Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning.

ABSTRACT: Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody-drug conjugates (ADCs) like trastuzumab deruxtecan, which shows promise for breast cancers with low HER2 expression. Current HER2 testing methods, including immunohistochemistry (IHC) and in situ hybridization (ISH), have limitations. IHC, a semi-quantitative assay, is prone to interobserver variability. While ISH provides higher precision than IHC, it remains more resource-intensive in terms of cost and workflow. However, turnaround time is typically faster than that of other advanced molecular methods such as next-generation sequencing. We adapted the clustering-constrained-attention multiple-instance deep

SUBMITTER: Macaulay DO 

PROVIDER: S-EPMC11884934 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature