Approximately 30% of early-stage lung cancer patients experience recurrence after curative surgery. However, the clinical utility of routine chest CT surveillance remains limited. Emerging evidence has demonstrated that liquid biopsy-based minimal residual disease (MRD) detection may serve as a more sensitive monitoring strategy. Longitudinal ctDNA dynamics analysis further enables real-time assessment of tumor progression and early detection of molecular relapse.
This study aims to develop a novel multidimensional approach for non-invasive postoperative recurrence monitoring in lung cancer and establish an MRD-guided adjuvant therapy model to optimize precision treatment and MRD-stratified follow-up protocols for lung cancer patients. This study will evaluate the sensitivity and specificity of novel monitoring methods and further compare emerging non-invasive recurrence monitoring approaches with conventional ctDNA-based techniques, driving continuous advancements in lung cancer research.
