Exploring the Use of Computer-Aided Imaging for HER2 Immunohistochemistry Scoring in Breast Cancer: A Single-Center Experience in a Developing Country
- Author:
Josh Matthew B. Chen
1
;
Steffanie Charlyne A. Tamayo
1
;
Manuelito A. Madrid
1
Author Information
- Publication Type:Other Types
- Keywords: Breast; HER2
- MeSH: Artificial Intelligence
- From: Philippine Journal of Pathology 2026;(75th PSP Research Competition Abstracts):1-
- CountryPhilippines
- Language:English
-
Abstract:
Background:Contemporary breast cancer treatment relies on precise diagnostic categorization
for tailored therapy. HER2 status, assessed using immunohistochemistry (IHC), is a key biomarker
guiding the choice for the antibody-drug conjugate trastuzumab deruxtecan, which is effective
even in HER2-low/ ultralow breast cancer (defined as HER2 IHC score 0+, 1+ or 2+ without ISH
amplification). While manual slide interpretation of IHC is subjective and prone to inter- and intraobserver variability, digitization potentially enables computer-assisted image analysis to improve
accuracy. However, implementation and infrastructure barriers persist, especially in low- to middleincome countries such as the Philippines.
Objective:This study evaluated the concordance between manual and AI-assisted HER2 IHC scoring at St. Luke’s Medical Center – Quezon City (SLMC QC) and identified areas of discrepancy and their possible causes.
Methodology:This retrospective study included all Invasive Breast Carcinoma, No Special Type (IBC, NST) tumors diagnosed on core needle biopsy and with HER2 IHC performed in SLMC QC from January 1 to December 31, 2024. Original HER2 IHC slides were independently scored by two breast pathology consultants; consensus results served as the ground truth. Slides were digitized using Microvisioneer, and three regions of interest (with ≥ 100 tumor cells) selected by a senior breast pathologist were analyzed using an AI-based image deconvolution algorithm to produce computer image-enhanced HER2 scores. After a minimum of two-week washout, the senior breast pathologist re-evaluated the digitized slides with AI-generated outputs to derive the Computer Image-Enhanced (AI-Assisted) Scores. Concordance between ground truth and AI scores was then assessed.
Results:A total of 161 HER2 IHC slides from patients with invasive breast carcinoma were evaluated. Manual and AI-assisted scoring showed an overall percent agreement of 65.22% and a Cohen’s kappa of 0.53, indicating moderate concordance. Weighted analysis demonstrated markedly higher alignment (95.10% agreement; kappa 0.85, almost perfect). Discrepancies were most common among manual scores of 0 to 2+, largely driven by background staining, whereas manual score 3+ classifications remained highly consistent with AI output.
Conclusion:The study supports the emerging role of AI as a valuable complementary tool in HER2 interpretation. With proper validation and integration, AI-assisted image analysis can strengthen standardization in HER2 assessment, improving precision in patient care especially in resourcelimited settings. - Full text:2026090215524004653Exploring the Use of Computer-Aided.pdf
