Development and validation of pretreatment nomogram for disease-specific mortality in gastric cancer-A competing risk analysis

A Novel Nomogram to Predict Resectable Gastric Cancer Based on Preoperative Circulating Tumor Cell

INTRODUCTION

Circulating tumor cells (CTCs) have been suggested to have an important prognostic role in gastrointestinal tumors. We developed a preoperative CTC-based nomogram to predict the prognosis of patients with resectable gastric cancer after surgery and established a risk stratification system based on the nomogram.

METHODS

From January 2012 to June 2017, we screened 258 patients with gastric cancer treated with surgery from one center as the training cohort and 133 patients with gastric cancer treated with surgery from another as the validation cohort, screened prognostic factors for the training cohort using univariate and multivariate Cox risk proportional models, created predictive overall survival (OS) and a recurrence-free survival (RFS) nomogram, and plotted the receiver operating characteristic curve and calibration curve for this nomogram in the training and validation cohorts. Risk score stratification was performed according to the nomogram, and OS curves were plotted for the low, medium, and high-risk groups using the Kaplan-Meier method.

RESULTS

The CTC positivity rate was 78.5% in all patients. CTC, TNM stage, and Ki-67 were the prognostic factors affecting OS and RFS after gastric cancer surgery. The nomogram consisted of these 3 variables. In the training group, the area under the curve of the nomogram for OS at 1, 3, and 5 years was 0.918, 0.829, and 0.813, respectively, and the area under the curve for RFS was 0.900, 0884, and 0.839, respectively. There was a statistically significant difference in OS among the low, medium, and high-risk groups according to the risk stratification system constructed from nomogram scores ( P < 0.001).

DISCUSSION

Two nomograms based on preoperative CTC were established to predict OS and RFS after resectable gastric cancer. The 2 nomograms had good discrimination and calibration and significant stratification ability of the risk stratification system established according to them.

Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST

Background. More than 50% of operable GEA relapse after curative-intent resection. We aimed at externally validating a nomogram to enable a more accurate estimate of individualized risk in resected GEA. Methods. Medical records of a training cohort (TC) and a validation cohort (VC) of patients undergoing radical surgery for c/uT2-T4 and/or node-positive GEA were retrieved, and potentially interesting variables were collected. Cox proportional hazards in univariate and multivariate regressions were used to assess the effects of the prognostic factors on OS. A graphical nomogram was constructed using R software’s package Regression Modeling Strategies (ver. 5.0-1). The performance of the prognostic model was evaluated and validated. Results. The TC and VC consisted of 185 and 151 patients. ECOG:PS > 0 (p < 0.001), angioinvasion (p < 0.001), log (Neutrophil/Lymphocyte ratio) (p < 0.001), and nodal status (p = 0.016) were independent prognostic values in the TC. They were used for the construction of a nomogram estimating 3- and 5-year OS. The discriminatory ability of the model was evaluated with the c-Harrell index. A 3-tier scoring system was developed through a linear predictor grouped by 25 and 75 percentiles, strengthening the model’s good discrimination (p < 0.001). A calibration plot demonstrated a concordance between the predicted and actual survival in the TC and VC. A decision curve analysis was plotted that depicted the nomogram’s clinical utility. Conclusions. We externally validated a prognostic nomogram to predict OS in a joint independent cohort of resectable GEA; the NOMOGAST could represent a valuable tool in assisting decision-making. This tool incorporates readily available and inexpensive patient and disease characteristics as well as immune-inflammatory determinants. It is accurate, generalizable, and clinically effectivex.

Development and validation of pretreatment nomogram for disease-specific mortality in gastric cancer-A competing risk analysis

Background

In several reports, gastric cancer nomograms for predicting overall or disease‐specific survival have been described. The American Joint Committee on Cancer (AJCC) introduced the attractiveness of disease‐specific mortality (DSM) as an endpoint of risk model. This study aimed to develop the first pretreatment gastric cancer nomogram for predicting DSM that considers competing risks (CRs).

Methods

The prediction model was developed using data for 5231 gastric cancer patients. Fifteen prognosticators, which were registered at diagnosis, were evaluated. The nomogram for DSM was created as visualizations of the multivariable Fine and Gray regression model. An independent cohort for external validation consisted of 389 gastric cancer patients from a different institution. The performance of the model was assessed by discrimination (Harrell's concordance (C)‐index), calibration, and decision curve analysis. DSM and CRs were evaluated, paying special attention to host‐related factors such as age and Eastern Cooperative Oncology Group performance status (ECOG PS), by using Gray's univariable method.

Results

Fourteen prognostic factors were selected to develop the nomogram. The new nomogram for DSM exhibited good discrimination. Its C‐index of 0.887 surpassed that of the American Joint Committee on Cancer (AJCC) clinical staging (0.794). The C‐index was 0.713 (AJCC, 0.582) for the external validation cohort. The nomogram showed good performance internally and externally, in the calibration and decision curve analysis. Host‐related factors including age and ECOG PS, were strongly correlated with competing risks.

Conclusions

The newly developed nomogram accurately predicts DSM, which can be used for patient counseling in clinical practice.