Literature DB >> 30213791

Development and Validation of Lifestyle-Based Models to Predict Incidence of the Most Common Potentially Preventable Cancers.

Juliet A Usher-Smith1, Stephen J Sharp2, Robert Luben3, Simon J Griffin4.   

Abstract

BACKGROUND: Most risk models for cancer are either specific to individual cancers or include complex or predominantly non-modifiable risk factors.
METHODS: We developed lifestyle-based models for the five cancers for which the most cases are potentially preventable through lifestyle change in the UK (lung, colorectal, bladder, kidney, and esophageal for men and breast, lung, colorectal, endometrial, and kidney for women). We selected lifestyle risk factors from the European Code against Cancer and obtained estimates of relative risks from meta-analyses of observational studies. We used mean values for risk factors from nationally representative samples and mean 10-year estimated absolute risks from routinely available sources. We then assessed the performance of the models in 23,768 participants in the EPIC-Norfolk cohort who had no history of the five selected cancers at baseline.
RESULTS: In men, the combined risk model showed good discrimination [AUC, 0.71; 95% confidence interval (CI), 0.69-0.73] and calibration. Discrimination was lower in women (AUC, 0.59; 95% CI, 0.57-0.61), but calibration was good. In both sexes, the individual models for lung cancer had the highest AUCs (0.83; 95% CI, 0.80-0.85 for men and 0.82; 95% CI, 0.76-0.87 for women). The lowest AUCs were for breast cancer in women and kidney cancer in men.
CONCLUSIONS: The discrimination and calibration of the models are both reasonable, with the discrimination for individual cancers comparable or better than many other published risk models. IMPACT: These models could be used to demonstrate the potential impact of lifestyle change on risk of cancer to promote behavior change. ©2018 American Association for Cancer Research.

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Year:  2018        PMID: 30213791      PMCID: PMC6330056          DOI: 10.1158/1055-9965.EPI-18-0400

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  60 in total

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Review 4.  Risk Prediction Models for Kidney Cancer: A Systematic Review.

Authors:  Hannah Harrison; Rachel E Thompson; Zhiyuan Lin; Sabrina H Rossi; Grant D Stewart; Simon J Griffin; Juliet A Usher-Smith
Journal:  Eur Urol Focus       Date:  2020-07-14
  4 in total

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