Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma

OBJECTIVES: Relapse occurs in ~20% of patients with classical Hodgkin lymphoma (cHL) despite treatment adaption based on 2-deoxy-2-[(18)F]fluoro-d-glucose positron emission tomography/computed tomography response. The objective was to evaluate pre-treatment FDG PET/CT–derived machine learning (ML) m...

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Detalles Bibliográficos
Autores principales: Frood, Russell, Clark, Matt, Burton, Cathy, Tsoumpas, Charalampos, Frangi, Alejandro F., Gleeson, Fergus, Patel, Chirag, Scarsbrook, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403224/
https://www.ncbi.nlm.nih.gov/pubmed/36006428
http://dx.doi.org/10.1007/s00330-022-09039-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403224/
https://www.ncbi.nlm.nih.gov/pubmed/36006428
http://dx.doi.org/10.1007/s00330-022-09039-0

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