Prostate Cancer Other Achievements: Highlights
- What are the real effects of PSA screening on racial disparities in prostate cancer survival?
- Are risks of biopsy upgrading consistent across active surveillance studies?
- Can conservative treatment improve the cost-effectiveness of screening?
- Under what conditions are empirical estimates of overdiagnosis unbiased?
- How do the methods used to estimate overdiagnosis affect the results?
- How does overdiagnosis depend on patient and tumor characteristics?
- Can we quantify the risk of overdetection of PSA recurrence after radical prostatectomy?
- How do age and comorbidity impact harms and benefits of PSA screening?
- Can PSA screening for prostate cancer be cost-effective?
- Can we find "smarter" PSA screening strategies that reduce harms while preserving benefit?
- How much higher is prostate cancer mortality after active surveillance vs immediate surgery?
- Can ecologic analysis be used to determine the likely efficacy of PSA screening?
- Can racial disparities in PSA screening explain racial differences in prostate cancer mortality declines?
- Is tumor dedifferentiation actually being prevented by early detection and consequent treatment?
- Do prostate cancer tumor characteristics affect PSA growth?
- Are there racial disparities in prostate cancer care?
What are the real effects of PSA screening on racial disparities in prostate cancer survival?
Racial disparities in prostate cancer survival narrowed during the PSA era, suggesting that screening may induce more equitable outcomes. However, artifacts of screening—lead time and overdiagnosis—can inflate survival without reflecting real benefit. We developed a simulation model to disentangle these artifacts from real survival improvements by age and race. We found that lead time and overdiagnosis explained nearly all of the apparent survival improvements at older ages. Depending on the screening benefit, real survival improvements in the PSA era ranged from 27%–40% among black men and 26%–38% among all races. We concluded that real improvements in survival disparities in the PSA era are smaller than those observed and reflect similar reductions in the risk of prostate cancer death among blacks and all races (Kaur et al., 2018).
Are risks of biopsy upgrading consistent across active surveillance studies?
Active surveillance is an increasingly accepted approach for managing low-risk prostate cancer, yet there is no consensus about implementation. This lack of consensus is due in part to uncertainty about risks for disease progression, which have not been systematically compared or integrated across studies with different surveillance protocols and different frequencies of dropout to active treatment. We developed and fit a joint model of PSA levels and risks for biopsy upgrading (from Gleason score ≤6 to ≥7) to patient records from Johns Hopkins University (JHU); Canary Prostate Active Surveillance Study (PASS); University of California, San Francisco (UCSF); and University of Toronto (UT) active surveillance studies. After accounting for differences in surveillance intervals and competing treatments, estimated risks for biopsy upgrading were similar in the PASS and UT studies but higher in UCSF and lower in JHU studies. However, despite heterogeneity in risks of upgrading, evidence across studies is consistent with only a small delay in upgrading associated with biennial compared with annual biopsies (Inoue et al., 2018).
Can conservative treatment improve the cost-effectiveness of screening?
Some researchers have suggested that personalized screening combined with conservative management for low-risk cancers could improve the net value of PSA screening. We evaluated 18 screening strategies combined with (1) contemporary treatment practices based on age and cancer stage and grade observed in the Surveillance, Epidemiology, and End Results Program in 2010 or (2) selective treatment practices whereby cancers with Gleason score <7 and clinical stage ≤T2a are treated only after clinical progression. Combined with contemporary treatment, only highly conservative screening strategies were cost-effective relative to no screening. Combined with selective treatment, many more strategies became cost-effective. For PSA screening to be cost-effective, it needs to be used conservatively and ideally in combination with a conservative management approach for low-risk disease (Roth et al., 2016).
Under what conditions are empirical estimates of overdiagnosis unbiased?
Overdiagnosis is often estimated by calculating the excess incidence in a screened group relative to an unscreened group. Yet conditions for unbiased estimation are poorly understood. We developed a conceptual framework to project the effects of screening on the incidence of so-called relevant cancers—cancers that would present clinically without screening—in common trial and population settings. Screening advances the date of diagnosis for a fraction of preclinical relevant cancers; which diagnoses are advanced and by how much depends on the preclinical detectable period, test sensitivity, and screening patterns. Using the model, we compared excess incidence with true overdiagnosis. In trials with no control arm screening, unbiased estimates are available using cumulative incidence if the screen arm stops screening and using annual incidence if the screen arm continues screening. In both designs, unbiased estimation requires waiting until screening stabilizes plus the maximum preclinical period. In continued-screen trials and population settings, excess cumulative incidence is persistently biased. In general, no trial or population setting automatically permits unbiased estimation of overdiagnosis; sufficient follow-up and appropriate analysis remain crucial (Gulati et al., 2016).
How do the methods used to estimate overdiagnosis affect the results?
