Background: We sought to derive and validate a prediction model of survival and recurrence among Western patients undergoing resection of gastric cancer. Methods: Patients who underwent curative-intent surgery for gastric cancer at seven US institutions and a major Italian center from 2000 to 2020 were included. Variables included in the multivariable Cox models were identified using an automated model selection procedure based on an algorithm. Best models were selected using the Bayesian information criterion (BIC). The performance of the models was internally cross-validated via the bootstrap resampling procedure. Discrimination was evaluated using the Harrell's Concordance Index and accuracy was evaluated using calibration plots. Nomograms were made available as online tools. Results: Overall, 895 patients met inclusion criteria. Age (hazard ratio [HR] 1.47, 95% confidence interval [CI] 1.17-1.84), presence of preoperative comorbidities (HR 1.66, 95% CI 1.14-2.41), lymph node ratio (LNR; HR 1.72, 95% CI 1.42-2.01), and lymphovascular invasion (HR 1.81, 95% CI 1.33-2.45) were associated with overall survival (OS; all p < 0.01), whereas tumor location (HR 1.93, 95% CI 1.23-3.02), T category (Tis-T1 vs. T3: HR 0.31, 95% CI 0.14-0.66), LNR (HR 1.82, 95% CI 1.45-2.28), and lymphovascular invasion (HR 1.49; 95% CI 1.01-2.22) were associated with disease-free survival (DFS; all p < 0.05) The models demonstrated good discrimination on internal validation relative to OS (C-index 0.70) and DFS (C-index 0.74). Conclusions: A web-based nomograms to predict OS and DFS among gastric cancer patients following resection demonstrated good accuracy and discrimination and good performance on internal validation.
Development of a Prognostic Nomogram and Nomogram Software Application Tool to Predict Overall Survival and Disease-Free Survival After Curative-Intent Gastrectomy for Gastric Cancer
Spolverato, Gaya;Capelli, Giulia;Lorenzoni, Giulia;Gregori, Dario;Pucciarelli, Salvatore;
2021
Abstract
Background: We sought to derive and validate a prediction model of survival and recurrence among Western patients undergoing resection of gastric cancer. Methods: Patients who underwent curative-intent surgery for gastric cancer at seven US institutions and a major Italian center from 2000 to 2020 were included. Variables included in the multivariable Cox models were identified using an automated model selection procedure based on an algorithm. Best models were selected using the Bayesian information criterion (BIC). The performance of the models was internally cross-validated via the bootstrap resampling procedure. Discrimination was evaluated using the Harrell's Concordance Index and accuracy was evaluated using calibration plots. Nomograms were made available as online tools. Results: Overall, 895 patients met inclusion criteria. Age (hazard ratio [HR] 1.47, 95% confidence interval [CI] 1.17-1.84), presence of preoperative comorbidities (HR 1.66, 95% CI 1.14-2.41), lymph node ratio (LNR; HR 1.72, 95% CI 1.42-2.01), and lymphovascular invasion (HR 1.81, 95% CI 1.33-2.45) were associated with overall survival (OS; all p < 0.01), whereas tumor location (HR 1.93, 95% CI 1.23-3.02), T category (Tis-T1 vs. T3: HR 0.31, 95% CI 0.14-0.66), LNR (HR 1.82, 95% CI 1.45-2.28), and lymphovascular invasion (HR 1.49; 95% CI 1.01-2.22) were associated with disease-free survival (DFS; all p < 0.05) The models demonstrated good discrimination on internal validation relative to OS (C-index 0.70) and DFS (C-index 0.74). Conclusions: A web-based nomograms to predict OS and DFS among gastric cancer patients following resection demonstrated good accuracy and discrimination and good performance on internal validation.
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