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1000 Titel
  • Personalized prediction of mode of cardiac death in heart failure using supervised machine learning in the context of cardiac innervation imaging
1000 Autor/in
  1. Werner, Rudolf A. |
  2. Derlin, Thorsten |
  3. Bengel, Frank M. |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-06-17
1000 Erschienen in
1000 Quellenangabe
  • 29(1):202-203
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s12350-020-02215-z |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873136/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Growth of molecular imaging bears potential to transform nuclear cardiology from a primarily diagnostic method to a precision medicine tool. Molecular targets amenable for imaging and therapeutic intervention are particularly promising to facilitate risk stratification, patient selection and exquisite guidance of novel therapies, and interrogation of systems-based interorgan communication. Non-invasive visualization of pathobiology provides valuable insights into the progression of disease and response to treatment. Specifically, inflammation, fibrosis, and neurohormonal signaling, central to the progression of cardiovascular disease and emerging therapeutic strategies, have been investigated by molecular imaging. As the number of radioligands grows, careful investigation of the binding properties and added-value of imaging should be prioritized to identify high-potential probes and facilitate translation to clinical applications. In this review, we discuss the current state of molecular imaging in cardiovascular medicine, and the challenges and opportunities ahead for cardiovascular molecular imaging to navigate the path from diagnosis to prognosis to personalized medicine.
1000 Sacherschließung
lokal Nuclear Medicine
lokal Death [MeSH]
lokal Heart Failure/diagnostic imaging [MeSH]
lokal Humans [MeSH]
lokal Cardiology
lokal Imaging / Radiology
lokal Supervised Machine Learning [MeSH]
lokal Editorial
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/V2VybmVyLCBSdWRvbGYgQS4=|https://frl.publisso.de/adhoc/uri/RGVybGluLCBUaG9yc3Rlbg==|https://frl.publisso.de/adhoc/uri/QmVuZ2VsLCBGcmFuayBNLg==
1000 Hinweis
  • DeepGreen-ID: 5466f6cbd778475680d7d8176c8740f3 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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1000 Erstellt am 2023-11-18T16:50:48.917+0100
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