32P Whole-slide histopathology embeddings enable non-genomic HER2 biomarker prediction in esophageal adenocarcinoma
Year: 2026
Type: article
Abstract: Human epidermal growth factor receptor 2 (HER2) status is a critical biomarker for treatment selection in esophageal adenocarcinoma, as HER2 amplification identifies patients who may benefit from targeted therapies. However, current methods for assessing HER2 such as immunohistochemistry and genomic testing are often limited by tissue availability, high costs, and logistical constraints. To overcome these challenges, we present a novel approach using Prov-GigaPath, a scalable, AI-driven histopathology model pretrained on over 1.3 billion image tiles.
Source: ESMO Open
Authors: K. Nijjer, A. Ahmed
Institutions: Stanford Medicine, Stanford University, York University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,910
OpenAlex ID: W7147514670
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