Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer

BACKGROUND AND PURPOSE: To compare the prediction performance of image features of computed tomography (CT) images extracted by radiomics, self-supervised learning and end-to-end deep learning for local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival...

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Detalles Bibliográficos
Autores principales: Ma, Baoqiang, Guo, Jiapan, Chu, Hung, van Dijk, Lisanne V., van Ooijen, Peter M.A., Langendijk, Johannes A., Both, Stefan, Sijtsema, Nanna M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663809/
https://www.ncbi.nlm.nih.gov/pubmed/38026084
http://dx.doi.org/10.1016/j.phro.2023.100502

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663809/
https://www.ncbi.nlm.nih.gov/pubmed/38026084
http://dx.doi.org/10.1016/j.phro.2023.100502

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