A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [(18)F]FDG-PET/CT parameters

BACKGROUND: In patients with non-small cell lung cancer (NSCLC), accuracy of [(18)F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image...

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Detalles Bibliográficos
Autores principales: Rogasch, Julian M. M., Michaels, Liza, Baumgärtner, Georg L., Frost, Nikolaj, Rückert, Jens-Carsten, Neudecker, Jens, Ochsenreither, Sebastian, Gerhold, Manuela, Schmidt, Bernd, Schneider, Paul, Amthauer, Holger, Furth, Christian, Penzkofer, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199849/
https://www.ncbi.nlm.nih.gov/pubmed/36820890
http://dx.doi.org/10.1007/s00259-023-06145-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199849/
https://www.ncbi.nlm.nih.gov/pubmed/36820890
http://dx.doi.org/10.1007/s00259-023-06145-z

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