Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and va...

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Detalles Bibliográficos
Autores principales: Wichtmann, Barbara D., Albert, Steffen, Zhao, Wenzhao, Maurer, Angelika, Rödel, Claus, Hofheinz, Ralf-Dieter, Hesser, Jürgen, Zöllner, Frank G., Attenberger, Ulrike I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317842/
https://www.ncbi.nlm.nih.gov/pubmed/35885506
http://dx.doi.org/10.3390/diagnostics12071601

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317842/
https://www.ncbi.nlm.nih.gov/pubmed/35885506
http://dx.doi.org/10.3390/diagnostics12071601

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