[Advances on Postoperative Recurrence Risk Prediction in Non-small Cell Lung Cancer].
- 1Provincial School of Clinical Medicine, Fujian Medical University; Department of Pulmonary and Critical Care Medicine, Provincial Hospital of Fuzhou University, Fuzhou 350001, China.
Abstract
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. .
Related
- Multi-Scale MRI, Radiology Reports, and Blood Biomarker–Guided Multimodal Deep Learning for Predicting Postoperative Recurrence in Cervical Cancer: A Multicenter Study
- Risk factors for postoperative recurrence of chronic subdural hematoma and construction and validation of an online dynamic nomogram prediction model.
- Molecular Mechanisms and Therapeutic Advances in KRAS G12C‐Mutant Non‐Small Cell Lung Cancer: Toward Mechanism‐Guided Longitudinal Management
- Skeletal muscle depletion during neoadjuvant chemoimmunotherapy predicts postoperative complications in non-small cell lung cancer: a retrospective cohort study
- Predictive value of preoperative [18F]FDG PET/CT imaging parameters for postoperative recurrence and metastasis in breast cancer patients
BibTeX
@article{zhang2026advances,
title = {{[Advances on Postoperative Recurrence Risk Prediction in Non-small Cell Lung Cancer].}},
author = {Shihang Zhang and Haitao Yang and Zhibin Huang and Baosong Xie},
journal = {Zhongguo fei ai za zhi = Chinese journal of lung cancer},
year = {2026},
month = aug,
doi = {10.3779/j.issn.1009-3419.2026.101.21},
url = {https://europepmc.org/article/MED/42896975},
note = {CC BY},
}
RIS
TY - JOUR
TI - [Advances on Postoperative Recurrence Risk Prediction in Non-small Cell Lung Cancer].
AU - Shihang Zhang
AU - Haitao Yang
AU - Zhibin Huang
AU - Baosong Xie
PY - 2026
DA - 2026/08/01
JO - Zhongguo fei ai za zhi = Chinese journal of lung cancer
DO - 10.3779/j.issn.1009-3419.2026.101.21
UR - https://europepmc.org/article/MED/42896975
AB - 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. .
SN - 1009-3419
SN - 1999-6187
ER -