Imaging features and clinical markers for predicting postoperative recurrence in early-stage lung cancer

This study aimed to evaluate the predictive value of imaging features and clinical markers for postoperative recurrence in patients with early-stage lung cancer and to establish a nomogram and a neural network model for prediction. A total of 439 patients with early-stage lung cancer who underwent surgical treatment at Changzhou First People's Hospital between January 2020 and January 2023 were retrospectively enrolled. Clinical characteristics, preoperative imaging findings, postoperative pathology, laboratory test results, and recurrence status were collected. By April 1, 2025, 85 of the 439 patients had relapsed, accounting for 19.36% of the cohort. Univariate analysis revealed significant differences between the recurrence and non-recurrence groups in terms of age, tumor density, CYFRA21-1, CA19-9, and CA125 (all <i>P</i><0.05). Multivariate logistic regression analysis identified solid tumor density (OR=2.132), CYFRA21-1≥3.3 ng/mL (OR=2.307), CA19-9≥37 U/mL (OR=2.901), and CA125≥35 U/mL (OR=5.974) as independent risk factors for postoperative recurrence. In addition, increasing age was associated with higher recurrence risk (OR=1.121) (all <i>P</i><0.05). The nomogram model based on these predictors demonstrated an area under the receiver operating characteristic curve (AUC) of 0.804 in the training set and 0.760 in the validation set, both exceeding 0.7, indicating good predictive performance. The neural network model yielded AUC values of 0.882 in the training set and 0.734 in the validation set, also showing favorable performance. DeLong test revealed a significant difference in AUC between the two models in the training set (Z=-3.514, <i>P</i><0.001), but no significant difference in the validation set (Z=0.374, <i>P</i>=0.709). External validation showed that the nomogram achieved a sensitivity of 74.36%, specificity of 73.84%, and accuracy of 73.93%, while the neural network model achieved a sensitivity of 79.49%, specificity of 68.60%, and accuracy of 70.62%. In conclusion, this study developed a nomogram and a neural network model incorporating imaging features and clinical markers to predict postoperative recurrence in early lung cancer. These models may serve as valuable tools to identify high-risk patients and guide individualized clinical management.