A validated nomogram integrating baseline peripheral T-lymphocyte subsets and NK cells for predicting survival in stage I–IIIA non-small cell lung cancer after resection

BACKGROUND: Accurately predicting the risk of recurrence in stage I–IIIA non-small cell lung cancer (NSCLC) after resection is critical in the treatment process. This study aimed to establish a novel nomogram to identify patients with a risk of disease progression in stage I–IIIA lung cancer based on clinical characteristics, peripheral T-lymphocyte subsets, and CD16+56 natural killer (NK) cells. METHODS: A total of 306 NSCLC patients from Shanghai Municipal Hospital of Traditional Chinese Medicine between 2010 and 2020 who met the inclusion and exclusion criteria between January 2011 and December 2020 were retrospectively reviewed. Patients were randomly assigned to the training cohort (206 patients) and the validation cohort (100 patients). A nomogram model was developed based on the results of multivariate Cox regression in the training cohort. The optimal cut-off values were determined by X-tile software. The bootstrap method was used to validate the nomogram. Receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC) were used to compare prognostic factors. The concordance index (C-index) was calculated to determine the accuracy of the nomogram in predicting disease-free survival (DFS). RESULTS: Gender, drinking history, TNM stage, and CD4(+)T/CD8(+)T were independent factors for DFS and were integrated into the model, while CD16+56 NK cells were not proven to be significant independent factors for DFS. The calibration curves for probability of 3- and 5-year DFS showed excellent agreement between predicted and actual survival. The C-index for the nomogram to predict DFS was 0.839 in the training cohort. The nomogram showed an excellent predictive performance in the training cohort (3-/5-year AUC: 0.860/0.847) and in the validation cohort (3-/5-year AUC: 0.726/0.748). CONCLUSIONS: We developed a prognostic model which provided individual prediction of DFS for stage I–IIIA NSCLC patients after resection. This practical prognostic tool may help oncologists in clinical treatment planning.