In recent years, a stochastic model of cancer development and detection allowing for arbitrary screening schedules has been developed and applied to analysis of screening trials and population-based cancer incidence and mortality data. The model is entirely mechanistic, builds on a minimal set of biologically plausible assumptions, and yields the joint distribution of tumor size and age of a patient at the time of diagnosis. Whether or not parameters of the model can be estimated from data generated by cohort studies depends on model identifiability. The present paper provides a proof of this important property of the model.