A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor...

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Detalles Bibliográficos
Autores principales: Martino, Francesco, Varricchio, Silvia, Russo, Daniela, Merolla, Francesco, Ilardi, Gennaro, Mascolo, Massimo, dell’Aversana, Giovanni Orabona, Califano, Luigi, Toscano, Guglielmo, De Pietro, Giuseppe, Frucci, Maria, Brancati, Nadia, Fraggetta, Filippo, Staibano, Stefania
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281627/
https://www.ncbi.nlm.nih.gov/pubmed/32466184
http://dx.doi.org/10.3390/cancers12051344

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281627/
https://www.ncbi.nlm.nih.gov/pubmed/32466184
http://dx.doi.org/10.3390/cancers12051344

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