Di-san junyi daxue xuebao (Jun 2019)

Clinical characteristics of pulmonary large cell neuroendocrine carcinoma and a nomogram model for predicting its prognosis

  • YANG Qiao,
  • XU Zihan,
  • ZHENG Linpeng,
  • CHEN Mingjing,
  • YU Yongxin,
  • SUN Jianguo

DOI
https://doi.org/10.16016/j.1000-5404.201812070
Journal volume & issue
Vol. 41, no. 12
pp. 1156 – 1160

Abstract

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Objective To analyze the clinical characteristics and survival of patients with pulmonary large cell neuroendocrine carcinoma (PLCNEC) and construct a nomogram model for predicting its prognosis. Methods We searched the Surveillance, Epidemiology, and End Results (SEER) database to collect the data of patients diagnosed with PLCNEC between 2004 and 2013. Kaplan-Meier method was used for survival analysis of the patients. A Cox proportional hazard model was used to identify the factors affecting the overall survival (OS), and the significant factors were used to construct a nomogram model for predicting the patients' 1-year survival probability. The discrimination power of this model was assessed based on the C-index, and the calibration was evaluated with a bootstrap procedure. Results A total of 1 656 cases of PLCNEC with complete record of the clinical characteristics were collected. Of these patients, 55.37% were male, 52.05% were above 65 years of age at diagnosis, and 84.12% were white; 51.93% of the patients received chemotherapy, 40.94% had surgeries, and 37.80% had radiotherapy. Kaplan-Meier survival analysis showed that the median OS of patients with PLCNEC was 11 months, and their 1-year survival rate was 46.5%. Multivariate analysis identified a female gender, black race, surgery, radiotherapy and chemotherapy as the protective factors, while an age ≥65 years, an increased tumor size, invasion of adjacent tissues, lymph node involvement and distant metastasis were the risk factors for poor prognosis; the primary tumor sites did not significantly affect the survival outcomes of the patients. We established a nomogram model for predicting the 1-year survival probability of the patients based on the significant factors identified in the multivariate analysis. The C-index of this prediction model was 0.76, and assessment of calibration showed that the 1-year survival rate predicted by this model was highly consistent with the observed 1-year survival rate of the patients. Conclusion According to the data from SEER, patients with PLCNEC have a median OS of 11 months and a 1-year survival rate of 46.5%. Gender, age, race, tumor size, tumor growth and metastasis and interventions are all associated with the OS of the patients. The nomogram model we established based on the clinical characteristics of PLCNEC is capable of predicting the 1-year survival probability of the patients.

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