Patient characteristics, treatment and survival in pulmonary carcinoid tumours: an analysis from the UK National Lung Cancer Audit

Abstract

Objectives

Pulmonary carcinoid (PC) is a rare tumour with good prognosis following surgical resection. However, little is known regarding patient characteristics and use of other treatments modalities. Our objective was to review patient characteristics, treatment and survival for patients with PC and contrast these results with other forms of non-small cell lung cancer (NSCLC).

Setting

Audit data from UK National Lung Cancer Audit (NLCA) 2008–2013.

Participants

184 906 lung cancer cases were submitted to the NLCA.

Outcome measures

Primary outcome—survival rates between PC and NSCLC. Secondary outcome—differences in performance status, lung function and treatment modality between PC and NSCLC.

Results

PC histology was recorded in 1341 (0.73%) patients and non-carcinoid NSCLC histology in 162 959 (87.4%) cases. 91% of patients with PC had good performance status (Eastern Cooperative Oncology Group (ECOG) 0–1), compared with only 53% of NSCLC. 66% of PC had localised disease. Of all PC cases, 77% were treated with surgery, 6.2% received chemotherapy and 3.6% received radiotherapy, with the remainder treated with best supportive care. Overall 1-year and 3-year survival rates for PC were 92% and 84.7%, respectively. In contrast, 1-year and 3-year survival rates for NSCLC were 36.2% and 15.6%, However, 3-year survival for PC markedly decreased with worsening performance status and advanced disease to 23.8% for performance status ECOG 3–4 and 33.6% for stage IV disease.

Conclusions

In contrast to other forms of NSCLC, the majority of patients with PC present with good performance status, preserved lung function and early stage disease amenable to surgical resection. However, 1 in 5 patients with PC has metastatic disease which is associated with poor prognosis, as is poor performance status at presentation. We believe these data will help clinicians provide accurate prognostic predictions stratified according to patient characteristics at presentation, as well as guide future clinical trials.