通过常规组织病理学进行乳腺癌分子分型的计算方法：综述
Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review
AI总结：
本综述系统梳理了AI驱动的乳腺癌组织病理学图像分子分型方法，涵盖多组学生物标志物提取，并分析了算法开发中的主要挑战及临床转化瓶颈。
摘要：
精准医学已成为乳腺癌管理的核心焦点，超越了传统方法，以提供更精确和个体化的治疗。传统上，组织病理学图像主要用于诊断目的；然而，现在人们认识到它们在分子分型方面的潜力，这为癌症预后和治疗反应提供了更深入的见解。人工智能（AI）的最新进展使数字病理学能够分析组织病理学图像，以检测靶向分子和更广泛的组学生物标志物，标志着个性化癌症护理的关键一步。这些技术提供了直接从常规苏木精-伊红（H&E）染色图像中提取各种生物标志物（如基因组、转录组、蛋白质组和代谢组标志物）的能力，这可以在无需昂贵分子检测的情况下支持治疗决策。在这项工作中，我们对AI驱动的生物标志物检测技术进行了全面综述，重点关注允许新型生物标志物发现的各种组学生物标志物。此外，我们分析了该领域在稳健算法开发方面面临的主要挑战。这些挑战突出了需要进一步研究的关键领域，以弥合AI研究与临床应用之间的差距。
英文摘要：
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.
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。
