Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data

PURPOSE: A three-dimensional deep generative adversarial network (GAN) was used to predict dose distributions for locally advanced head and neck cancer radiotherapy. Given the labor- and time-intensive nature of manual planning target volume (PTV) and organ-at-risk (OAR) segmentation, we investigate...

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Detalles Bibliográficos
Autores principales: Gu, Xiaojin, Strijbis, Victor I. J., Slotman, Ben J., Dahele, Max R., Verbakel, Wilko F. A. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565853/
https://www.ncbi.nlm.nih.gov/pubmed/37829347
http://dx.doi.org/10.3389/fonc.2023.1251132

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565853/
https://www.ncbi.nlm.nih.gov/pubmed/37829347
http://dx.doi.org/10.3389/fonc.2023.1251132

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