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Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.
Nat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFRadiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction.
J Immunother Cancer
January 2025
Vall d'Hebron Institute of Oncology, Barcelona, Spain.
Background: The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomics, to comprehensively evaluate TIME across diverse cancers. To address these challenges, we develop an ICI response signature by integrating radiomics with T cell-inflamed gene-expression profiles.
View Article and Find Full Text PDFNon-colorectal Cancer Screening and Vaccinations in Patients with Inflammatory Bowel Disease: Expert Review.
Clin Gastroenterol Hepatol
January 2025
Inflammatory Bowel Disease Center, Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville FL. Electronic address:
Description: The aim of this American Gastroenterological Association (AGA) Clinical Practice Update (CPU) is to provide best practice advice (BPA) statements for gastroenterologists and other health care providers who provide care to patients with inflammatory bowel disease (IBD). The focus is on IBD-specific screenings (excluding colorectal cancer screening, which is discussed separately) and vaccinations. We provide guidance to ensure that patients are up to date with the disease-specific cancer screenings, vaccinations, as well as advice for mental health and general wellbeing.
View Article and Find Full Text PDFDemographic inequities and cumulative environmental burdens within communities near superfund sites on Long Island, New York.
Health Place
January 2025
Yale School of Public Health, Department of Environmental Health Sciences, 60 College St., New Haven, CT, USA. Electronic address:
Nassau and Suffolk Counties of Long Island, New York are densely populated and contain 34 federally-designated and 449 state-designated Superfund sites, potentially exposing communities to toxic releases. We conducted a distributive justice analysis assessing proximity to Superfund sites, community socio-demographics, and other environmental burdens. Socio-demographic and environmental variables for 665 census tracts were obtained from the United States Census and Environmental Protection Agency's Environmental Justice Screening and Mapping Tool.
View Article and Find Full Text PDFIdentification of biomarkers for knee osteoarthritis through clinical data and machine learning models.
Sci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
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