Cargando…
PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning
BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy...
Autores principales: | Aswolinskiy, Witali, Munari, Enrico, Horlings, Hugo M., Mulder, Lennart, Bogina, Giuseppe, Sanders, Joyce, Liu, Yat-Hee, van den Belt-Dusebout, Alexandra W., Tessier, Leslie, Balkenhol, Maschenka, Stegeman, Michelle, Hoven, Jeffrey, Wesseling, Jelle, van der Laak, Jeroen, Lips, Esther H., Ciompi, Francesco |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644597/ https://www.ncbi.nlm.nih.gov/pubmed/37957667 http://dx.doi.org/10.1186/s13058-023-01726-0 |
Ejemplares similares
-
Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks
por: Bándi, Péter, et al.
Publicado: (2019) -
RM-SORN: a reward-modulated self-organizing recurrent neural network
por: Aswolinskiy, Witali, et al.
Publicado: (2015) -
Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
por: Mercan, Caner, et al.
Publicado: (2022) -
Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics
por: Balkenhol, Maschenka CA., et al.
Publicado: (2021) -
Was the implementation strategy of the ProACT trial adequately proactive?
por: Mathioudakis, Alexander G., et al.
Publicado: (2019)