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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Autores principales:	Amal Joseph Varghese, Varsha Gouthamchand, Balu Krishna Sasidharan, Leonard Wee, Sharief K Sidhique, Julia Priyadarshini Rao, Andre Dekker, Frank Hoebers, Devadhas Devakumar, Aparna Irodi, Timothy Peace Balasingh, Henry Finlay Godson, T Joel, Manu Mathew, Rajesh Gunasingam Isiah, Simon Pradeep Pavamani, Hannah Mary T Thomas
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2023-04-01
Colección:	Physics and Imaging in Radiation Oncology
Materias:	Head-and-neck cancer Radiomics Loco-regional recurrence Multi-institutional Prognosis Machine learning
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S2405631623000416
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