Frequencies of overdiagnosis of breast and prostate cancers, cancers that would not have presented in the absence of screening, have been estimated in numerous studies. This study explores study features and methods that influence published estimates. It finds that (1) the definition of the overdiagnosis, (2) the measurement of overdiagnosis, (3) the study design and context, and (4) the estimation approach are the most influential features. The study summarizes known issues with “excess-incidence” and “lead-time” approaches for estimating overdiagnosis, and it concludes with a suggested list of questions that readers of overdiagnosis studies should evaluate to better understand the likely validity of reported estimates (Etzioni et al., 2013). A separate study presented a targeted commentary on the limitations of these two estimation approaches (Etzioni et al., 2015).
How does overdiagnosis depend on patient and tumor characteristics?
Although overdiagnosis is not directly observable, it arises as the result of two competing risks in a screen-detected individual, namely the risk of disease progression to a clinical or symptomatic state and the risk of other-cause death. Consequently, the likelihood of overdiagnosis may be expected to vary with disease characteristics related to progression and patient characteristics, like age and comorbidity, related to the risk of non-cancer mortality. In this study, we used one model to project the fraction overdiagnosed among screen-detected cases given age, disease grade, and PSA level. We found that depending on these characteristics, the chance of overdiagnosis varied from 3% to 88% (Gulati et al, 2014).
Can we quantify the risk of overdetection of recurrence after radical prostatectomy?
PSA recurrence after radical prostatectomy can occur many years before progression to overt metastasis. If a patient dies of an unrelated cause before his cancer would have progressed, we say that his recurrence was overdiagnosed and any additional treatment could only have caused harm. To quantify the frequency of overdiagnosis of recurrence after prostatectomy, and to examine how it depends on patient age, PSA at diagnosis, and tumor stage and grade at diagnosis, this study compared time to metastasis in a well-studied cohort of patients who did not receive salvage treatment at recurrence and time to non-cancer death from U.S. life tables adjusted for this patient population. The comparison suggested that a non-trivial fraction of men with PSA recurrence after prostatectomy were overdiagnosed, reaching as high as 30% for men over 70 years of age at diagnosis with PSA failure within 5-10 years of diagnosis (Xia et al., 2014).
How do age and comorbidity impact harms and benefits of screening?
Harms and benefits of screening are known to depend on age and comorbid conditions, but reliable estimates of when to stop screening for prostate or other cancers had not been carefully studied. In a collaboration involving 7 models and 3 cancer sites, we examined false positive tests and overdiagnoses (harms) and cancer deaths prevented and life-years gained (benefits) under population screening programs that terminated at ages between 66 and 90 for individuals with 1 of 4 levels of comorbid conditions. The models projected that individuals with few comorbid conditions can continue screening later and individuals with more comorbid conditions can stop screening earlier to match the harm-benefit tradeoffs estimated for average-health individuals (Lansdorp-Vogelaar et al., 2014).
Can PSA screening for prostate cancer be cost-effective?
Although the European Randomized Study of Screening for Prostate Cancer (ERSPC) trial showed a statistically significant 29% prostate cancer mortality reduction, overdiagnosis due to screening can impact quality of life. Alternative screening strategies for the population may exist that optimize the effects on mortality reduction, quality of life, overdiagnosis, and costs. Based on data from the ERSPC trial, we used one model to predict the cost-effectiveness of 68 screening strategies starting at age 55 years. The screening strategies varied by age to stop screening and screening interval (1 to 14 years or once in a lifetime screens) and therefore the number of tests. The results indicated that prostate cancer screening can be cost-effective when it is limited to two or three screens between ages 55 to 59 years. Screening above age 63 years is less cost-effective because of loss of QALYs because of overdiagnosis in this setting (Heijnsdijk et al., 2014).
Can we find “smarter” PSA screening strategies that reduce harms while preserving benefit?
The U.S. Preventive Services Task Force recommended against routine PSA screening in 2012 based on harms and benefits of then-current PSA-based screening practices but called for additional research into alternative use of existing screening tools and practices that improve harm-benefit tradeoffs. One model was used to project plausible harms and benefits under 35 alternative PSA screening strategies that differed by ages to start and stop screening, screening frequency, and criteria for biopsy referral. The results indicated that PSA screening strategies that use higher thresholds for biopsy referral for older men and that screen men with low PSA levels less frequently can reduce harms while preserving lives saved (Gulati et al., 2013).
How much higher is prostate cancer mortality after active surveillance vs immediate surgery?
Active surveillance has become increasingly accepted as a viable alternative to radical treatment for low-risk prostate cancers and a key component of a concerted effort to reduce overtreatment. However, the risk of prostate cancer death following active surveillance is unknown. This study developed a simulation model to combine data on patterns of progression while on active surveillance and implications for prostate cancer death after delayed radical treatment. The model projected that 3.4% of men on active surveillance would die of prostate cancer compared with 2.0% of men who receive immediate radical treatment. Yet the former would enjoy, on average, 6.4 years without the adverse effects of treatment (Xia et al., 2012).
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 et al., 2004; Etzioni, Feuer 2008).
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 et al., 2007). These similarities indicate that racial disparity in PSA testing is probably not a major factor behind racial differences in prostate cancer mortality declines.
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 Study of Screening for Prostate Cancer (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 et al., 2006).
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 et al., 2004).
Are there racial disparities in prostate cancer care?
Using linked SEERMedicare 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 et al., 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 et al., 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.