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Diet- and Lifestyle-Based Prediction Models to Estimate Cancer Recurrence and Death in Patients With Stage III Colon Cancer (CALGB 89803/Alliance).

Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT. Division of Research, Kaiser Permanente Northern California, Oakland, CA. Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA. Department of Biostatistics, Yale School of Public Health, New Haven, CT. Center on Methods for Implementation and Prevention Science, Yale School of Public Health, New Haven, CT. Memorial Sloan Kettering Cancer Center, New York, NY. Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC. Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada. Loyola University, Stritch School of Medicine, Naperville, IL. Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL. Virginia Oncology Associates, Norfolk, VA. Messino Cancer Centers, Asheville, NC. University of Chicago, Chicago, IL. Department of Epidemiology, and Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA. Department of Epidemiology and Biostatistics, and Urology, University of California, San Francisco, CA. Cancer Metabolism Program, Pennington Biomedical Research Center, Baton Rouge, LA. Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT. Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale Cancer Center, New Haven, CT. Division of Hematology and Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT. Yale Cancer Center, Smilow Cancer Hospital, New Haven, CT. Hematology and Oncology Product Development, Genentech & Roche, South San Francisco, CA.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2022;(7):740-751
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Abstract

PURPOSE Current tools in predicting survival outcomes for patients with colon cancer predominantly rely on clinical and pathologic characteristics, but increasing evidence suggests that diet and lifestyle habits are associated with patient outcomes and should be considered to enhance model accuracy. METHODS Using an adjuvant chemotherapy trial for stage III colon cancer (CALGB 89803), we developed prediction models of disease-free survival (DFS) and overall survival by additionally incorporating self-reported nine diet and lifestyle factors. Both models were assessed by multivariable Cox proportional hazards regression and externally validated using another trial for stage III colon cancer (CALGB/SWOG 80702), and visual nomograms of prediction models were constructed accordingly. We also proposed three hypothetical scenarios for patients with (1) good-risk, (2) average-risk, and (3) poor-risk clinical and pathologic features, and estimated their predictive survival by considering clinical and pathologic features with or without adding self-reported diet and lifestyle factors. RESULTS Among 1,024 patients (median age 60.0 years, 43.8% female), we observed 394 DFS events and 311 deaths after median follow-up of 7.3 years. Adding self-reported diet and lifestyle factors to clinical and pathologic characteristics meaningfully improved performance of prediction models (c-index from 0.64 [95% CI, 0.62 to 0.67] to 0.69 [95% CI, 0.67 to 0.72] for DFS, and from 0.67 [95% CI, 0.64 to 0.70] to 0.71 [95% CI, 0.69 to 0.75] for overall survival). External validation also indicated good performance of discrimination and calibration. Adding most self-reported favorable diet and lifestyle exposures to multivariate modeling improved 5-year DFS of all patients and by 6.3% for good-risk, 21.4% for average-risk, and 42.6% for poor-risk clinical and pathologic features. CONCLUSION Diet and lifestyle factors further inform current recurrence and survival prediction models for patients with stage III colon cancer.