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Erschienen in:
24.10.2018 | Health Services Research and Global Oncology
A Novel Nomogram and Risk Classification System Predicting the Cancer-Specific Survival of Patients with Initially Diagnosed Metastatic Esophageal Cancer: A SEER-Based Study
Erschienen in: Annals of Surgical Oncology | Ausgabe 2/2019
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Background
Metastatic esophageal cancer (mEC) is the end stage of esophageal cancer. We aimed to construct a predictive model predicting the cancer-specific survival (CSS) of mEC patients.
Methods
Data from 1917 patients with initially diagnosed mEC were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Patients were randomly divided into the training and validation cohorts (7:3). Cox regression was conducted to select the predictors of CSS. The validation of the nomogram was performed using concordance index (C-index), calibration curves, and decision curve analyses (DCAs).
Results
Cancer-specific death occurred in 1559/1917 (81.3%) cases. Multivariate Cox regression indicated that factors including age, sex, grade at diagnosis, number of metastatic organs at diagnosis, pathological type, local treatment, and chemotherapy were independent predictors of CSS. Based on these factors, a predictive model was built and virtualized by nomogram. The C-index of the nomogram was 0.762. The calibration curves showed good consistency of CSS between the actual observation and the nomogram prediction, and the DCA showed great clinical usefulness of the nomogram. A risk classification system was built that could perfectly classify mEC patients into three risk groups. In the total cohort, the median CSS of patients in the low-, intermediate- and high-risk groups was 11.0 months (95% confidence interval [CI] 10.1–11.9), 8.0 months (95% CI 7.3–8.7), and 2.0 months (95% CI 1.8–2.2), respectively.
Conclusions
We constructed a nomogram and a corresponding risk classification system predicting the CSS of patients with initially diagnosed mEC. These tools can assist in patient counseling and guiding treatment decision making.