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Cancer Intervention and Surveillance Modeling Network

Modeling to guide public health research and priorities

Other Achievements: Highlights

Is tumor dedifferentiation actually being prevented by early detection and consequent treatment?

Tumor differentiation as measured by the Gleason score is highly predictive of the course of prostatic cancer after diagnosis. Data from the European randomized controlled trial on PSA screening (ERSPC) was fit to the Erasmus MC, University Medical Center Rotterdam prostate cancer model (MISCAN) under two different model assumptions: Model I, where tumors dedifferentiate before becoming screen-detectable, and Model II, where dedifferentiation occurs during the screen-detectable pre-clinical phase. Model II fit the ERSPC data significantly better than Model I, where tumors dedifferentiate before becoming screen-detectable, and Model II, where dedifferentiation occurs during the screen-detectable pre-clinical phase. Model II fit the ERSPC data significantly better than Model I, providing epidemiological evidence that tumors dedifferentiate during the screen-detectable phase and, consequently, screening with PSA and early treatment can possibly prevent progression to poorer Gleason scores (Draisma 2006).

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Do prostate cancer tumor characteristics affect PSA growth?

The Fred Hutchinson Cancer Research Center group combined three retrospective studies of PSA growth prior to prostate diagnosis: the Nutritional Prevention of Cancer Trials, the Beta-Carotene and Retinol Efficacy Trial, and the Baltimore Longitudinal Study of Aging. This showed accelerated PSA growth among cases later diagnosed with late-stage or high-grade disease than among those later diagnosed with early-stage or low-grade disease. The findings have important implications for screening strategies because they suggest that the window of opportunity to identify more aggressive cancers or those destined to spread may be shorter than that for more indolent cancers (Inoue 2004).

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What are the patterns of prostate cancer treatment in the United States?

Using SEER-Medicare and CaPSURE oncology databases, the University of Michigan group developed a model and new statistical methodology to estimate the patterns of prostate cancer treatment utilization in the United States by age, calendar year, stage, and grade. Model predictions were used to develop the Prostate Treatment Utilization Generator, a versatile software tool simulating the patterns of cancer treatment decisions from 1973 to 2005. This tool was used by all prostate cancer groups to unify treatment utilization model inputs in the collaborative base case CISNET studies (Tsodikov 2008).

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Can racial disparities in PSA screening explain racial differences in prostate cancer mortality declines?

By combining Medicare claims and National Health Interview Survey data, patterns on PSA screening were reconstructed for African-American and white men in the United States. Results indicate that uptake of screening among young African-American men (under age 65) was comparable to that among young white men and that overall screening dissemination among African-Americans only lagged slightly behind that among whites (Mariotto 2007). These similarities indicate that racial disparity in PSA testing is probably not a major factor behind racial differences in prostate cancer mortality declines.

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Are there racial disparities in prostate cancer care?

Using linked SEER–Medicare data, estimated trends of prostate cancer treatments have confirmed the findings of other studies showing rapid growth in the uptake of adjuvant and neo-adjuvant hormonal therapy in the 1990s (Zeliadt 2004). However, the same study also showed that African-American men were significantly less likely than white men to receive aggressive therapy for their tumors during the 1990s. In a separate study (Zeliadt 2003), a noticeable difference between African-American and whites in the frequency of post-diagnosis surveillance was found. Taken together, these results are consistent with the hypothesis that treatment disparities play a role in the poorer outcomes experienced by African-American prostate cancer patients.

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Can ecologic analysis be used to determine the likely efficacy of PSA screening?

In the absence of conclusive findings from ongoing randomized trials of PSA screening, ecological studies comparing rates of prostate-cancer death between regions or countries with different screening intensities may play a role in the debate about the benefits of screening. This study compared PSA screening and prostate cancer mortality rates in nine SEER areas in the United States and found moderate association between the extent of PSA use and prostate cancer mortality declines. A computer model was used to determine whether divergence of mortality declines would be expected under an assumption of a clinically significant survival benefit due to screening. The model projected that in the presence of modest differences in screening frequencies, the mortality differences are likely to be small and might be swamped by other effects such as treatment changes. The authors concluded that ecologic studies of PSA screening, particularly those with negative results, should be interpreted with extreme caution (Shaw 2004; Etzioni, Feuer 2008).

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What are the optimal age-specific PSA cutoffs to detect significant prostate cancers in curable stages?

The natural history of prostate cancer was modeled jointly with PSA growth (Inoue 2008). This model serves as a framework for evaluating candidate PSA-based screening policies. The large prostate cancer screening trials currently under way utilize specific PSA cutoffs (4 ng/ml in the United States and 3 ng/ml in Europe) to determine who will have a biopsy or not, and the trials are not equipped to interrogate alternative policies. This model optimizes PSA screening policies in a way that is not possible in randomized trials. Early results were presented at the 2008 meeting of the Society for Medical Decision Making and showed that changing the PSA cutoff from 4.0 ng/ml to 2.5 ng/ml increases the number of overdiagnosed cases by 35% but leads to an increase of only 0.5% in the number of cases detected with non-metastatic disease. For every additional case detected with non-metastatic disease using the lower cutoff, 425 additional cases would be overdiagnosed.

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