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. 2021 Oct 26;9(30):9011-9022.
doi: 10.12998/wjcc.v9.i30.9011.

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

Dong-Yun Zhang  1 , Gai-Rong Huang  2 , Jian-Wei Ku  3 , Xue-Ke Zhao  4 , Xin Song  4 , Rui-Hua Xu  4 , Wen-Li Han  4 , Fu-You Zhou  5 , Ran Wang  4 , Meng-Xia Wei  4 , Li-Dong Wang  6
Affiliations

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

Dong-Yun Zhang et al. World J Clin Cases. .

Abstract

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 7th tumor node metastasis (TNM) staging in the primary cohort [0.659 (95%CI: 0.607-0.712) vs 0.591 (95%CI: 0.517-0.666), P = 0.033] and in the validation cohort [0.700 (95%CI: 0.622-0.778) vs 0.605 (95%CI: 0.490-0.721), P = 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 7th 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.

Keywords: Decision curve analysis; Esophagus; Nomogram; Primary small cell carcinoma; Prognosis.

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Conflict of interest statement

Conflict-of-interest statement: We have no potential conflicts of interest to disclose.

Figures

Figure 1
Figure 1
Survival analysis in the primary cohort. A: Univariate analysis in the primary cohort; B: Multivariate analysis in the primary cohort. T: Tumor invasion depth; N: Number of positive lymph nodes; CD56: Neuronal cell adhesion molecule 56; CgA: Chromogranin A; Syn: Synaptophysin.
Figure 2
Figure 2
Nomogram model for predicting the 1-, 3-, and 5-year overall survival in primary small cell carcinoma of the esophagus patients. N: Number of positive lymph nodes; T: Tumor invasion depth; CD56: Neuronal cell adhesion molecule 56; CgA: Chromogranin A.
Figure 3
Figure 3
Calibration curves for predicting overall survival at (A) 1 year, (B) 3 years, and (C) 5 years in the primary cohort and at (D) 1 year, (E) 3 years, and (F) 5 years in the validation cohort. OS: Overall survival.
Figure 4
Figure 4
Decision curve analysis for the 18-mo survival predictions in primary small cell carcinoma of the esophagus patients. TNM: Tumor node metastasis.
Figure 5
Figure 5
Kaplan–Meier curves for all three groups based on the nomogram prediction. A: Kaplan–Meier curves for all three groups in the primary cohort. B: Kaplan–Meier curves for all three groups in the validation cohort.
Figure 6
Figure 6
Distribution of the nomogram predicted 5-year survival rate according to 7th edition tumor node metastasis stages. A: Distribution in the primary cohort; B: Distribution in the validation cohort. TNM: Tumor node metastasis.
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