2026-10-12T21:22:40Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34021473 2026-09-29T05:46:22Z category_427 portal_316 item_type_3 month_year_09_2026

Table 2_Automatic grading of HER2 immunohistochemical whole-slide images based on a multi-scale hierarchical graph neural network.docx Yingjie Jiang (4473253) Wenhao Sun (3201390) Chao Ye (765248) Chun Xu (286394) Jiewen Xue (25135533) Yiliang Wang (1272954) Shiju Yan (25135536) Yiming Li (40290) Chenguang Bai (13951266) Public Health and Health Services not elsewhere classified artificial intelligence automated HER2 grading breast cancer graph neural network HER2 immunohistochemistry whole-slide imaging Objective<p>The HER2 immunohistochemical (IHC) score of breast cancer is a critical precursor for determining eligibility for anti-HER2 targeted therapy, as the final HER2 status (positive/negative/low) is determined by IHC results combined with FISH confirmation for equivocal 2 + cases according to ASCO/CAP guidelines. However, most existing artificial intelligence methods separate the detection of invasive cancer regions from HER2 grading and fail to adequately model the spatial heterogeneity of whole-slide tumors. This study proposes an end-to-end intelligent HER2 grading framework that integrates automated detection of invasive cancer regions with hierarchical graph reasoning, enabling automated and precise evaluation of HER2 IHC in breast cancer.</p>Method<p>A coarse-to-fine automated HER2 grading framework was developed. First, invasive tumor regions were automatically identified through supervised multi-scale tissue segmentation. Within the detected regions, patch-level HER2 grading integrates Delaunay cell image features with self-supervised visual representations, while WSI-level spatial representations model tumor heterogeneity to achieve fully automated HER2 grading across whole-slide images. The framework was trained using data from two scanning platforms and externally validated on 82 independent WSI samples from a third platform.</p>Results<p>In independent external validation, the Dice coefficient for invasive region detection reached 0.89 (on 50 WSI images); the accuracy rate of HER2 grading at the patch level was 96.49% (95% CI, 96.08–96.87%) (7,924 patches), while that at the WSI level reached 98.0% (95% CI, 91.5–99.9%) (82 WSI images). The quadratic-weighted Cohen’s κ was 0.97 (95% CI, 0.94–1.00), and all misclassifications at the WSI level were confined to the distinction between HER2 1 + and 2 +.</p>Conclusion<p>This framework enables fully automated HER2 IHC scoring and interpretation under multicenter and cross-scanner conditions, providing a computational tool for standardized IHC assessment, while final HER2 status determination for 2 + cases still requires FISH validation.</p> 2026-09-29T05:46:22Z Dataset Dataset 10.3389/fpubh.2026.1940487.s001 https://figshare.com/articles/dataset/Table_2_Automatic_grading_of_HER2_immunohistochemical_whole-slide_images_based_on_a_multi-scale_hierarchical_graph_neural_network_docx/34021473 CC BY 4.0