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. 2018 Nov 21:10:6109-6120.
doi: 10.2147/CMAR.S186194. eCollection 2018.

Recurrence and prognostic model for identifying patients at risk for esophageal cancer after surgery

Dongni Chen  1 , Weidong Wang  1 , Youfang Chen  1 , Jia Hu  1 , Men Yang  2 , Junxian Mo  3 , Zhesheng Wen  1
Affiliations

Recurrence and prognostic model for identifying patients at risk for esophageal cancer after surgery

Dongni Chen et al. Cancer Manag Res. .

Abstract

Objective: The aim of this study was to construct a risk model to assess overall survival (OS) and disease-free survival (DFS) in patients with esophageal cancer (EC) after surgery.

Patients and methods: A total of 872 consecutive EC patients who had undergone surgery between February 2009 and October 2012 were retrospectively analyzed. The cutoff for risk value (RV) was inferred by receiver operating characteristic curves and the Youden index. A log-rank test was used to compare the survival curves, and a Cox regression analysis was performed to clarify the significant prognostic factors.

Results: The area under the curve was 0.688 for OS and 0.645 for DFS. The survival rates were 69.4% (259/373) and 39.1% (195/499), and the rates of recurrence were 19.2% (70/364) and 27.6% (132/479), respectively, for RV<0.218 and RV≥0.218 (c 2=78.83, P<0.001; c 2=9.07, P=0.003; respectively). A multivariate Cox regression analysis identified cases suffering from higher overall mortalities with RV≥0.218 compared to RV<0.218 (HR=1.45; 95% CI, 1.21-2.02; P=0.015); similar results were also found for DFS (HR=1.38; 95% CI, 1.03-1.86; P=0.033). Kaplan-Meier survival curves showed that cases with RV<0.218 had better OS and DFS than cases with RV≥0.218 (log rank = 75.80, P<0.001; log rank = 24.78, P<0.001).

Conclusion: This model could be applied to an integrated assessment of recurrence and prognostic risk after the surgical treatment of EC.

Keywords: esophageal cancer; model; prognosis; recurrence; surgery.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
RV bar plot for survival and dead. Notes: The median RV was 0.200 and 0.311 for survivals and deceased patients, respectively. Abbreviation: RV, risk value.
Figure 2
Figure 2
RV bar plot for free recurrence and recurrence. Notes: The median RV was 0.203 and 0.263 for patients with no relapse and with disease recurrence, respectively. Abbreviation: RV, risk value.
Figure 3
Figure 3
ROCs were employed to assess the RV discriminative performance of the prognosis. Notes: The AUC was 0.688 for OS (95% CI 0.653–0.723; P<0.001). Abbreviations: AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic; RV, risk value.
Figure 4
Figure 4
ROCs were employed to assess the RV discriminative performance of the prognosis. Notes: AUC was 0.645 for DFS (95% CI 0.582–0.708; P<0.001). Abbreviations: AUC, area under the curve; DFS, disease-free survival; ROC, receiver operating characteristic; RV, risk value.
Figure 5
Figure 5
OS in the cohort grouped by the cutoff of RV. Notes: Patients with RV<0.218 had better OS compared with RV≥0.218 (log rank =75.80, P<0.001). Abbreviations: OS, overall survival; RV, risk value.
Figure 6
Figure 6
DFS in the cohort grouped by the cutoff of RV. Notes: Patients with RV<0.218 had better DFS compared with RV≥0.218 (log rank =24.78, P<0.001). Abbreviations: DFS, disease-free survival; RV, risk value.
Figure 7
Figure 7
ROCs were employed to compare the performance in predicting OS and DFS between RV and TNM stage. Notes: (A) AUC was 0.544 (95% CI 0.506–0.582; P=0.025) for OS in TNM stage compared with RV (AUC =0.688). (B) AUC was 0.532 (95% CI 0.458–0.605; P=0.405) for DFS in TNM stage compared with RV (AUC =0.645). Abbreviations: AUC, area under the curve; DFS, disease-free survival; OS, overall survival; ROC, receiver operating characteristic curve; RV, risk value.
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