Stage-specific cancer incidence: an artificially mixed multinomial logit model

The role of lifestyle characteristics on prostate cancer progression in two active surveillance cohorts

BACKGROUND

Although much research has examined the relationship between lifestyle and prostate cancer (PCa) risk, few studies focus on the relationship between lifestyle and PCa progression. The present study examines this relationship among men initially diagnosed with low- to intermediate-risk PCa and managed with active surveillance (AS).

METHODS

Men enrolled in two separate AS programs were recruited for this study. Data regarding clinical, demographic and lifestyle characteristics were collected. Results were then compared between men whose disease remained low- to intermediate-risk and men whose disease progressed.

RESULTS

Demographic, clinical and physical characteristics were similar between comparative groups and cohorts, with the exception that age at the time of diagnosis and questionnaire was increased among men whose disease progressed. Lifestyle scores among men who remained low- to intermediate-risk were higher than those whose risk progressed; however, scores were only significant in one cohort on univariable analysis. On multivariable analysis, the only predictor of progression was age at diagnosis. Physical activity was consistently higher in both low risk groups, although this difference was insignificant. Consistent differences in other lifestyle variables were not observed.

CONCLUSIONS

Age remains an important predictor of PCa progression. Improving lifestyle characteristics among men initially managed with AS might help to reduce the risk of progression. Given the limitations of this study, more rigorous investigation is required to confirm whether lifestyle characteristics influence the progression of low- to intermediate-risk PCa.

Prostate cancer in young men: an important clinical entity

Prostate cancer is considered a disease of older men (aged >65 years), but today over 10% of new diagnoses in the USA occur in young men aged ≤55 years. Early-onset prostate cancer, that is prostate cancer diagnosed at age ≤55 years, differs from prostate cancer diagnosed at an older age in several ways. Firstly, among men with high-grade and advanced-stage prostate cancer, those diagnosed at a young age have a higher cause-specific mortality than men diagnosed at an older age, except those over age 80 years. This finding suggests that important biological differences exist between early-onset prostate cancer and late-onset disease. Secondly, early-onset prostate cancer has a strong genetic component, which indicates that young men with prostate cancer could benefit from evaluation of genetic risk. Furthermore, although the majority of men with early-onset prostate cancer are diagnosed with low-risk disease, the extended life expectancy of these patients exposes them to long-term effects of treatment-related morbidities and to long-term risk of disease progression leading to death from prostate cancer. For these reasons, patients with early-onset prostate cancer pose unique challenges, as well as opportunities, for both research and clinical communities. Current data suggest that early-onset prostate cancer is a distinct phenotype-from both an aetiological and clinical perspective-that deserves further attention.

The prostate cancer conundrum revisited: treatment changes and prostate cancer mortality declines

BACKGROUND

Prostate cancer mortality rates in the United States declined by >40% between 1991 and 2005. The impact of changes in primary treatment and adjuvant and neoadjuvant hormone therapy on this decline is unknown.

METHODS

The authors applied 3 independently developed models of prostate cancer natural history and disease detection under common assumptions about treatment patterns, treatment efficacy, and survival in the population. Primary treatment patterns were derived from the Surveillance, Epidemiology, and End Results registry; data on the frequency of hormone therapy were obtained from the CaPSURE (Cancer of the Prostate Strategic Urologic Research Endeavor) database; and treatment efficacy was based on estimates from randomized trials and comparative effectiveness studies of treatment alternatives. The models projected prostate cancer mortality without prostate-specific antigen screening and in the presence and absence of treatment benefit. The impact of primary treatment was expressed as a fraction of the difference between observed mortality and projected mortality in the absence of treatment benefit.

RESULTS

The 3 models projected that changes in treatment explained 22% to 33% of the mortality decline by 2005. These contributions were accounted for mostly by surgery and radiation therapy, which increased in frequency until the 1990s, whereas hormone therapies contributed little to the mortality decline by 2005. Assuming that treatment benefit was less for older men, changes in treatment explained only 16% to 23% of the mortality decline by 2005.

CONCLUSIONS

Changes in primary treatment explained a minority of the observed decline in prostate cancer mortality. The remainder of the decline probably was because of other interventions, such as prostate-specific antigen screening and advances in the treatment of recurrent and progressive disease.

The impact of PLCO control arm contamination on perceived PSA screening efficacy

PURPOSE

To quantify the extent to which a clinically significant prostate cancer mortality reduction due to screening could have been masked by control arm screening (contamination) in the Prostate, Lung, Colorectal, and Ovarian (PLCO) trial.

METHODS

We used three independently developed models of prostate cancer natural history to conduct a virtual PLCO trial. Simulated participants underwent pre-trial screening based on population patterns. The intervention arm followed observed compliance during the trial then resumed population screening. A contaminated control arm followed observed contamination during the trial then resumed population screening, while an uncontaminated control arm discontinued screening upon entry. We assumed a clinically significant screening benefit, applied population treatments and survival patterns, and calculated mortality rate ratios relative to the contaminated and uncontaminated control arms.

RESULTS

The virtual trial reproduced observed incidence, including stage and grade distributions, and control arm mortality after 10 years of complete follow-up. Under the assumed screening benefit, the three models found that contamination increased the mortality rate ratio from 0.68-0.77 to 0.86-0.91, increased the chance of excess mortality in the intervention arm from 0-4 % to 15-28 %, and decreased the power of the trial to detect a mortality difference from 40-70 % to 9-25 %.

CONCLUSIONS

Our computer simulation models indicate that contamination substantially limited the ability of the PLCO to identify a clinically significant screening benefit. While the trial shows annual screening does not reduce mortality relative to population screening, contamination prevents concluding whether screening reduces mortality relative to no screening.

What if I don't treat my PSA-detected prostate cancer? Answers from three natural history models

BACKGROUND

Making an informed decision about treating a prostate cancer detected after a routine prostate-specific antigen (PSA) test requires knowledge about disease natural history, such as the chances that it would have been clinically diagnosed in the absence of screening and that it would metastasize or lead to death in the absence of treatment.

METHODS

We use three independently developed models of prostate cancer natural history to project risks of clinical progression events and disease-specific deaths for PSA-detected cases assuming they receive no primary treatment.

RESULTS

The three models project that 20%-33% of men have preclinical onset; of these 38%-50% would be clinically diagnosed and 12%-25% would die of the disease in the absence of screening and primary treatment. The risk that men age less than 60 at PSA detection with Gleason score 2-7 would be clinically diagnosed in the absence of screening is 67%-93% and would die of the disease in the absence of primary treatment is 23%-34%. For Gleason score 8 to 10 these risks are 90%-96% and 63%-83%.

CONCLUSIONS

Risks of disease progression among untreated PSA-detected cases can be nontrivial, particularly for younger men and men with high Gleason scores. Model projections can be useful for informing decisions about treatment.

IMPACT

This is the first study to project population-based natural history summaries in the absence of screening or primary treatment and risks of clinical progression events following PSA detection in the absence of primary treatment.