Clinicopathological features and prognostic nomogram of giant cell carcinoma of the lung: A population‐based study

Abstract

Background: Due to its rarity, the features and prognosis of giant cell carcinoma of the lung (GCCL) are not well defined. The present study aimed to describe the clinicopathological features and prognostic analysis of this rare disease, compare it with lung adenocarcinoma (LAC), further determine the prognostic factors and establish a nomogram. Methods: Patients diagnosed with GCCL and LAC were identified from the SEER database between 2004 and 2016. The features and survival between GCCL and LAC were compared in the unmatched and matched cohorts after propensity score matching (PSM) analysis. Univariate and multivariate Cox analyses were used to identify the prognostic factors, and a nomogram was constructed. Area under the curve (AUC), C-index, calibration curve and decision curve analysis (DCA) were used to confirm the established nomogram. Results: A total of 295 patient diagnosed with GCCL and 149 082 patients with LAC were identified. Compared with LAC, patients with GCCL tend to be younger, male, black and have pathological Grade III/IV GCCL, more proportion of AJCC-TNM-IV, T3/T4 and distant metastases. The 1-, 2-and 5-year OS rates of the patients with GCCL were 21.7%, 13.4% and 7.9%, respectively. The median OS and CSS were 3 and 4 months, respectively. Patients with GCCL had significantly shorter OS and CSS than those with LAC in the unmatched and matched cohorts after PSM. Multivariate Cox analysis demonstrated that T, N and M stages and use of chemotherapy and surgery were independent of survival. Furthermore, we constructed a prognostic nomogram for OS and CSS by using independent prognostic factors. The C-index of OSspecific nomogram is 0.78 (0.74-0.81), and the C-index of CSS-specific nomogram is 0.77 (0.73-0.80). The calibration curve and ROC analysis showed good predictive capability of these nomograms. DCA showed that the nomogram had greater clinical practical value in predicting the OS and CSS of GCCL than TNM staging.Jiang He and Jin-Ping Ni contributed equally to this work.