[Advances on Postoperative Recurrence Risk Prediction in Non-small Cell Lung Cancer]

Lung cancer remains the malignant tumor with the highest incidence and mortality rates worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases. Radical surgical resection serves as the primary curative modality for patients with resectable NSCLC; however, postoperative recurrence persists in a subset of patients, thereby compromising long-term survival outcomes. Consequently, the precise prediction of postoperative recurrence risk, identification of high-risk populations, and formulation of individualized adjuvant therapy and surveillance strategies are of paramount importance for improving patient prognosis. In recent years, fueled by the rapid evolution of artificial intelligence (AI) and liquid biopsy technologies, the prediction of postoperative recurrence in NSCLC has progressively transitioned toward greater precision and personalization. This review summarizes the current applications of traditional predictive models, AI-based models, and liquid biopsy techniques in forecasting NSCLC recurrence following surgery, evaluating their respective strengths and limitations. Furthermore, it provides insights into future research trajectories.
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