Development of a nomogram based on the clinicopathological and CT features to predict the survival of primary pulmonary lymphoepithelial carcinoma patients

The aim of this study was to develop a nomogram by combining chest computed tomography (CT) images and clinicopathological predictors to assess the survival outcomes of patients with primary pulmonary lymphoepithelial carcinoma (PLEC).

113 patients with stage I–IV primary PLEC who underwent treatment were retrospectively reviewed. The Cox regression analysis was performed to determine the independent prognostic factors associated with patient’s disease-free survival (DFS) and cancer-specific survival (CSS). Based on results from multivariate Cox regression analysis, the nomograms were constructed with pre-treatment CT features and clinicopathological information, which were then assessed with respect to calibration, discrimination and clinical usefulness.

Multivariate Cox regression analysis revealed the independent prognostic factors for DFS were surgery resection and hilar and/or mediastinal lymphadenopathy, and that for CSS were age, smoking status, surgery resection, tumor site in lobe and necrosis. The concordance index (C‑index) of nomogram for DFS and CSS were 0.777 (95% CI: 0.703–0.851) and 0.904 (95% CI: 0.847–0.961), respectively. The results of the time‑dependent C‑index were internally validated using a bootstrap resampling method for DFS and CSS also showed that the nomograms had a better discriminative ability.

We developed nomograms based on clinicopathological and CT factors showing a good performance in predicting individual DFS and CSS probability among primary PLEC patients. This prognostic tool may be valuable for clinicians to more accurately drive treatment decisions and individualized survival assessment.