肺癌仍然是全球癌症相关死亡的主要原因之一，其复杂性是由错综复杂的遗传、表观遗传和环境因素驱动的。尽管基因组学、转录组学和蛋白质组学取得了进展，但对肺癌表型多样性的了解仍然落后。下一代表型组学将高通量表型数据与多组学方法和人工智能 (AI) 等数字技术相结合，为揭示肺癌的复杂性提供了一种变革性策略。这种方法利用先进的成像、单细胞技术和人工智能来捕捉细胞、组织和整个生物体水平上的动态表型变化，并以在时间和空间背景下解析的方式。通过将高通量、空间和时间分辨的表型谱映射到分子改变上，下一代表型组学可以更深入地了解肿瘤微环境、癌症异质性以及药物疗效、安全性和耐药机制。此外，将表型数据与基因组和蛋白质组网络相结合，可以根据生物结构和功能来识别新型生物标志物和治疗靶点，从而促进肺癌治疗中的精准医学。这篇专家综述探讨并阐述了下一代表型组学的当前进展及其重新定义肺癌诊断、预后和治疗的潜力。它强调了人工智能和机器学习在分析复杂表型数据集、实现个性化治疗干预方面的新兴作用。最终，下一代表型组学有望弥合分子改变与临床和人群健康结果之间的差距，提供对肺癌生物学的全面了解，从而彻底改变其管理并提高患者生存率。

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.