Development of Nomograms for Predicting Lymph Node Metastasis and Distant Metastasis in Newly Diagnosed T1-2 Non-Small Cell Lung Cancer: A Population-Based Analysis

Background For different lymph node metastasis (LNM) and distant metastasis (DM), the diagnosis, treatment and prognosis of T1-2 non-small cell lung cancer (NSCLC) are different. It is essential to figure out the risk factors and establish prediction models related to LNM and DM. Methods Based on the surveillance, epidemiology, and end results (SEER) database from 1973 to 2015, a total of 43,156 eligible T1-2 NSCLC patients were enrolled in the retrospective study. Logistic regression analysis was used to determine the risk factors of LNM and DM. Risk factors were applied to construct the nomograms of LNM and DM. The predictive nomograms were discriminated against and evaluated by Concordance index (C-index) and calibration plots, respectively. Decision curve analysis (DCAs) was accepted to measure the clinical application of the nomogram. Cumulative incidence function (CIF) was performed further to detect the prognostic role of LNM and DM in NSCLC-specific death (NCSD). Results Eight factors (age at diagnosis, race, sex, histology, T-stage, marital status, tumor size, and grade) were significant in predicting LNM and nine factors (race, sex, histology, T-stage, N-stage, marital status, tumor size, grade, and laterality) were important in predicting DM(all, P < 0.05). The calibration curves displayed that the prediction nomograms were effective and discriminative, of which the C-index were 0.723 and 0.808. The DCAs and clinical impact curves exhibited that the prediction nomograms were clinically effective. Conclusions The newly constructed nomograms can objectively and accurately predict LNM and DM in patients suffering from T1-2 NSCLC, which may help clinicians make individual clinical decisions before clinical management.

Automated summary

The newly constructed nomograms can accurately predict LNM and DM in patients suffering from T1-2 NSCLC, which may help clinicians make individual clinical decisions before clinical management.