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

He Huang,Weiyue Fang,Zhanzhong Zheng,Guangrong Lu,Zefan Wang,Ying Lin,Xiangfen Chen,Kang Yu,Cristina Magi-Galluzzi

doi:10.1155/2021/4138575

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.

Highlights

MethodsA total of 39,783 esophageal adenocarcinoma (EAC) patients (between 1975 and 2017) were identified from the SEER registry database of the National Cancer Institute using SEER∗Stat software
He Huang,1 Weiyue Fang,1 Ying Lin,1 Zhanzhong Zheng,2 Zefan Wang,2 Xiangfen Chen,2 Kang Yu,3 and Guangrong Lu 4
We developed a nomogram with a multivariate Cox proportional hazards regression model that incorporates comprehensive demographic and baseline clinical variables, including age, race, insurance and marital status, tumor grade, primary site, clinical stage, chemotherapy, surgery, and radiotherapy strategy

Summary

Methods

A total of 39,783 EAC patients (between 1975 and 2017) were identified from the SEER registry database of the National Cancer Institute using SEER∗Stat software Patients with the incomplete 7th edition of the American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) staging system were excluded. En, the patients with multiple primaries tumors were further excluded. Patients with incomplete survival data, missing data in SEER cause-specific death classification, unknown surgery, unknown grade, unknown location, unknown race, unknown insurance, and unknown marital status were excluded from the study. Because all of the data used in this study were obtained from the SEER database with a publicly available method, no local ethical approval or declaration was required for this study. All data used in this study are publicly available All data used in this study are publicly available (https://seer. cancer.gov/)

Results

Discussion

Conclusion