A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, pred...

Full description

Bibliographic Details
Main Authors:	Raffaella Massafra, Maria Colomba Comes, Samantha Bove, Vittorio Didonna, Sergio Diotaiuti, Francesco Giotta, Agnese Latorre, Daniele La Forgia, Annalisa Nardone, Domenico Pomarico, Cosmo Maurizio Ressa, Alessandro Rizzo, Pasquale Tamborra, Alfredo Zito, Vito Lorusso, Annarita Fanizzi
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2022-01-01
Series:	PLoS ONE
Online Access:	https://doi.org/10.1371/journal.pone.0274691

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification by Raffaella Massafra, Maria Colomba Comes, Samantha Bove, Vittorio Didonna, Sergio Diotaiuti, Francesco Giotta, Agnese Latorre, Daniele La Forgia, Annalisa Nardone, Domenico Pomarico, Cosmo Maurizio Ressa, Alessandro Rizzo, Pasquale Tamborra, Alfredo Zito, Vito Lorusso, Annarita Fanizzi

Published 2022-01-01

Get full text

Article

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

Similar Items