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Pathologist-Artificial Intelligence Concordance in HER2 Interpretation for Advanced Biliary Tract Cancer:

Hyunchul Kim1, Jinhyung Heo2, Soo Ick Cho3

  • 1Department of Pathology, CHA Ilsan Medical Center, CHA University School of Medicine, Goyang-si, Republic of Korea.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|November 13, 2025
PubMed
Summary

This study shows artificial intelligence (AI) pathology improves HER2 scoring consistency in biliary tract cancer (BTC). AI pathology achieved high concordance with expert pathologists, aiding objective HER2 assessments for targeted therapies.

Keywords:
HER2artificial intelligencebiliary tract cancerdigital pathologyimmunohistochemistry

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Area of Science:

  • Oncology
  • Pathology
  • Medical Imaging

Background:

  • Biliary tract cancer (BTC) is an aggressive malignancy with limited treatment options.
  • HER2 overexpression occurs in a subset of BTC patients, making it a target for therapy.
  • Accurate HER2 assessment in BTC is challenging, particularly for low expression levels.

Purpose of the Study:

  • To compare HER2 interpretations in advanced BTC by pathologists using light microscopy (LM) and digital pathology (DP).
  • To evaluate the potential of artificial intelligence (AI)-powered pathology in enhancing HER2 scoring consistency.
  • To quantify intraobserver and interobserver variability in HER2 evaluation for BTC.

Main Methods:

  • 309 HER2 immunohistochemistry slides from advanced BTC patients were analyzed.
  • Three pathologists independently evaluated HER2 expression twice using LM and DP.
  • An AI whole slide image analyzer assessed HER2 expression, with ground truth determined by consensus.

Main Results:

  • Pathologists achieved complete agreement in 62.1% (LM) and 63.4% (DP) of cases.
  • AI showed an 83.5% concordance rate with the ground truth for HER2 categories.
  • Clinical factors influenced pathologist variability, while histologic grade affected AI performance.

Conclusions:

  • AI-powered HER2 scoring demonstrates high concordance with pathologist evaluations in BTC.
  • AI pathology is expected to assist pathologists in achieving more objective HER2 assessments.
  • Improved HER2 scoring consistency can enhance therapeutic decisions for BTC patients.