Clinical Utility of an Artificial Intelligence Radiomics-based Tool for Risk Stratification of Pulmonary Nodules.

BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. METHODS: We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. RESULTS: Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI, 0.48-0.69) and 0.65 (95% CI, 0.56-0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared to the Mayo model. CONCLUSIONS: Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

University of Pennsylvania Telehealth Research Center of Excellence.

Drawing from insights from communication science and behavioral economics, the University of Pennsylvania Telehealth Research Center of Excellence (Penn TRACE) is designing and testing telehealth strategies with the potential to transform access to care, care quality, outcomes, health equity, and health-care efficiency across the cancer care continuum, with an emphasis on understanding mechanisms of action. Penn TRACE uses lung cancer care as an exemplar model for telehealth across the care continuum, from screening to treatment to survivorship. We bring together a diverse and interdisciplinary team of international experts and incorporate rapid-cycle approaches and mixed methods evaluation in all center projects. Our initiatives include a pragmatic sequential multiple assignment randomized trial to compare the effectiveness of telehealth strategies to increase shared decision-making for lung cancer screening and 2 pilot projects to test the effectiveness of telehealth to improve cancer care, identify multilevel mechanisms of action, and lay the foundation for future pragmatic trials. Penn TRACE aims to produce new fundamental knowledge and advance telehealth science in cancer care at Penn and nationally.

A novel approach to treating opioid use disorders: Dual agonists of glucagon-like peptide-1 receptors and neuropeptide Y2 receptors.

The widespread misuse of opioids and opioid use disorder (OUD) together constitute a major public health crisis in the United States. The greatest challenge for successfully treating OUD is preventing relapse. Unfortunately, there are few FDA-approved medications to treat OUD and, while effective, these pharmacotherapies are limited by high relapse rates. Thus, there is a critical need for conceptually new approaches to developing novel medications to treat OUD. Here, we review an emerging preclinical literature that suggests that glucagon-like peptide-1 receptor (GLP-1R) agonists could be re-purposed for treating OUD. Potential limitations of this approach are also discussed along with an alternative strategy that involves simultaneously targeting and activating GLP-1Rs and neuropeptide Y2 receptors (Y2Rs) in the brain using a novel monomeric dual agonist peptide. Recent studies indicate that this combinatorial pharmacotherapy approach attenuates voluntary fentanyl taking and seeking in rats without producing adverse effects associated with GLP-1R agonist monotherapy alone. While future studies are required to comprehensively determine the behavioral effects of GLP-1R agonists and dual agonists of GLP-1Rs and Y2Rs in rodent models of OUD, these provocative preclinical findings highlight a potential new GLP-1R-based approach to preventing relapse in humans with OUD.