Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer

Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer

The tumor suppressor TP53 is frequently mutated in hormone receptor-negative, HER2-positive breast cancer (BC), contributing to tumor aggressiveness. Traditional ancillary methods like immunohistochemistry (IHC) to assess TP53 functionality face pre- and post-analytical challenges. This proof-of-con...

Full description

Saved in:
Bibliographic Details
Main Authors: Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Breast cancer
Artificial intelligence
Deep learning
TP53
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037024004057
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items