Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers

作者：Junxiao Wang, Shiying Zhang, Jia Li, Mei Deng, Zhi Zeng, Zehua Dong, Fang-Fang Chen, Wenzhao Liu, Lianlian Wu, Honggang Yu · 发表于：BMC Gastroenterology · 年份：2025 · DOI：10.1186/s12876-025-04045-0 · 被引用次数：4 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、HER2/EGFR in Cancer Research

BACKGROUND: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it is time and tissue consuming, and highly dependent on skilled laboratory technicians. Recently, deep learning-based IHC biomarker prediction models have been widely developed, but few investigations have explored their clinical application effectiveness. METHODS: In this study, we aimed to create an automatic pipeline for the construction of deep learning models to generate AI-IHC (Artificial Intelligence) output using H&E whole slide images (WSIs) and compared the pathology reports by pathologists on AI-IHC versus conventional IHC. We obtained 134 WSIs including H&E and IHC pairs, and automatically extracted 415,463 tiles from H&E slides for model construction based on the annotation transfer from IHC slides. Five IHC biomarker prediction models (P40, Pan-CK, Desmin, P53, Ki-67) were developed to support a range of clinically relevant diagnostic applications across various gastrointestinal cancer subtypes, including esophageal, gastric, and colorectal cancers. The Ki-67 proliferation index was quantitatively assessed using digital image analysis. RESULTS: The AUCs of five IHC biomarker models ranged from 0.90 to 0.96 and the accuracies were between 83.04 and 90.81%. Additional 150 WSIs from 30 patients were collected to assess the effectiveness of AI-IHC through the multi-reader multi-case (MRMC) study. Each case was read by three pathologists, once on AI-IHC and once on conve...