Gaya Spolverato1, Giulia Capelli1, Giulia Lorenzoni2, Dario Gregori2, Malcolm H Squires3, George A Poultsides4, Ryan C Fields5, Mark P Bloomston6, Sharon M Weber7, Konstantinos I Votanopoulos8, Alexandra W Acher7, Linda X Jin5, William G Hawkins5, Carl R Schmidt9, David A Kooby10, David J Worhunsky11, Neil D Saunders10, Edward A Levine8, Clifford S Cho12, Shishir K Maithel10, Salvatore Pucciarelli1, Timothy M Pawlik13. 1. First Surgical Clinic, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University of Padua, Padua, Italy. 2. Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Vascular Sciences and Public Health, University of Padua, ThoracicPadua, Italy. 3. Division of Surgical Oncology, Department of Surgery, Levine Cancer Institute, Atrium Health, Charlotte, NC, USA. 4. Department of Surgery, Stanford University, Stanford, CA, USA. 5. Department of Surgery, Washington University School of Medicine, Saint Louis, MO, USA. 6. Department of Surgery, The Ohio State University, Columbus, OH, USA. 7. Division of Surgical Oncology, Department of Surgery, University of Wisconsin, Madison, WI, USA. 8. Department of Surgery, Wake Forest University, Winston-Salem, NC, USA. 9. Department of Surgery, West Virginia University, Morgantown, WV, USA. 10. Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA. 11. Department of Surgery, University of Kentucky, Lexington, KY, USA. 12. Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA. 13. Department of Surgery, The Ohio State University, Columbus, OH, USA. tim.pawlik@osumc.edu.
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.
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.
Authors: Deborah H Charbonneau; Shonee Hightower; Anne Katz; Ke Zhang; Judith Abrams; Nicole Senft; Jennifer L Beebe-Dimmer; Elisabeth Heath; Tara Eaton; Hayley S Thompson Journal: Digit Health Date: 2020-02-11