Authors
Min-Hsiang Chang, Hsin-Hsiu Tsai, Chun-Jui Chien, Jian-Chiao Wang
Published in
Technology in cancer research & treatment. Volume 25. Pages 15330338261500841. Epub Oct 16, 2026.
Abstract
IntroductionAntibody-drug conjugates have expanded HER2-targeted therapy to HER2-low breast cancer, yet categorical immunohistochemistry scoring inadequately supports patient selection at expression boundaries where interobserver agreement is suboptimal.MethodWe propose that artificial intelligence-enabled continuous quantification addresses this gap by preserving expression gradients essential for treatment eligibility decisions while maintaining pathologist interpretability.ResultUsing a pilot dataset (n=66), we demonstrate that data-driven thresholds derived from continuous scores can support three clinically relevant decision points: anti-HER2 therapy eligibility, in situ hybridization triage, and HER2-low identification for ADC candidacy. Bootstrap validation and cross-validation analyses supported threshold robustness.ConclusionThis transition from categorical boundaries to quantitative spectra may contribute to precision in patient selection, pending validation with treatment outcome data.
PMID:
42887894
Bibliographic data and abstract were imported from PubMed on 17 Oct 2026.
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