability to be reassured, informed, or empowered earlier; ease of sharing health information; more efficient workflow due to the automatic release of results. These results emphasize the importance of clear communication within health systems and between patients and providers.

AbstractINTRODUCTIONCerebral microhemorrhages (CMHs) are detectable by magnetic resonance imaging (MRI). CMHs in deep brain regions are linked to hypertensive vasculopathy, while those in lobar regions with amyloid beta (Aβ) deposition in blood vessels. This study aims to determine the association between anti‐thrombotic treatment and CMH prevalence among cognitively asymptomatic adults, and to assess the role of Aβ markers, apolipoprotein E (APOE) ε4 carrier status, and cardiovascular risk factors in CMH development.METHODSUsing baseline data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies, we examined CMH presence via 3T MRI, along with medication use, APOE ε4 carrier status, medical history, and blood pressure.RESULTSOur analysis showed a significantly higher prevalence of CMHs in the A4 cohort (17.3%) compared to the LEARN cohort (2.6%).DISCUSSIONFactors such as male sex, age, Aβ markers, and APOE ε4 status were significantly associated with higher CMH prevalence in the A4 cohort. However, anti‐thrombotic treatment did not show association with overall CMHs.Highlights Male sex, age > 75, amyloid beta (Aβ) burden, and apolipoprotein E (APOE) ε4 homozygosity are significantly associated with higher prevalence of CMHs (cerebral microhemorrhages) in a cohort of cognitively asymptomatic individuals. Male sex, age > 75, Aβ burden, and APOE ε4 homozygosity are significantly associated with higher prevalence of lobar CMHs in a cohort of cognitively asymptomatic individuals. Anti‐platelet or anti‐coagulant usage were not associated with an increased prevalence of CMHs in either brain location or overall, in a cohort of cognitively asymptomatic individuals. History of a lipid disorder is associated with a higher prevalence of lobar CMHs in a cohort of cognitively asymptomatic individuals.

In the rapidly evolving field of oncology, education and knowledge sharing have become essential aspects of oncology practice and must-needed tools to help patients conquer cancer. Oncology education is an art that needs to be mastered by applying scientific learning methods through structured frameworks into it, in order to have an impact. To effectively educate and share knowledge with oncology professionals, we propose following a “chain of education”. This chain consists of five links that are essential for creating a successful educational activity: “who”, “why”, “where”, “what”, and “how”. By following the “chain of education” through all links leading to the final key of how to educate, and guided by learning science principles, educators can optimize and enhance their teaching and knowledge-sharing skills and educate learners with more impact. Two common scenarios of medical education in oncology are the education of trainees and the education of community oncologists. In this article, we review them through the prism of the “chain of education”. We discuss how using effective learning science principles can guide small changes that lead to significant impacts in trainees' education and can also leverage social media platforms in the education of community oncologists. Alongside this described framework, we briefly review some available oncology education resources, and we discuss incorporating the “chain of education” into future technologies.