The Microsimulation model for the Assessment of Individualized Cancer Care (MAICare) is a microsimulation model framework describing the cancer disease process. To do this a description of underlying tumor growth as well as its interaction with diagnostics, treatments and surveillance is used. Thereby the framework allows for the exploration of the impact of altering diagnostics as well as (individualized) treatments on health outcomes and costs.

The framework

The framework consists of two components; the disease model and clinical management module.

• The disease model consists of a tumor level, describing growth and metastasis of the tumor (pictured above), and a patient level, describing clinical observed states, such as recurrence and death, either from the disease or other causes (not pictured).
• The clinical management module consists of the care patients receive, i.e. the diagnostic process, treatment and surveillance (pictured below). This module interacts with the disease process, influencing rate of transitioning between tumor growth states at the tumor level, and the rate of detecting a recurrence at the patient level.

The simulation cycle

1. Patients enter the model at first diagnosis. A patient is generated by drawing sex, age, stage, and additional stage specific characteristics. Also time to death due to other causes is drawn, based on sex and age at diagnosis.
2. Based on the patient characteristics further diagnostic tests are selected and a treatment is assigned. Based on tests and treatment, the starting states of the local, regional and distant chain on the tumor level are drawn.
3. Subsequently, at the tumor level, tumor growth is simulated from the starting states drawn in the previous step, taking the impact of the choice of treatment on the tumor growth rates into account. There are two options.
   1. All three chains are in the absent state: No growth is simulated. The only transition to occur is death due to other causes (drawn during step 1).
   2. At least for one of the three chains, the tumor is the dormant state or in a more severe state. Growth is simulated based on the tumor growth model. A recurrence will be detected either at surveillance or at the latest when one of the chains enters the symptomatic state.
4. Based on patient characteristics, a surveillance schedule is selected. At each scheduled surveillance visit, the tumor state on each of the three chains is recorded and detection rates of planned diagnostic tests are applied to simulate whether a recurrence or progression is detected or not. Surveillance continues until the end of the surveillance schedule as specified in the clinical management module, until a recurrence is detected (by surveillance of by symptoms), or until death of other causes, whatever comes first.
5. When a patient enters the recurrence state, the type of recurrence is determined by the chain that first reached the symptomatic state. In case of a local or regional recurrence, the patient goes back to step 2, and proceeds through the model as described. In case of a recurrence at a distant site, time to death ois drawn according to the time-to-event model.

Project progress

We have implemented the framework for melanoma care and progression in the Netherlands. MAICARE-melanoma has been calibrated to data from the literature and from a Dutch retrospective observational study. Currently, we are using the model to evaluate different diagnosis and treatment strategies. Furthermore, a MAICARE application for non-small cell lung cancer is under development.