Nomograms as predictive models

Abstract

Nomograms are valuable tools for estimating the likelihood of cancer being diagnosed, the pathologic features of a localized cancer, and the prognosis of a patient after treatment. Although the available nomograms are reasonably accurate, better predictive factors including additional clinical factors and new molecular analyses are needed to improve the accuracy or predictions. Nomogram performance will also be enhanced with larger datasets of patients and longer follow-up. We review the concepts of risk stratification and the development and use of nomograms as predictive tools.