فهرست نشریات علمی دسترسی آزاد ایران

Background and Objectives: Non-small cell lung cancer (NSCLC) is the main type of lung cancer, with postoperative recurrence limiting long-term survival. Traditional staging systems (e.g., TNM) focus on tumor size, lymph node involvement, and metastasis but ignore respiratory function and detailed pathological features (e.g., differentiation), leading to insufficient prediction accuracy of individual recurrence risk, necessitating integrated models. Objective: To construct and validate a Nomogram integrating clinical risk factors, pathological parameters, and respiratory function (FEV1/FVC ratio) for predicting NSCLC postoperative recurrence, aiming to improve accuracy vs. traditional staging and highlight respiratory function's value.    Methods: A retrospective analysis of 244 NSCLC surgical patients (2021-2023) was conducted, with random division into training (n=157) and validation (n=87) sets (7:3). Independent risk factors were identified via multivariate Logistic regression. The Nomogram was evaluated using ROC (AUC), calibration curves (Hosmer-Lemeshow test), and DCA.    Results: No significant differences in recurrence rate or clinical data between the two sets (all P>0.05). Seven independent risk factors were identified: TNM stage Ⅲ-Ⅳ, mediastinal lymph node metastasis, adenocarcinoma, poor differentiation, ≥3 lymph node metastases, large tumor size, and decreased FEV1/FVC (all P<0.05). The Nomogram performed excellently: C-index 0.884 (training) and 0.913 (validation); good calibration (Hosmer-Lemeshow: P=0.138, 0.051); AUC 0.881 (95%CI:0.808-0.955) and 0.907 (95%CI:0.724-1.000).    Conclusion: The Nomogram, integrating clinical, pathological, and respiratory factors, shows high accuracy in predicting NSCLC postoperative recurrence, outperforming traditional staging. It aids personalized treatment but requires multi-center prospective validation with postoperative therapeutic and molecular factors.