转化中的肺细胞学

摘要(中文译文)

传统上，肺细胞学一直用于诊断和筛查包括肺癌在内的多种呼吸系统疾病。与组织学诊断相比，其周转时间更短，因此治疗决策有时可能需要仅基于细胞学检查结果来做出。然而，在精准医学时代，生物标志物评估和分子检测已变得越来越重要，尤其是在肺癌的管理中。此外，病理切片的数字化促进了基于人工智能（AI）的诊断工具的发展。这些新兴技术在肺细胞学中的应用目前正在积极研究中，并持续发展，克服了肿瘤细胞输入量低和细胞学标本三维特性等挑战。本综述总结了肺细胞学的最新进展，重点关注免疫细胞化学（ICC）、蛋白质组学分析和AI辅助诊断。ICC不仅为亚分类提供关键信息，还为相关治疗性生物标志物的评估提供重要依据。蛋白质组学分析能够利用有限的细胞学材料同时全面表征数千种蛋白质表达谱。但其在常规诊断中的未来应用必须在临床试验中加以确立和验证。此外，数字化的细胞学切片可用于基于AI的分析，有望减少诊断工作量并提供额外的预测信息。尽管取得了这些进展，在标本制备、标准化和验证方面仍存在挑战。将形态学与经过适当验证的分子和计算方法相结合，有望改变肺细胞学的框架，将其作用从形态学诊断扩展到对肺部肿瘤进行全面且具有临床意义的表征。

Abstract

Traditionally, pulmonary cytology has been used to diagnose and screen for a variety of respiratory diseases, including lung cancer. Owing to its rapid turnaround time compared with histological diagnosis, therapeutic decisions may occasionally need to be made based on cytological findings alone. In the era of precision medicine, however, biomarker assessment and molecular testing have become increasingly important, particularly in the management of lung cancer. In addition, the digitalization of pathological slides has facilitated the development of artificial intelligence (AI)-based diagnostic tools. The utility of these emerging technologies in pulmonary cytology is currently being actively investigated and continues to evolve, overcoming challenges including low input of tumor cells and the three-dimensional nature of cytological specimens. This review summarizes recent advances in pulmonary cytology, focusing on immunocytochemistry (ICC), proteomic analysis, and AI-assisted diagnostics. ICC provides crucial information not only for subclassification but also for the assessment of relevant therapeutic biomarkers. Proteomic analysis enables comprehensive characterization of thousands of protein expression profiles simultaneously, using limited cytology materials. But its future use in routine diagnostics must be established and tested in clinical trials. Furthermore, digitalized cytological slides can be utilized for AI-based analyses, potentially reducing diagnostic workload and providing additional predictive information. Despite these advances, challenges remain regarding specimen preparation, standardization, and validation. The integration of morphology with appropriately validated molecular and computational methods has the potential to transform the framework of pulmonary cytology, extending its role from morphological diagnosis toward comprehensive and clinically significant characterization of pulmonary tumors.

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