An unmet significant challenge in the treatment of many early-stage cancers is the lack of effective prognostic models to identify patients who are at high risk of disease progression from a large number of potentially cured patients.Semi-parametric mixture cure models can account for latent cure fractions in patient populations thus are more suitable prognostic models than standard survival models such as Cox Proportional Hazard models or Proportional Odds models that ignore the existence of latent cure fractions.Without the requirement of knowing who is surely cured,the semiparametric mixture cure models can be used to evaluate predictive utility of biomarkers on cure probability and on survival of uncured subjects.