Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization

BACKGROUND AND PURPOSE: Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulat...

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Detalles Bibliográficos
Autores principales: Varghese, Amal Joseph, Gouthamchand, Varsha, Sasidharan, Balu Krishna, Wee, Leonard, Sidhique, Sharief K, Rao, Julia Priyadarshini, Dekker, Andre, Hoebers, Frank, Devakumar, Devadhas, Irodi, Aparna, Balasingh, Timothy Peace, Godson, Henry Finlay, Joel, T, Mathew, Manu, Gunasingam Isiah, Rajesh, Pavamani, Simon Pradeep, Thomas, Hannah Mary T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227455/
https://www.ncbi.nlm.nih.gov/pubmed/37260438
http://dx.doi.org/10.1016/j.phro.2023.100450

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227455/
https://www.ncbi.nlm.nih.gov/pubmed/37260438
http://dx.doi.org/10.1016/j.phro.2023.100450

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