Deep learning predicts subtype heterogeneity and outcomes in luminal A breast cancer using routinely stained whole-slide images Journal Article
| Authors: | Kurian, N. C.; Gann, P. H.; Kumar, N.; McGregor, S. M.; Verma, R.; Sethi, A. |
| Article Title: | Deep learning predicts subtype heterogeneity and outcomes in luminal A breast cancer using routinely stained whole-slide images |
| Abstract: | SIGNIFICANCE: A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients. ©2024 The Authors; Published by the American Association for Cancer Research. |
| Keywords: | genetics; gene expression profiling; pathology; breast neoplasms; breast tumor; transcriptome; procedures; humans; prognosis; human; female; deep learning |
| Journal Title: | Cancer Research Communications |
| Volume: | 5 |
| Issue: | 1 |
| ISSN: | 2767-9764 |
| Publisher: | American Association for Cancer Research |
| Date Published: | 2025-01-01 |
| Start Page: | 157 |
| End Page: | 166 |
| Language: | English |
| DOI: | 10.1158/2767-9764.Crc-24-0397 |
| PUBMED: | 39740059 |
| PROVIDER: | scopus |
| PMCID: | PMC11770635 |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
