American Cancer Society Guidelines for the Early Detection of Cancer

The European Randomized study of Screening for Prostate Cancer (ERSPC) has shown a disease-specific mortality reduction of prostate-specific antigen (PSA) screening for prostate cancer (1). After eleven years of follow-up, prostate cancer mortality was reduced by 29% after adjustment for noncompliance. In terms of absolute effect, 37 cancers would need to be detected to avert one prostate cancer death (1). Some of the screen-detected prostate tumors (23% to 42%) might never give rise to clinical symptoms and would not lead to death from prostate cancer (2). These overdetected cancers reduce quality of life and result in higher costs because of overtreatment (3), affecting the balance of benefits and harms as well as cost-effectiveness of PSA testing for prostate cancer. In our recent study, we demonstrated that the introduction of a screening program between the ages of 55 to 70 with a four-year interval would result in a gain of 52 life-years and 41 quality-adjusted life-years (QALYs) per 1000 men over their life span (a 23% negative impact on the life-years gained because of quality of life [4]).

Very recently the American Urological Association (AUA) recommended shared decision-making for men age 55 to 69 years who are considering PSA screening, but they gave no clear indication of the screen interval. In the ERSPC, the Swedish center used a two-year screening interval, whereas the other centers used four-year intervals (1). In the United States, annual screening is more common. There are no trials comparing different screening intervals, and such empirical studies are highly unlikely to be conducted because of the immense resources required.

Few recent cost-effectiveness studies have been published using QALYs gained. Most cost-effectiveness studies for prostate cancer screening have been performed before large screening trial results had been published and showed very inconsistent results (5,6).

The aim of this study was to assess the cost-effectiveness of prostate cancer screening. Based on data of the ERSPC trial various prostate cancer screening strategies were modeled to find the optimal screening intervals and ages.

/pdf/cost-effectiveness-of-prostate-cancer-screening-a-simulation-18rr5oe8o8.pdf

Cost-effectiveness of Prostate Cancer Screening: A Simulation Study Based on ERSPC Data

Selecting a Dynamic Simulation Modeling Method for Health Care Delivery Research—Part 2: Report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force

Screening for prostate cancer is a controversial topic within the field of urology. The US Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial did not demonstrate any difference in prostate-cancer-related mortality rates between men screened annually rather than on an 'opportunistic' basis. However, in the world's largest trial to date--the European Randomised Study of Screening for Prostate Cancer--screening every 2-4 years was associated with a 21% reduction in prostate-cancer-related mortality rate after 11 years. Citing the uncertain ratio between potential harm and potential benefit, the US Preventive Services Task Force recently recommended against serum PSA screening. Although this ratio has yet to be elucidated, PSA testing--and early tumour detection--is undoubtedly beneficial for some individuals. Instead of adopting a 'one size fits all' approach, physicians are likely to perform personalized risk assessment to minimize the risk of negative consequences, such as anxiety, unnecessary testing and biopsies, overdiagnosis, and overtreatment. The PSA test needs to be combined with other predictive factors or be used in a more thoughtful way to identify men at risk of symptomatic or life-threatening cancer, without overdiagnosing indolent disease. A risk-adapted approach is needed, whereby PSA testing is tailored to individual risk.

Risk stratification in prostate cancer screening.

Prostate cancer is the most common solid tumor in American men and is screened for using prostate-specific antigen (PSA) tests. We report on a nonstationary partially observable Markov decision process (POMDP) for prostate biopsy referral decisions. The core states are the patients' prostate cancer related health states, and PSA test results are the observations. Transition probabilities and rewards are inferred from the Mayo Clinic Radical Prostatectomy Registry and the medical literature. The objective of our model is to maximize expected quality-adjusted life years. We solve the POMDP model to obtain an age and belief (probability of having prostate cancer) dependent optimal biopsy referral policy. We also prove a number of structural properties including the existence of a control-limit type policy for the biopsy referral decision. Our empirical results demonstrate a nondecreasing belief threshold in age, and we provide sufficient conditions under which PSA screening should be discontinued for older patients. Finally, the benefits of screening under the optimal biopsy referral policy are estimated, and sensitivity analysis is used to prioritize the model parameters that would benefit from additional data collection.

/pdf/optimization-of-prostate-biopsy-referral-decisions-2ahskpl9pu.pdf

Optimization of Prostate Biopsy Referral Decisions

Objective—To estimate the benefit of PSA-based screening for prostate cancer from the patient and societal perspectives. Method—A partially observable Markov decision process (POMDP) model was used to optimize PSA screening decisions. We considered age-specific prostate cancer incidence rates and the mortality rates from prostate cancer and competing causes. Our model trades off the potential benefit of early detection with the cost of screening and loss of patient quality of life due to screening and treatment. PSA testing and biopsy decisions are made based on the patient’s probability of having prostate cancer. Probabilities are inferred based on the patient’s complete PSA history using Bayesian updating.

/pdf/optimization-of-psa-screening-policies-a-comparison-of-the-3p5qffoe8i.pdf

Optimization of PSA Screening Policies: a Comparison of the Patient and Societal Perspectives

We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.

https://btdenton.engin.umich.edu/wp-content/uploads/sites/138/2015/08/Underwood-2012.pdf

Simulation optimization of PSA-threshold based prostate cancer screening policies

A system (10) for personalization of patient pathways and treatment options includes a patient information database (32)which stores patient data relating to a patient's medical records. A patient personalization system (12) receives the patient's lifestyle values and preferences and evaluates choices of pathways and treatments and a clinical decision support system (18)generates choices of pathways and treatments from the patient data and the patient's lifestyle values and preferences.

Jingyu Zhang

Papers

Optimization of Prostate Biopsy Referral Decisions

Optimization of PSA Screening Policies: a Comparison of the Patient and Societal Perspectives

Simulation optimization of PSA-threshold based prostate cancer screening policies

Personalizing patient pathways based on individual preferences, lifestyle regime, and preferences on outcome parameters to assist decision making

Partially Observable Markov Decision Processes for Prostate Cancer Screening