CareLit · DOI-Nachweis · Nuklearmedizin - NuclearMedicine
Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)
DOI 10.1055/a-2198-0545
Bibliografische Angaben
Nachweis in CareLit
Autor:innenJulian Manuel Michael Rogasch, Kuangyu Shi, David Kersting, Robert Seifert
ZeitschriftNuklearmedizin - NuclearMedicine
Ausgabe06/2023
Jahrgang / SeitenJg. 62 · S. 361-369
Kernaussage aus der Literatur
Abstract-Vorschau
Abstract Aim Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. Methods A systematic search for original articles was r…
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Zitierempfehlung
Julian Manuel Michael Rogasch, Kuangyu Shi, David Kersting, Robert Seifert (2023) Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin - NuclearMedicine, 62 (06), 361-369. https://doi.org/10.1055/a-2198-0545
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