Sep 2026· Frontiers in Public Health· 17 references
HER2/EGFR in Cancer Research
Abstract
Objective 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. Method 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. Results 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 +. Conclusion 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.
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.
Yaser Ghanam, F. Maurer, P. Abrahamsson· Information and Software Tec...· 41 citations· ⚡3
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· Scientometrics· 21 citations· ⚡1
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.· bioRxiv· 15 citations
Abstract Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been...
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka· Nature Computational Science· 13 citations
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.· Journal of Chemical Informat...· 13 citations· ⚡1
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