Cancers, Vol. 12, Pages 1344: A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections
Cancers, Vol. 12, Pages 1344: A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections
Cancers doi: 10.3390/cancers12051344
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
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 tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as th...
Source: Cancers - Category: Cancer & Oncology 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 Tags: Article Source Type: research
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