SUMMARY
The objective of this study is to estimate the probability of cause-specific mortality using a competing-risks nomogram and recursive partitioning analysis in a large population-based cohort of patients with esophageal neuroendocrine carcinoma. The surveillance, epidemiology and end results database was used to identify 162 patients diagnosed with esophageal neuroendocrine carcinoma from 1998 to 2014. We estimated a cumulative incidence function for cause-specific mortality. A nomogram was constructed by using a proportional subdistribution hazard model, validated using bootstrap cross-validation, and evaluated with a decision curve analysis to assess its clinical utility. Finally, we performed risk stratification using a recursive partitioning analysis to divide patients with esophageal neuroendocrine carcinoma into clinically useful prognostic groups. Tumor location, distant metastasis, surgery, radiotherapy, and chemotherapy were significantly associated with cause-specific mortality. The calibration plots demonstrated good concordance between the predicted and actual outcomes. The discrimination performance of a Fine–Gray model was evaluated by using the c-index, which was 0.723 for cause-specific mortality. The decision curve analysis ranged from 0.268 to 0.968 for the threshold probability at which the risk model provided net clinical benefits relative to hypothetical all-screening and no-screening scenarios. The risk groups stratified by a recursive partitioning analysis allowed significant distinction between cumulative incidence curves. We determined the probability of cause-specific mortality in patients with esophageal neuroendocrine carcinoma and developed a nomogram and recursive partitioning analysis stratification system based on a competing-risks model. The nomogram and recursive partitioning analysis appear to be suitable for risk stratification of cause-specific mortality in patients with esophageal neuroendocrine carcinoma and will help clinicians to identify patients at increased risk of cause-specific mortality to guide treatment and surveillance decisions.
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