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Breast cancer remains one of the most common cancers among women worldwide. Breast cancer screening programmes aim to catch the disease at its early phase, by regularly examining asymptomatic women for signs of cancer. The rationale is straightforward: early detection, before symptoms onset, offers patients broader treatment options and improves the chances of recovery. To evaluate the cancer screening programmes, we focus on two measures: lead time and overdiagnosis.
While, in the real world, a person can either be or not be invited to the screening programme, in a counterfactual framework, a person is considered in both potential worlds – the one where they are invited to the screening programme and the one where they are not. Within this framework, lead time is defined as the difference between the times at which a person would have been diagnosed in the two worlds. Another important metric is overdiagnosis, referring to cancers detected by screening that would never have been identified had the person not been invited to the programme. E.g., this can happen when a non-progressive tumour is detected at screening visit, but the disease would never have progressed to the symptomatically detectable phase.
The MOCCI method [1] was developed to jointly estimate lead time and overdiagnosis. Based on comparison of cancer incidences between two groups (invited or not invited to the programme), it aims to find distributions of lead time and overdiagnosis which best explain the difference in incidence, using MLE principles.
In this work, we extend the MOCCI method by integrating the biological tumour growth model [2]. Tumour size at diagnosis, routinely recorded in cancer registries, offers valuable information that can be used for the lead time estimation. Alongside age and calendar time at diagnosis, tumour size can be introduced to the MOCCI method as a third modelling dimension. With this introduction, the focus of the estimation procedure shifts to the estimation of latent processes (e.g. tumour growth rate), from which the lead time and overdiagnosis can be estimated.
We outline the extended MOCCI estimation procedure, focusing on the utilization of the tumour growth model, and present preliminary simulation results assessing the feasibility of the proposed approach.
[1] Vratanar B, Pohar Perme M. Estimating lead time and overdiagnosis in cancer screening programmes: the MOCCI method, under review.
[2] Isheden G, Humphreys K. Modelling breast cancer tumour growth for a stable disease population. Stat Methods Med Res. 2019 Mar;28(3):681-702.
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