The microsimulation screening analysis prostate cancer model (MISCAN-PRO) simulates individual life histories. The cancer progression process in individuals is modeled as a sequence of tumor states. There are 18 preclinical detectable states in the natural history of prostate cancer (Figure 1), which are derived from combinations of histologic grade (Surveillance, Epidemiology, and End Results [SEER] categories well, moderately, and poorly differentiated), clinical T-stages (American Joint Committee on Cancer stages T1, T2, and T3), and clinical M-stages (M0 and M1). The onset of disease is modeled as an age-dependent hazard. Progression through the clinical stages and grades is modeled as a semi-Markov process, and we assume stage- and grade-specific risks of transitions from earlier to later stages and grades. From each preclinical detectable state, the cancer can progress to the clinical disease state and be diagnosed. Screen detection depends on the probability of attendance, frequency of prostate-specific antigen (PSA) tests, the PSA threshold for a positive test, and, after a positive PSA test, biopsy compliance and stage-specific biopsy sensitivity.
The baseline parameters for the natural history of prostate cancer were estimated first using data from the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC)(1, 2). Data from the Swedish section of the ERSPC have also been included(3). For calibration to the US situation, the model used US life tables and we re-estimated the sensitivity parameters and estimated an additional stage-specific risk of clinical diagnosis to capture different pre-PSA disease diagnosis patterns in the US as compared with Europe. US-specific estimates for the parameters were obtained by calibrating the model to the observed age-specific incidence and age-specific SEER stage distribution using maximum likelihood(4).
The model is capable of handling a variety of ways to model the benefit of early detection. This includes modeling benefit among screen-detected men using a stage-shift approach, a constant cure rate, or a cure rate dependent on the stage or the lead time(5).
Recent extensions include substantial changes to the original MISCAN-PRO model. We are now able to model PSA growth and progression of disease after diagnosis. We used a modified version of the PSA growth model in the PSAPC model and linked this to the disease progression in MISCAN-PRO. We calibrated the PSA growth parameters to the PSA distribution in the ERSPC trial and SEER incidence data. In this new model we are able to implement PSA-dependent screening policies: we can stop screening or change the screening frequency if PSA is below a certain value at a certain age or screening round in a trial. Additionally, we are able to study screening policies where PSA threshold for biopsy referral depends on age. We found that stopping screening at age 70 is a reasonable way to reduce overdiagnosis and retain the benefit of early detection(6), and decreasing the stopping age has a more pronounced impact on overdiagnosis reduction than reducing the screening frequency.
References
- Draisma G, Boer R, Otto SJ, van der Cruijsen IW, Damhuis RA, Schröder FH, et al. Lead times and overdetection due to prostate-specific antigen screening: Estimates from the European Randomized Study of Screening for Prostate Cancer. J Natl Cancer Inst. 2003;95:868-78. [Abstract]
- Draisma G, Postma R, Schröder FH, van der Kwast TH, de Koning HJ. Gleason score, age and screening: modeling dedifferentiation in prostate cancer. Int J Cancer. 2006;119:2366-71. [Abstract]
- Wever EM, Hugosson J, Heijnsdijk EA, Bangma CH, Draisma G, de Koning HJ. To be screened or not to be screened? Modeling the consequences of PSA screening for the individual. Br J Cancer. 2012;107:778-84. [Abstract]
- Wever EM, Draisma G, Heijnsdijk EA, Roobol MJ, Boer R, Otto SJ, et al. Prostate-specific antigen screening in the United States vs in the European Randomized Study of Screening for Prostate Cancer-Rotterdam. J Natl Cancer Inst. 2010;102:352-5. [Abstract]
- Wever EM, Draisma G, Heijnsdijk EA, de Koning HJ. How does early detection by screening affect disease progression? Modeling estimated benefits in prostate cancer screening. Med Decis Making. 2011;31:550-8. [Abstract]
- de Carvalho TM, Heijnsdijk EA, de Koning HJ. Screening for prostate cancer in the US? Reduce the harms and keep the benefit. Int J Cancer. 2015;136:1600-7. [Abstract]
