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 tiss...

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Publicado en:	Cancers Vol. 12; no. 5; pp. 1344 - 1345
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:	pictorial research tables/charts Journal Article
Publicado:	MDPI May2020
Acceso en línea:	Ver este registro en EBSCOhost