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

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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Bibliographic Details
Main Authors: Francesco Martino, Silvia Varricchio, Daniela Russo, Francesco Merolla, Gennaro Ilardi, Massimo Mascolo, Giovanni Orabona dell’Aversana, Luigi Califano, Guglielmo Toscano, Giuseppe De Pietro, Maria Frucci, Nadia Brancati, Filippo Fraggetta, Stefania Staibano
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Cancers
Subjects:
Ki67
digital pathology
machine learning
Online Access:https://www.mdpi.com/2072-6694/12/5/1344

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