From Categorical to Continuous: A Symbiotic Human-AI Approach to HER2 Scoring in the Antibody-Drug Conjugates Era

The advent of antibody-drug conjugates (ADCs) has fundamentally transformed HER2 assessment in breast cancer, shifting therapeutic focus from gene amplification to protein expression levels. This paradigm exposes critical limitations in traditional categorical scoring: inability to preserve expression gradients, poor interobserver agreement at clinically relevant boundaries, and systematic discarding of information within borderline categories (HER2-low and HER2-ultra-low) on which ADC treatment decisions now depend. We developed a continuous HER2 scoring framework (c-score) calculated as the weighted average of categorical proportions, maintaining visual-cognitive correspondence with traditional assessment while enabling quantitative precision. In a proof-of-concept cohort (66 cases spanning all HER2 categories), c-score achieved robust discrimination across clinical decision tasks: unambiguous HER2 3+ identification (AUC=1.00), effective ISH triage (AUC=0.96), and accurate HER2-low detection (AUC=0.98). Critically, c-score revealed substantial heterogeneity within borderline categories, preserving gradient information that categorical boundaries necessarily discard. Multiple independent groups converging on continuous quantification through diverse methodologies suggest this evolution is neither speculative nor optional, but an inevitable response to contemporary therapeutic biology. Validation in outcome cohorts will determine whether preserved expression gradients improve patient selection for ADCs.