Ovarian cancer screening based on mixture change-point model

Ovarian cancer is one of the most deadly female genital malignant tumors in many regions while an effective early screening strategy can save numerous lives. CA125 and HE4 are tumor markers validated efficacious as well as most commonly used in recent screening research of ovarian cancer. In this paper, the authors construct a change-point and mixture model on the basis of longitudinal CA125 and HE4 levels and estimated parameters using maximum likelihood method with the preclinical duration assumed right-censored, which is more adaptive and yields comparable results in comparison to the Bayesian approach raised by Skates. Consistency of estimators is proved. The authors also run a 5-year simulation of sequential screening by calculating the risk of cancer and hypothesis testing the true incidence time respectively. Results show that diagnosis based on hypothesis test performs better in early detection.