用AI从常规病理切片中预测乳腺癌分子特征，省去昂贵检测
Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review
- 基于AI分析H&E染色切片，提取基因组到代谢组的多类生物标志物
- 可实现无需分子检测的精准治疗决策支持，降低临床成本
- 适合病理、AI与肿瘤临床研究者关注，推动个性化医疗落地
精准医学已成为乳腺癌管理的核心，已超越传统方法，实现更精准的个体化治疗。以往组织病理图像主要用于诊断，现被发现具有分子分型潜力，可深入揭示癌症预后和治疗反应。人工智能（AI）的进步使数字病理学能够从组织学图像中分析靶向分子及广泛的组学生物标志物，标志着个性化癌症护理的重要一步。这些技术可直接从常规苏木精-伊红（H&E）染色图像中提取基因组、转录组、蛋白质组和代谢组等生物标志物，支持治疗决策而无需昂贵的分子检测。本文综述了驱动生物标志物检测的AI方法，重点聚焦于多种组学生物标志物以实现新型标志物发现，并分析该领域在算法稳健开发中面临的主要挑战。这些挑战凸显了未来研究的关键方向，以弥合AI研究与临床应用之间的差距。
原文摘要 · Abstract (English)
Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for diagnostic purposes; however, they are now recognized for their potential in molecular profiling, which provides deeper insights into cancer prognosis and treatment response. Recent advancements in artificial intelligence (AI) have enabled digital pathology to analyze histopathologic images for both targeted molecular and broader omic biomarkers, marking a pivotal step in personalized cancer care. These technologies offer the capability to extract various biomarkers such as genomic, transcriptomic, proteomic, and metabolomic markers directly from the routine hematoxylin and eosin (H&E) stained images, which can support treatment decisions without the need for costly molecular assays. In this work, we provide a comprehensive review of AI-driven techniques for biomarker detection, with a focus on diverse omic biomarkers that allow novel biomarker discovery. Additionally, we analyze the major challenges faced in this field for robust algorithm development. These challenges highlight areas where further research is essential to bridge the gap between AI research and clinical application.
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