75 research outputs found
Algorithms, nomograms and the detection of indolent prostate cancer
Purpose: Prostate cancer is the most commonly diagnosed cancer in men. However, only about 12% of the men diagnosed with prostate cancer will die of their disease. Result: The serum PSA test can detect prostate cancers early, but using a PSA based cut-off indication for prostate biopsy results in unnecessary testing in app. 75-80% of the men and perhaps even more important the serum PSA test cannot tell how aggressive the cancer is. To decrease unnecessary testing different test results are often combined, converted into a probability and displayed graphically. There are more than 40 of these so called nomograms in the case of prostate cancer. These nomograms can be divided into two categories, namely those that predict biopsy outcome using results from serum determination(s) or non-invasive tests such as the DRE and TRUS. The second category represents those nomograms that predict tumor characteristics and prognosis using information coming from pathology review. Conclusion: The ultimate nomogram able to predict tumor characteristics and progression purely based on non-invasive testing will for a large part put an end to the negative side effects and uncertainties that coincide with the early detection of prostate cancer, if it will ever be made
Ethnicity and prostate cancer: The way to solve the screening problem?
In their analysis in BMC Medicine, Lloyd et al. provide individual patient lifetime risks of prostate cancer diagnosis and prostate cancer death stratified by ethnicity. This easy to understand information is helpful for men to decide whether to start prostate-specific antigen testing (i.e. screening). A higher lifetime risk of prostate cancer death in some ethnic groups is not automatically a license to start screening. The potential benefit in the form of reducing metastases and death should still be weighed against the potential risk of over diagnosis. In case of ethnicity, this harm-to-benefit ratio does not differ between groups. Stratifying men for screening based on ethnicity is therefore not optimal and will not solve the current screening problem. Other methods for risk-stratifying men have been proven to produce a more optimal harm-to-benefit ratio
Case-control studies in evaluating prostate cancer screening: an overview
Objectives: Ongoing randomized controlled screening trials for prostate
cancer have not shown a beneficial effect on prostate cancer mortality
reduction yet. A large number of observational (non-randomized) studies
on prostate cancer screening have been published with contradictory
outcome. This paper reviews the current case-control studies.
Methods: Seven case-control studies of screening for prostate cancer
were identified in a PubMed search, published from 1991 onwards, all
conducted in North America. The screening test was either digital rectal
examination (DRE) alone or in combination with PSA.
Results: One DRE case-control study, found a significant preventive
effect, whereas two others showed no effect of DRE screening on prostate
cancer mortality nor on the occurrence of metastatic disease. Conflicting
results were also observed in the studies assessing the effect of PSA/DRE.
Only one study showed a significant 27% mortality reduction in the
White male cohort, but found no effects in Blacks. The most recent
study showed that screening with PSA/DRE was not protective in reducing
prostate cancer mortality.
Conclusions: Our review of the case-control studies does not indicate a
benefit of prostate cancer screening. An answer has to come from the
ERSPC trial, in Europe, and the PLCO trial, in the US, of which the
outcomes are expected in 2007–2010.
# 2006 European Association of Urolog
Personalized strategies in population screening for prostate cancer
This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment
Prostate cancer screening: tests and algorithms
Although the concept of early detection of cancer sounds intuitively logical it is
not automatically so in the case of prostate cancer despite the fact that the
data on incidence and mortality show that it is an important health problem.
The fact that prostate cancer is in general a slow growing tumor mainly in
elderly men raises the question whether early detection and available
treatment (with related morbidity) will improve prostate cancer specific survival.
