Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery

Abstract

Background: Elderly gastric cancer (ELGC) remains one of the intensively investigated topics during the last decades. To establish a comprehensive nomogram for effective clinical practice and assessment is of significance. This study is designed to develop a prognostic nomogram for ELGC both in overall survival (OS) and cancer-specific survival (CSS).

Methods: The recruited cases were from the Surveillance, Epidemiology, and End Results (SEER) database and input for the construction of nomogram.

Results: A total of 4,414 individuals were recruited for this study, of which 2,208 were randomly in training group and 2,206 were in validation group. In univariate analysis of OS, significant variables (P<0.05) included age, marital status, grade, American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage, bone/brain/liver/lung metastasis and tumor size. In univariate analysis of CSS, significant variables (P<0.05) included age, grade, AJCC TNM stage, bone/brain/liver/lung metastasis and tumor size. In multivariate analysis of OS, sex, age, race, grade, TNM stage, lung metastasis and tumor size were considered as the significant variables and subjected to the establishment of nomogram. In multivariable analysis of CSS, age, grade, TNM, tumor size were considered as the significant variables and input to the establishment of nomogram. Sex, age, race, grade, TNM stage, lung metastasis and tumor size were included for the establishment of nomogram in OS while age, grade, TNM, tumor size were included to the establishment of nomogram in CSS. C-index, decision curve analysis (DCA) and the area under the curve (AUC) showed distinct value of newly established nomogram models. Both OS and CSS nomograms showed higher statistic power over the AJCC stage.

Conclusions: This study established and validated novel nomogram models of OS and CSS for ELGC based on population dataset.

Keywords: Elderly gastric cancer (ELGC); cancer-specific survival (CSS); nomogram; overall survival (OS).

Figure 1

The inclusion flowchart of recruited ELGC patients. ELGC, elderly gastric cancer.

Figure 2

Determination of best-cutoff points of age and tumor size variables by the X-tile software. (A) Identification of optimal cutoff point using X-tile plot of training sets in age; (B) the cutoff points were displayed in histogram; (C) distinct prognosis among high/middle/low subsets using a Kaplan-Meier plot (low subset = blue, middle subset = gray, high subset = magenta); (D) identification of optimal cutoff point using X-tile plot of training sets in tumor size; (E) the cutoff points were displayed in histogram; (F) distinct prognosis among high/middle/low subsets using a Kaplan-Meier plot (low subset = blue, middle subset = gray, high subset = magenta). CS, collaborative stage.

Figure 3

Forest plot of all variables with hazard ratios in ELGC. *, P<0.05. HR, hazard ratio; CI, confidence interval; ELGC, elderly gastric cancer.

Figure 4

Establishment of nomograms regarding both overall survival (OS) and cancer-specific survival (CSS). (A) Establishment of OS nomogram; (B) establishment of CSS nomogram.

Figure 5

Evaluation of calibration plots using OS nomogram model. (A) Evaluation of calibration plot based on OS of training dataset in 1-year; (B) evaluation of calibration plot based on OS of training dataset in 3-year; (C) evaluation of calibration plot based on OS of training dataset in 5-year; (D) evaluation of calibration plot based on OS of validation dataset in 1-year; (E) evaluation of calibration plot based on OS of validation dataset in 3-year; (F) evaluation of calibration plot based on OS of validation dataset in 5-year. OS, overall survival.

Figure 6

Evaluation of calibration plots using CSS nomogram model. (A) Evaluation of calibration plot based on CSS of training dataset in 1-year; (B) evaluation of calibration plot based on CSS of training dataset in 3-year; (C) evaluation of calibration plot based on CSS of training dataset in 5-year; (D) evaluation of calibration plot based on CSS of validation dataset in 1-year; (E) evaluation of calibration plot based on CSS of validation dataset in 3-year; (F) evaluation of calibration plot based on CSS of validation dataset in 5-year. CSS, cancer-specific survival.

Figure 7

Display of decision curve analysis (DCA) of nomograms models both for OS and CSS. (A) DCA in nomogram using OS training dataset; (B) DCA in nomogram using OS validation set; (C) DCA in nomogram using CSS training set; (D) DCA in nomogram using CSS validation set. Green: AJCC stage; red: nomogram. OS, overall survival; CSS, cancer-specific survival.

Figure 8

Receiver operating characteristics curve (ROC) comparison of OS nomogram and AJCC TNM stage. (A) ROC of nomogram using OS of train dataset in 1-year; (B) ROC of nomogram using OS of train dataset in 3-year; (C) ROC of nomogram using OS of train dataset in 5-year; (D) ROC of nomogram using OS of validation dataset in 1-year; (E) ROC of nomogram using OS of validation dataset in 3-year; (F) ROC of nomogram using OS of validation dataset in 5-year. OS, overall survival.

Figure 9

Receiver operating characteristics curve (ROC) comparison of CSS nomogram and AJCC TNM stage. (A) ROC of nomogram using CSS of train dataset in 1-year; (B) ROC of nomogram using CSS of train dataset in 3-year; (C) ROC of nomogram using CSS of train dataset in 5-year; (D) ROC of nomogram using CSS of validation dataset in 1-year; (E) ROC of nomogram using CSS of validation dataset in 3-year; (F) ROC of nomogram using CSS of validation dataset in 5-year. CSS, cancer-specific survival.