During the past decade, with its breakthroughs in systems biology, precision medicine (PM) has emerged as a novel health-care paradigm. Challenging reductionism and broad-based approaches in medicine, PM is an approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. It involves integrating information from multiple sources in a holistic manner to achieve a definitive diagnosis, focused treatment, and adequate response assessment. Biomedical imaging and imaging-guided interventions, which provide multiparametric morphologic and functional information and enable focused, minimally invasive treatments, are key elements in the infrastructure needed for PM. The emerging discipline of radiogenomics, which links genotypic information to phenotypic disease manifestations at imaging, should also greatly contribute to patient-tailored care. Because of the growing volume and complexity of imaging data, decision-support algorithms will be required to help physicians apply the most essential patient data for optimal management. These innovations will challenge traditional concepts of health care and business models. Reimbursement policies and quality assurance measures will have to be reconsidered and adapted. In their 10th biannual symposium, which was held in August 2013, the members of the International Society for Strategic Studies in Radiology discussed the opportunities and challenges arising for the imaging community with the transition to PM. This article summarizes the discussions and central messages of the symposium.
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Alzheimers Dement
December 2024
Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau - Autonomous University of Barcelona, Barcelona, Catalonia, Spain.
Background: Alzheimer's and related disorders (ADRD) represent a range of neurodegenerative conditions characterized by abnormal protein deposits in the brain. Despite advances, there is a need for enhanced diagnostic and treatment approaches that acknowledge the diversity of ADRD. This project introduces the Alzheimer's and Related Disorders Multicenter Archive (ARMA), a collaborative platform with an advanced Electronic Data Capture (EDC) system linked to Electronic Medical Records (EMR) designed to refine ADRD diagnosis and natural history understanding, thus informing precision medicine.
View Article and Find Full Text PDFBackground: Early-onset Alzheimer's disease (EOAD) associated with amyloid precursor protein (APP) duplications or presenilin (PSEN) variants increases risk of seizures. Targeting epileptiform activity with antiseizure medicine (ASM) administration to AD patients may beneficially attenuate cognitive decline (Vossel et al, JAMA Neurology 2021). However, whether mechanistically distinct ASMs differentially suppress seizures in discrete EOAD models is understudied (Lehmann et al, Neurochem Res 2021).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Background: Alzheimer's disease (AD) presents challenges with its complex neurodegenerative mechanisms, leading to a high failure rate in clinical trials. While drug repositioning offers a cost-effective solution, the lack of a subtype-driven strategy hinders success. Previously, we defined genetic subtypes and their prioritized genes for each genetic subtype (Sahelijo et al.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
NYU Langone Health, New York, NY, USA.
Background: Large language models (LLMs) provide powerful natural language processing capabilities in medical and clinical tasks. Evaluating LLM performance is crucial due to potential false results. In this study, we assessed ChatGPT and Llama2, two state-of-the-art LLMs, in extracting information from clinical notes, focusing on cognitive tests, specifically the Mini Mental State Exam (MMSE) and Cognitive Dementia Rating (CDR).
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