Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.

ABSTRACT:

Background

Accurate quantification of the Ki-67 proliferation index is essential for breast cancer prognosis and treatment planning. Current automated methods, including classical and deep learning approaches based on cell detection or segmentation, often face challenges due to densely packed nuclei, morphological variability, and inter-laboratory differences. Since Hematoxylin and Eosin (H&E) staining is routinely performed, accurately estimating Ki-67 from these slides could save resources by eliminating the need for additional immunohistochemical (IHC) staining. We developed and validated a transformer-based regression model to estimate Ki-67 expression directly from H&E-stained Whole Slide Images (WSIs).

Methods

We used seven public datasets to select optimal transformer

SUBMITTER: Chaurasia AK 

PROVIDER: S-EPMC12625586 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature