Free Essays from Bartleby | TREATMENT of localized prostate cancer usually includes prostatectomy and radiation therapy, occasionally augmented with. Facts About Prostate Cancer - Research Paper Outline Rough Draft Background Information - Second most common type of cancer in American men(
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Post a new comment. Metabolomics measures large numbers of small molecules in body fluids which reflect internal e. Using data from matched case-control sets from the European Prospective Investigation into Cancer and Nutrition EPIC researchers measured metabolite concentrations in plasma samples collected from men with an average age of Cases of prostate cancer were diagnosed on average 9. The results showed that men with metabolite profiles characterised by higher concentrations of either phosphatidylcholines and hydroxysphingomyelins, acylcarnitines C and C, glutamate, ornithine and taurine, or lysophosphatidylcholines had a lower risk of advanced stage and aggressive prostate cancer at diagnosis, with no heterogeneity by length of follow-up, and of prostate cancer death.
The age-specific model predictions are less accurate for the years preceding , which suggests that the PSA sub-model does not capture the dynamic changes in earlier PSA testing Fig E, S1 Appendix. The model was not calibrated for prostate cancer mortality.
For the validation by calendar period, see Fig F in S1 Appendix. The predicted shape of the temporal trends is similar to observed rates, although the level for the predicted mortality rates are lower than those observed. Detailed prostate cancer mortality rates by age and calendar period are shown in Fig G in S1 Appendix. The predicted rates are similar to the observed rates.
Finally, we show all cause mortality rates by age and calendar period Fig H, S1 Appendix. The model predictions follow closely the observed patterns in Stockholm and Sweden. When planning for prostate cancer testing policies, the following measures were considered to represent the burden of disease: prostate cancer incidence rate; prostate cancer overdiagnosis rate, where overdiagnosis is defined as the lifetime risk of having a prostate cancer diagnosis that would never have been clinically detected prior to death due to another cause; prostate cancer mortality rate; and life expectancy.
We predicted these measures for a policy that replaces the current testing pattern see Fig 2 with regular prostate cancer testing during ages 55—69 years: regular testing was introduced from at age 55 years for those born in and in later birth cohorts. After 15 years, regular testing had replaced current testing for ages 55—69 years. For each policy million life histories were simulated.
We assumed that preferences for PSA testing did not change with the introduction of regular testing, such that only men who would undertake testing under current testing would participate in regular testing [ 19 , 20 ]. Our modelling of organised screening specifically addresses the effect of screening intensity for the targeted age groups.
In Fig 6 we predicted prostate cancer incidence and overdiagnosis rate ratios for 20 years of 2-yearly testing, 8-yearly testing and the complete cessation of asymptomatic testing in comparison with the current testing pattern. The 2-yearly testing scenario resulted in a small reduction, RR 0.
The less intensive 8-yearly testing scenario substantially reduced the prostate cancer incidence, RR 0. The hypothetical cessation of all PSA testing for asymptomatic men in would result in a substantial decrease, RR 0. The changes in testing policy were introduced in for a population reflecting the Swedish age-structure. The purpose of early detection for prostate cancer is to lower prostate cancer mortality and increase the life expectancy. To assess these effects, we predicted mortality rates and life-years gained for the different PSA testing policies Fig 7.
Both the mortality rates and the life-years gained were expressed relative to the current PSA testing pattern. We predict that the broad introduction across the — birth cohorts contributed to the mortality reduction, which, while wearing off towards the end of the 20 year period, causes an increase in mortality, particularly for the 8 yearly testing.
The relative effect on the mortality is considerably smaller than the effect on the incidence and while the 2 yearly testing pattern has a similar mortality, RR 0. Similarly the 2 yearly testing pattern slightly increased the life expectancy, 0. The hypothetical scenario of cessation of PSA testing for asymptomatic men in was predicted to significantly increase prostate cancer mortality over 20 years, RR 1. The shifts in testing policy was introduced on a population reflecting the Swedish age-structure.
The modest mortality reductions are potentially explained by relatively high levels of testing under the current PSA testing, and the use of the currently observed biopsy compliance for all predicted scenarios. These reductions are also comparable to the non-significant mortality reduction found in the Prostate, Lung, Colorectal and Ovarian PLCO cancer screening trial [ 8 ], where there were high levels of PSA testing in the control arm [ 21 ]. Our aim was to develop, calibrate and validate a prostate cancer natural history model that could be used to evaluate prostate cancer testing.
Using extensive Swedish data resources, we extended an older US-calibrated prostate cancer natural history model for the Swedish population and validated the new model.
We then used the revised model to predict longer-term patterns of prostate cancer incidence and mortality in Sweden. One of the challenges with natural history models is finding a model that is biologically meaningful and representative, whilst being mathematically simple and potentially estimable. Investigators are divided in how to resolve these challenges. One school uses very simple models with expert judgement for the effectiveness of interventions. The validity of the predictions depend on the accuracy of the experts. A second school uses Markov models fitted to evidence from randomised controlled trials RCTs to assess the effectiveness of specific interventions within the follow-up from the RCTs.
The validity is limited by the available RCT evidence, with strong limitations for predicting outside of the observed data. A third school uses more detailed natural history models and simulate for individuals. The validity of the predictions primarily reflects the validity of the natural history model.
We are firmly in the last of these three schools. We have previously modelled cancer screening using both simple and more complex Markov models, and found issues with validity for the simple models and issues with model complexity for scaling more detailed Markov models to combinations of natural history and test states by time in state [ 22 ].
One potential criticism of many microsimulation models for cancer screening is that their complexity is coupled with a lack of model detail and that the source is usually closed. FHCRC are available on request. We encourage other microsimulation modellers to make their code openly available, which will lower the entry requirements for other investigators.
If the cost of entry remains high, then a closed source consulting model will continue to be predominant. There are several potential limitations.
First, the revised natural history model was less accurate for modelling age dependent incidence e. More accurate modelling at older ages would require a more detailed natural history model. Second, it is difficult to assess whether the natural history model is causal and accurate: the disease process is only partially observed and the biology represented using a simple mathematical representation. Third, the prediction of age-standardised mortality rates were slightly lower than that observed in the Swedish population. This underestimation could be due to e. Nonetheless, we expect that our predictions will have strong internal validity, as the simulations allow for carefully controlled experimental conditions.
Strengths of our approach include the wealth of detailed longitudinal data available from Sweden, and that we have made the model open source. Our natural history model can support an evidence-based approach to assessing whether the introduction of organised re-testing or screening would be effective and cost-effective. Since that time, both the Stockholm Prostata model and the FHCRC model have incorporated a number of similar extensions, including T-stage development and more detailed modelling of Gleason grading [ 5 ]. A key difference is that the updated FHCRC model includes a two-parameter model for cancer onset [ 24 ].
We are currently investigating whether to incorporate these extensions into the Stockholm Prostata model. A key advantage of the Stockholm Prostata model is the availability of detailed longitudinal data on PSA values and prostate biopsies linked with clinical outcomes.
In contrast the Swedish registry data have high coverage and are well suited for modelling the disease progression and treatment pathways within men. Our choice of modelling approach included model calibration for some key parameters in both unscreened and screened populations Table 1. To assess whether the adapted model was valid for Sweden, we compared the model predictions with observed population incidence.
This approach demonstrates both the strengths and potential weaknesses of our model. Our model is now well suited to the health economic evaluation of new prostate cancer screening tests. Those preferences are expected to affect the effectiveness of new prostate cancer screening interventions in populations with established PSA testing patterns. From the section on Model predictions, we found evidence to suggest that organised screening would reduce overdiagnosis without increasing mortality compared to current screening practices.
Future work is needed to investigate refined screening strategies and the evaluation of cost-effectiveness. In this section, we will describe the various data sources used to develop the model, explain the model formulation, outline the methods for the calibration and validation of the model, and finish with a description of the model implementation.