The identification of PSA as a diagnostic tool, and an increased awareness of
the disease by patients and doctors resulted in an increase in incidence of
prostate cancer. Whether such early detection and treatment of prostate
cancer will save lives can only be answered by a well performed randomized
controlled trial. The European Randomized study of Screening for Prostate
Cancer (ERSPC) is a multi centre study that has the power to investigate the
impact of screening for prostate cancer on disease specific mortality. The
ERSPC also provides a means to study the performance of screening tests in
identifying men with an elevated risk of having prostate cancer in an
asymptomatic population. This thesis concentrates on this subject
Prostate cancer screening in Europe and Asia
Prostate cancer (PCa) is the second most common cancer among men worldwide and even ranks first in Europe. Although Asia is known as the region with the lowest PCa incidence, it has been rising rapidly over the last 20 years mostly due to the introduction of prostate-specific antigen (PSA) testing. Randomized PCa screening studies in Europe show a mortality reduction in favor of PSA-based screening but coincide with high proportions of unnecessary biopsies, overdiagnosis and subsequent overtreatment. Conclusive data on the value of PSA-based screening and hence the balance between harms and benefits in Asia is still lacking. Because of known racial variations, Asian countries should not directly apply the European screening models. Like in the western world also in Asia, new predictive markers, tools and risk stratification strategies hold great potential to improve the early detection of PCa and to reduce the worldwide existing negative aspects of PSA-based PCa screening
Mapping polygons to the grid with small Hausdorff and Fréchet distance
We show how to represent a simple polygon P by a grid (pixel-based) polygon Q that is simple and whose Hausdorff or Fréchet distance to P is small. For any simple polygon P, a grid polygon exists with constant Hausdorff distance between their boundaries and their interiors. Moreover, we show that with a realistic input assumption we can also realize constant Fréchet distance between the boundaries. We present algorithms accompanying these constructions, heuristics to improve their output while keeping the distance bounds, and experiments to assess the output
Improving prediction models with new markers: A comparison of updating strategies
Background: New markers hold the promise of improving risk prediction for individual patients. We aimed to compare the performance of different strategies to extend a previously developed prediction model with a new marker. Methods: Our motivating example was the extension of a risk calculator for prostate cancer with a new marker that was available in a relatively small dataset. Performance of the strategies was also investigated in simulations. Development, marker and test sets with different sample sizes originating from the same underlying population were generated. A prediction model was fitted using logistic regression in the development set, extended using the marker set and validated in the test set. Extension strategies considered were re-estimating individual regression coefficients, updating of predictions using conditional likelihood ratios (LR) and imputation of marker values in the development set and subsequently fitting a model in the combined development and marker sets. Sample sizes considered for the development and marker set were 500 and 100, 500 and 500, and 100 and 500 patients. Discriminative ability of the extended models was quantified using the concordance statistic (c-statistic) and calibration was quantified using the calibration slope. Results: All strategies led to extended models with increased discrimination (c-statistic increase from 0.75 to 0.80 in test sets). Strategies estimating a large number of parameters (re-estimation of all coefficients and updating using conditional LR) led to overfitting (calibration slope below 1). Parsimonious methods, limiting the number of coefficients to be re-estimated, or applying shrinkage after model revision, limited the amount of overfitting. Combining the development and marker set using imputation of missing marker values approach led to consistently good performing models in all scenarios. Similar results were observed in the motivating example. Conclusion: When the sample with the new marker information is small, parsimonious methods are required to prevent overfitting of a new prediction model. Combining all data with imputation of missing marker values is an attractive option, even if a relatively large marker data set is available
Comparison of clinically significant prostate cancer detection by MRI cognitive biopsy and in-bore MRI-targeted biopsy for naïve biopsy patients
Background: Multiparametric magnetic resonance imaging (mpMRI) targeted prostate biopsy increases the diagnostic accuracy of clinically significant prostate cancer (PCa). Currently there is no consensus on which type of MRI-targeted biopsy performs better in a given setting. In this study, we aimed to compare the detection rate of (clinically significant) PCa by MRI cognitive targeted biopsy (COG) and in-bore MRI-targeted biopsy (IB) techniques for naïve prostate biopsy patients in China. Methods: Our study included 85 men from Beijing United Family Hospital and Clinics and 88 men from Beijing Hospital, National Center of Gerontology. All men had no history of prostate biopsy, undergoing mpMRI scan due to elevated PSA and/or abnormal DRE. The men in Beijing United Family Hospital group received COG plus systematic biopsy. The men in Beijing Hospital group only received IB. Results: The median age in COG and IB group was 63.0 years and 70.0 years (P<0.01). The median PSA was 7.4 and 6.8 ng/mL in COG and IB group respectively (P=0.124). The detection rate of PCa was 36.5% by COG and 52.3% by IB (P=0.037). The detection rate of clinically significant PCa (Gleason score ≥7) was 23.5% and 29.5% by COG and IB (P=0.371) respectively. In COG group, combination biopsy (COG + systematic biopsy) achieved improved PCa (42.4%) and clinically s
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