Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer
This proof-of-concept study shows that H&E pre-operative biopsies from TNBC, by means of deep learning technology, contain valuable information having predictive value for the outcome of NAC resulting in an AUC value of 0.696 outperforming a predictive AUC value of 0.63 based on structured clinical data of histological tumor grade, TILs, and ki-67 known from the literature.
Authors
- Bernhard Sturm
- Peter Lock
- Willeke A.M. Blokx (ORCID: https://orcid.org/0000-0002-4647-8830)
- Jeroen van der Laak (ORCID: https://orcid.org/0000-0001-7982-0754)
- Dushyant Kumar (ORCID: https://orcid.org/0000-0003-4255-8283)
Institutions
- Radboud University Nijmegen (NL)
- Radboud University Medical Center (NL)
- Raytheon Technologies (Finland) (FI)
- University Medical Center Utrecht (NL)
- Royal Dutch Kentalis (NL)
- PathoFinder (Netherlands) (NL)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1016/j.jpi.2025.100448
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00