Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer

PURPOSE: Surgical oncologists are frequently confronted with the question of expected long-term prognosis. The aim of this study was to apply machine learning algorithms to optimize survival prediction after oncological resection of gastroesophageal cancers. METHODS: Eligible patients underwent onco...

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Detalles Bibliográficos
Autores principales: Jung, Jin-On, Crnovrsanin, Nerma, Wirsik, Naita Maren, Nienhüser, Henrik, Peters, Leila, Popp, Felix, Schulze, André, Wagner, Martin, Müller-Stich, Beat Peter, Büchler, Markus Wolfgang, Schmidt, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article – Clinical Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097798/
https://www.ncbi.nlm.nih.gov/pubmed/35616729
http://dx.doi.org/10.1007/s00432-022-04063-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097798/
https://www.ncbi.nlm.nih.gov/pubmed/35616729
http://dx.doi.org/10.1007/s00432-022-04063-5

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