Predictive Model for Overall Survival and Cancer-Specific Survival in Patients with Esophageal Adenocarcinoma.

Abstract

OBJECTIVE:

Recent years, there has been a rapid increase in the incidence of esophageal adenocarcinoma (EAC), while the prognosis for patients diagnosed remains poor and has slightly improved.

METHODS:

We extracted 6,466 cases with detailed demographical characteristics including age at diagnosis, sex, ethnicity, marital status, and clinical features, involving tumor grade and stage at diagnosis and treatment modalities (radiation therapy, chemotherapy, and surgery) from the Surveillance, Epidemiology, and End Results (SEER) (1975-2017) dataset. They were further randomly divided into the training and validating cohorts. Univariate and multivariate Cox analyses were conducted to determine significant variables for construction of nomogram. The predictive power of the model was then assessed by Harrell concordance index (C-index) and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

RESULTS:

Multivariate analysis revealed that age, marital status, insurance, tumor grade, TNM stage, surgery, and chemotherapy all showed a significant association with overall survival (OS) and cancer-specific survival (CSS). These characteristics were employed to build a nomogram. Particularly, the discrimination of nomogram for OS and CSS prediction in the training set were excellent (C-index = 0.762, 95% CI: 0.754-0.770 and C-index = 0.774, 95% CI: 0.766-0.782). The AUC of the nomogram for predicting 2- and 5-year OS was 0.834 and 0.853 and CSS was 0.844 and 0.866. Similar results were observed in the internal validation set.

CONCLUSION:

We have successfully established a novel nomogram for predicting OS and CSS in EAC patients with good accuracy, which can help clinicians predict the survival of individual patient survival and provide optimal treatment strategies.

• Authors: He Huang, Weiyue Fang, Ying Lin, Zhanzhong Zheng, Zefan Wang, Xiangfen Chen, Kang Yu, Guangrong Lu
• Journal: Journal of oncology (J Oncol) Vol. 2021 Pg. 4138575 ( 2021) ISSN: 1687-8450 [Print] Egypt
• PMID: 34567114 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 He Huang et al.