跨免疫染色和癌症类型的AI驱动免疫组织化学评估的泛化：一种通用免疫组织化学分析器
Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer
AI总结：
该研究开发了通用免疫组织化学（UIHC）分析器，利用多癌症队列训练数据，实现对不同肿瘤和免疫染色类型IHC图像的通用解释，在未见数据上表现优于传统单队列模型，并能定量评估特定基因突变下的蛋白表达，推动个性化医疗发展。
摘要：
尽管方法学取得了进步，免疫组织化学（IHC）仍然是靶向治疗中组织病理学和伴随诊断最常用的辅助检测手段。然而，客观的IHC评估面临挑战。人工智能（AI）已成为一种潜在的解决方案，但其开发需要对每种癌症和IHC类型进行大量训练，限制了通用性。我们开发了一种通用IHC（UIHC）分析器，这是一种用于解释IHC图像的AI模型，无论肿瘤或IHC类型如何均可适用，所使用的训练数据集来自用PD-L1和/或HER2染色的各种癌症。这一多队列训练模型在解释未见过的IHC时优于传统的单队列模型（Kappa分数0.578对比最高0.509），并在不同的阳性染色截断值下持续表现出卓越的性能。定性分析表明，UIHC能够根据表达水平有效地对图像块进行聚类。UIHC模型还能定量评估具有MET突变的c-MET表达，这代表了在个性化医疗和新型生物标志物不断积累的时代，AI应用的一项重大进展。
英文摘要：
Despite advancements in methodologies, immunohistochemistry (IHC) remains the most utilized ancillary test for histopathologic and companion diagnostics in targeted therapies. However, objective IHC assessment poses challenges. Artificial intelligence (AI) has emerged as a potential solution, yet its development requires extensive training for each cancer and IHC type, limiting versatility. We developed a Universal IHC (UIHC) analyzer, an AI model for interpreting IHC images regardless of tumor or IHC types, using training datasets from various cancers stained for PD-L1 and/or HER2. This multi-cohort trained model outperforms conventional single-cohort models in interpreting unseen IHCs (Kappa score 0.578 vs. up to 0.509) and consistently shows superior performance across different positive staining cutoff values. Qualitative analysis reveals that UIHC effectively clusters patches based on expression levels. The UIHC model also quantitatively assesses c-MET expression with MET mutations, representing a significant advancement in AI application in the era of personalized medicine and accumulating novel biomarkers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
