Zhonghua Yi Xue Za Zhi. 2025 Dec 2;105(44):4013-4018. doi: 10.3760/cma.j.cn112137-20250602-01355.

ABSTRACT

Conventional radiomics approaches are constrained by suboptimal diagnostic performance, limited capacity to characterize molecular heterogeneity within the tumor microenvironment, and insufficient capture of critical biological information regarding host immune responses. In response, AI-augmented multi-omics analytics have emerged as a core component throughout the management of lung cancer and pulmonary nodules, decoding intricate tumor biological landscapes to deliver novel dimensions for precision diagnosis and treatment, thereby establishing a foundational component of their comprehensive disease management. This cross-dimensional data synthesis not only transcends the informational limitations inherent in unimodal methodologies but also enables integrative profiling spanning anatomical architecture to molecular mechanisms, thereby substantially propelling the advancement of precision diagnosis and treatment of lung cancer and pulmonary nodules. Nevertheless, persistent challenges including inadequate data standardization and limited model interpretability remain to be addressed. The translational pathway is further impeded by delayed clinical validation and technical complexities in multi-omics data integration. Future research endeavors should prioritize the implementation of prospective trials and the development of novel AI technologies to overcome these obstacles, ultimately enabling early detection and personalized therapeutic interventions for lung cancer and pulmonary nodules.

PMID:41320656 | DOI:10.3760/cma.j.cn112137-20250602-01355