Development and validation of a prognostic nomogram model for Chinese patients with primary small cell carcinoma of the esophagus.

World J Clin Cases

State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China.

Published: October 2021

Background: Primary small cell carcinoma of the esophagus (PSCE) is a highly invasive malignant tumor with a poor prognosis compared with esophageal squamous cell carcinoma. Due to the limited samples size and the short follow-up time, there are few reports on elucidating the prognosis of PSCE, especially on the establishment and validation of a survival prediction nomogram model covering general information, pathological factors and specific biological proteins of PSCE patients.

Aim: To establish an effective nomogram to predict the overall survival (OS) probability for PSCE patients in China.

Methods: The nomogram was based on a retrospective study of 256 PSCE patients. Univariate analysis and multivariate Cox proportional hazards regression analysis were used to examine the prognostic factors associated with PSCE, and establish the model for predicting 1-, 3-, and 5-year OS based on the Akaike information criterion. Discrimination and validation were assessed by the concordance index (C-index) and calibration curve and decision curve analysis (DCA). Histology type, age, tumor invasion depth, lymph node invasion, detectable metastasis, chromogranin A, and neuronal cell adhesion molecule 56 were integrated into the model.

Results: The C-index was prognostically superior to the 7 tumor node metastasis (TNM) staging in the primary cohort [0.659 (95%CI: 0.607-0.712) 0.591 (95%CI: 0.517-0.666), = 0.033] and in the validation cohort [0.700 (95%CI: 0.622-0.778) 0.605 (95%CI: 0.490-0.721), = 0.041]. Good calibration curves were observed for the prediction probabilities of 1-, 3-, and 5-year OS in both cohorts. DCA analysis showed that our nomogram model had a higher overall net benefit compared to the 7 TNM staging .

Conclusion: Our nomogram can be used to predict the survival probability of PSCE patients, which can help clinicians to make individualized survival predictions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8567530PMC
http://dx.doi.org/10.12998/wjcc.v9.i30.9011DOI Listing

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