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Introduction: Cancer-associated cachexia (CC) is a progressive syndrome characterized by unintentional weight loss, muscle atrophy, fatigue, and poor outcomes that affects most patients with pancreatic ductal adenocarcinoma (PDAC). The ability to identify and classify CC stage along its continuum early in the disease process is challenging but critical for management. Objectives: The main objective of this study was to determine the prevalence of CC stage overall and by sex and race and ethnicity among treatment-naïve PDAC cases using clinical, nutritional, and functional criteria. Secondary objectives included identifying the prevalence and predictors of higher symptom burden, supportive care needs, and quality of life (QoL), and examining their influence on overall survival (OS). Materials and methods: A population-based multi-institutional prospective cohort study of patients with PDAC was conducted between 2018 and 2021 by the Florida Pancreas Collaborative. Leveraging patient-reported data and laboratory values, participants were classified at baseline into four stages [noncachexia (NCa), pre-cachexia (PCa), cachexia (Ca), and refractory cachexia (RCa)]. Multivariate regression, Kaplan Meier analyses, and Cox regression were conducted to evaluate associations. Results: CC stage was estimated for 309 PDAC cases (156 females, 153 males). The overall prevalence of NCa, PCa, Ca, and RCa was 12.9%, 24.6%, 54.1%, and 8.4%, respectively. CC prevalence across all CC stages was highest for males and racial and ethnic minorities. Criteria differentiated NCa cases from other groups, but did not distinguish PCa from Ca. The most frequently reported symptoms included weight loss, fatigue, pain, anxiety, and depression, with pain significantly worsening over time. The greatest supportive care needs included emotional and physical domains. Males, Black people, and those with RCa had the worst OS. Conclusions: Using clinical, nutritional, and functional criteria, nearly onequarter of the PDAC cases in our diverse, multi-institutional cohort had PCa and 62.5% had Ca or RCa at the time of diagnosis. The PCa estimate is higher than that reported in prior studies. We recommend these criteria be used to aid in CC classification, monitoring, and management of all incident PDAC cases. Findings also highlight the recommendation for continued emotional support, assistance in alleviating pain, and supportive care needs throughout the PDAC treatment journey. [ABSTRACT FROM AUTHOR]

Simple Summary: Cachexia occurs in up to 80% of patients with cancer. Although cancer-associated cachexia dramatically decreases overall survival and quality of life, it is often overlooked. To make meaningful progress in identifying cancer cachexia earlier and finding treatments for this condition, Moffitt Cancer Center held its first Cachexia Working Group Retreat in 2022. This manuscript describes the priorities discussed at the retreat and highlights collaborations that arose afterward. For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the deadliest forms of cancer, characterized by high rates of metastasis, late detection, and poor prognoses. Artificial intelligence and machine learning (AI/ML) have proven to be highly effective in improving the current standard of care for many cancers, including PDAC. This review article provides a holistic overview of high-impact, transformative AI/ML applications in various areas of PDAC care. Reflecting a patient's medical journey, these areas include screening, diagnosis, treatment, and post-treatment surveillance. Obstacles and limitations in AI/ML applications within the context of PDAC are also discussed, along with potential solutions and future directions. Collectively, this review article offers novel approaches and meaningful insights, potentially leading to solutions for the multifaceted challenges inherent in PDAC. Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations. [ABSTRACT FROM AUTHOR]

Rationale Activity levels in patients with pulmonary arterial hypertension (PAH) have correlated with surrogate markers of disease severity. It is not known whether physical activity measures are useful in monitoring patients with PAH. Objectives This pilot study aimed to evaluate whether change in physical activity measured by an accelerometer correlates with changes in six-minute walk distance (6MWD), echocardiographic parameters, NT-proBNP, or health-related quality-of-life measures (HRQOL). Methods The study design was a prospective, observational study in subjects with prevalent PAH. Subjects wore a wrist-worn accelerometer (Fitbit Charge HR®) between two outpatient visits. Daily step count and activity levels were recorded, and the change over time was correlated with changes in 6MWD, echocardiographic parameters, HRQOL, and NT-proBNP. Measurements and Main Results 30 subjects were enrolled, of which 20 patients had adequate accelerometer data to be analyzed over the study duration. The mean duration of follow-up was 136.4 ( ± 47.3) days. The change in daily step count correlated with a change in 6MWD (r 0.43, p 0.05). Changes in duration spent in moderately active (r 0.52, p 0.02), lightly active (r 0.48, p 0.05), and sedentary activity levels (r - 0.54, p 0.02) correlated with a change in HRQOL. Changes in activity levels did not correlate with echocardiographic measures or NT-pro BNP. Conclusions Changes in daily step count and time spent at fairly active, lightly active, and sedentary activity levels correlate with changes in 6MWD, and HRQOL in subjects with PAH suggesting that accelerometry may be a useful monitoring tool., Author(s): Sameep Sehgal [sup.1] [sup.9] , Ananda Chowdhury [sup.2] , Fadi Rabih [sup.3] , Abhishek Gadre [sup.4] , Margaret M. Park [sup.5] [sup.6] , Manshi Li [sup.7] , Xiaofeng Wang [...]

BackgroundThe development of tools to monitor the right ventricle in pulmonary arterial hypertension (PAH) is of clinical importance. PAH is associated with pathologic expression of the transcription factor hypoxia-inducible factor (HIF)-1α, which induces glycolytic metabolism and mobilization of proangiogenic progenitor (CD34(+)CD133(+)) cells. We hypothesized that PAH cardiac myocytes have a HIF-related switch to glycolytic metabolism that can be detected with fasting 2-deoxy-2-[(18)F]fluoro-d-glucose positron emission tomography (FDG-PET) and that glucose uptake is informative for cardiac function.MethodsSix healthy control subjects and 14 patients with PAH underwent fasting FDG-PET and echocardiogram. Blood CD34(+)CD133(+) cells and erythropoietin were measured as indicators of HIF activation. Twelve subjects in the PAH cohort underwent repeat studies 1 year later to determine if changes in FDG uptake were related to changes in echocardiographic parameters or to measures of HIF activation.Measurements and resultsFDG uptake in the right ventricle was higher in patients with PAH than in healthy control subjects and correlated with echocardiographic measures of cardiac dysfunction and circulating CD34(+)CD133(+) cells but not erythropoietin. Among patients with PAH, FDG uptake was lower in those receiving β-adrenergic receptor blockers. Changes in FDG uptake over time were related to changes in echocardiographic parameters and CD34(+)CD133(+) cell numbers. Immunohistochemistry of explanted PAH hearts of patients undergoing transplantation revealed that HIF-1α was present in myocyte nuclei but was weakly detectable in control hearts.ConclusionsPAH hearts have pathologic glycolytic metabolism that is quantitatively related to cardiac dysfunction over time, suggesting that metabolic imaging may be useful in therapeutic monitoring of patients.