AI-enabled digital pathology and molecular testing

With the rapid evolution of precision oncology, particularly in non-small cell lung cancer (NSCLC), therapeutic decision-making is increasingly shaped by histologic subtype, driver alterations, immune biomarkers, and minimal residual disease (MRD). Under this paradigm, conventional pathology based solely on morphologic interpretation is no longer sufficient for modern clinical needs. The integration of artificial intelligence (AI) and digital pathology has transformed whole-slide imaging (WSI) from static glass slides into computable, sharable, and traceable data objects, enabling automated tumor region detection, histologic classification, tumor cell proportion estimation, PD-L1 quantification, tumor microenvironment analysis, and even prediction of potential molecular phenotypes. In parallel, molecular testing has expanded from a limited number of actionable genes to broad multigene panels, while liquid biopsy and circulating tumor DNA (ctDNA) provide complementary options for molecular profiling when tissue is limited. MRD monitoring further shifts lung cancer management from one-time pretreatment stratification toward dynamic peri-treatment risk assessment. This review systematically summarizes the roles of AI-assisted pathology interpretation, the integration of driver mutations with PD-L1, TMB, ctDNA and MRD, the coupling of digital pathology with molecular subtyping, the importance of data standardization in precision medicine, and the major barriers to clinical translation, including insufficient external validation, platform heterogeneity, limited interpretability, regulatory concerns, and fragmented workflows. We argue that the true value of AI-enabled digital pathology and molecular testing lies not merely in improving the accuracy or efficiency of individual diagnostic steps, but in establishing an intelligent companion diagnostic system spanning the entire continuum of lung cancer care. Such a system can continuously integrate pathology, molecular profiling, liquid biopsy, MRD surveillance, and clinical decision-making. Looking forward, the field is expected to evolve from single-task algorithms to multimodal foundation models, from static companion diagnostics to dynamic companion diagnostics, and from isolated laboratory tools to regionalized, platform-based intelligent ecosystems, ultimately promoting data-driven precision lung cancer care.

Key Words: lung cancer; digital pathology; artificial intelligence; molecular testing; companion diagnostics