Advancing Ki67 hotspot detection in breast cancer: a comparative analysis of automated digital image analysis algorithms.

ABSTRACT:

Aim

Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment.

Methods

Tissue sections of 135 consecutive invasive breast carcinomas were immunohistochemically stained for Ki67. Two DIA algorithms, based on VDS and DL, automatically determined the Ki67 hotspot PI. For manual assessment; two independent observers detected hotspots and calculated scores using a validated scoring pro

SUBMITTER: Zwager MC 

PROVIDER: S-EPMC11649514 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature