Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides

I. Garberis (), V. Gaury, C. Saillard, D. Drubay, K. Elgui, B. Schmauch, A. Jaeger, L. Herpin, J. Linhart, M. Sapateiro, F. Bernigole, A. Kamoun, A. Filiot, O. Tchita, R. Dubois, M. Auffret, L. Guillou, I. Bousaid, M. Azoulay, J. Lemonnier, M. Sefta, S. Everhard, A. Sarrazin, Reboud J-F, F. Brulport, J. Dachary, B. Pistilli, S. Delaloge, P. Courtiol, F. André, V. Aubert and M. Lacroix-Triki
Additional contact information
I. Garberis: Paris-Saclay University
V. Gaury: Owkin
C. Saillard: Owkin
D. Drubay: Université Paris-Saclay
K. Elgui: Owkin
B. Schmauch: Owkin
A. Jaeger: Owkin
L. Herpin: Owkin
J. Linhart: Owkin
M. Sapateiro: Paris-Saclay University
F. Bernigole: Paris-Saclay University
A. Kamoun: Owkin
A. Filiot: Owkin
O. Tchita: Owkin
R. Dubois: Owkin
M. Auffret: Owkin
L. Guillou: Owkin
I. Bousaid: Gustave Roussy
M. Azoulay: Gustave Roussy
J. Lemonnier: Unicancer
M. Sefta: Owkin
S. Everhard: Unicancer
A. Sarrazin: Owkin
Reboud J-F: Owkin
F. Brulport: Owkin
J. Dachary: Owkin
B. Pistilli: Paris-Saclay University
S. Delaloge: Paris-Saclay University
P. Courtiol: Owkin
F. André: Paris-Saclay University
V. Aubert: Owkin
M. Lacroix-Triki: Paris-Saclay University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 − ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-60824-z Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60824-z

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-60824-z

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().