Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning
The results from this study suggest that gene expression levels of proliferation scores can be predicted directly from breast cancer morphology in WSIs using CNNs and that the predictions provide prognostic information that could be used in research as well as in the clinical setting.
Authors
- Johan Vallon‐Christersson (ORCID: https://orcid.org/0000-0002-2195-0385)
- Mattias Rantalainen (ORCID: https://orcid.org/0000-0002-7740-2986)
- Yinxi Wang (ORCID: https://orcid.org/0000-0002-1651-7763)
- Constance Boissin (ORCID: https://orcid.org/0000-0003-4026-3190)
- A. Ekholm
Institutions
- Karolinska University Hospital (SE)
- Lund University (SE)
- Karolinska Institutet (SE)
Publication Details
- Journal
- BMC Cancer
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1186/s12885-024-13248-9
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Swedish e-Science Research Centre
- VINNOVA
- Cancerfonden
- Lunds Universitet
- Karolinska Institutet
- Vetenskapsrådet