Nomograms and instruments for the initial prostate evaluation: the ability to estimate the likelihood of identifying prostate cancer.
ABSTRACT As a result of prostate cancer screening programs, approximately 10% of otherwise healthy men will be found to have an elevated prostate-specific antigen (PSA) level and therefore be at risk for harboring prostate cancer. Patients with an elevated PSA level have a wide variation in the risk for having prostate cancer diagnosed by transrectal ultrasound (TRUS)-guided prostate biopsy. To adequately counsel these patients, some form of individualized risk assessment must be given. There are several tables, artificial neural network (ANN) models, and nomograms that are available to stratify an individual patients risk for having prostate cancer diagnosed by a TRUS biopsy, either initially or on subsequent biopsies after a previous negative biopsy. Presently, nomograms are also being developed to predict the risk not only for having prostate cancer but also for clinically significant prostate cancer. The difficulty in calculating this risk for an individual patient is that the multiple competing clinical and pathologic factors have varying degrees of effect on the overall risk. This problem of competing risk factors can be overcome by the use of nomograms or ANNs. This article reviews the available instruments that are available to the urologist to enable prediction of the risk for having prostate cancer diagnosed by TRUS-guided prostate biopsy.
SourceAvailable from: Parminder Raina[Show abstract] [Hide abstract]
ABSTRACT: This systematic review aimed to evaluate, within unselected populations: the (1) performance of family history (FHx)-based models in predicting cancer risk; (2) overall benefits and harms associated with established cancer prevention interventions; (3) impact of FHx-based risk information on the uptake of preventive interventions; and (4) potential for harms associated with collecting cancer FHx. MEDLINE, EMBASE, CINAHL, Cochrane Central, Cochrane Database of Systematic Reviews, and PsycINFO were searched from 1990 to June 2008 inclusive. Cancer guidelines and recommendations were searched from 2002 forward and systematic reviews from 2003 to June 2008. Standard systematic review methodology was employed. Eligibility criteria included English studies evaluating breast, colorectal, ovarian, or prostate cancers. Study designs were restricted to systematic review, experimental and diagnostic types. Populations were limited to those unselected for cancer risk. Interventions were limited to collection of cancer FHx; primary and/or secondary prevention interventions for breast, colorectal, ovarian, and prostate cancers. Accuracy of models. Seven eligible studies evaluated systems based on the Gail model, and on the Harvard Cancer Risk Index. No evaluations demonstrated more than modest discriminatory accuracy at an individual level. No evaluations were identified relevant to ovarian or prostate cancer risk. Efficacy of preventive interventions. From 29 eligible systematic reviews, seven found no experimental studies evaluating interventions of interest. Of the remaining 22, none addressed ovarian cancer prevention. The reviews were generally based on limited numbers of randomized or controlled clinical trials. There was no evidence either to support or refute the use of selected chemoprevention interventions, there was some evidence of effectiveness for mammography and fecal occult blood testing. Uptake of intervention. Three studies evaluated the impact of FHx-based risk information on uptake of clinical preventive interventions for breast cancer. The evidence is insufficient to draw conclusions on the effect of FHx-based risk information on change in preventive behavior. Potential harms of FHx taking. One uncontrolled trial evaluated the impact of FHx-based breast cancer risk information on psychological outcomes and found no evidence of significant harm. Our review indicates a very limited evidence base with which to address all four of the research questions: 1) the few evaluations of cancer risk prediction models do not suggest useful individual predictive accuracy; 2) the experimental evidence base for primary and secondary cancer prevention is very limited; 3) there is insufficient evidence to assess the effect of FHx-based risk assessment on preventive behaviors; 4) there is insufficient evidence to assess whether FHx-based personalized risk assessment directly causes adverse outcomes.Evidence report/technology assessment 04/2009;
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ABSTRACT: Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower in Chinese men, nomograms based on other populations cannot be directly applied to Chinese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Chinese male population. Data were collected from 893 Chinese male referrals, 697 in the derivation set and 196 in the external validation set, who underwent initial prostate biopsies as individual screening. We analyzed age, prostate volume, total prostate-specific antigen (PSA), PSA density (PSAD), digital rectal examinations (DRE) and transrectal ultrasound (TRUS) echogenicity. Logistic regression analysis estimated odds ratio, 95% confidence intervals and P values. Independent predictors of a positive biopsy result included advanced age, small prostate volume, elevated total PSA, abnormal digital rectal examination, and hyperechoic or hypoechoic TRUS echogenicity. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver-operating characteristic curve for the model was 88.8%, which was greater than that of the prediction based on total PSA alone (area under the receiver-operating characteristic curve 74.7%). If externally validated, the predictive probability was 0.827 and the accuracy rate was 78.1%, respectively. Incorporating clinical and laboratory data into a prebiopsy nomogram improved the prediction of prostate cancer compared with predictions based solely on the individual factors.Asian Journal of Andrology advance online publication, 14 October 2013; doi:10.1038/aja.2013.100.Asian Journal of Andrology 10/2013; 15(6). DOI:10.1038/aja.2013.100 · 2.53 Impact Factor
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ABSTRACT: Artificial neural networks (ANN) represent an interesting alternative methodological approach to predict prostate cancer related outcomes on an individual basis. Constructed by flexible, nonlinear regression models, they have the potential to offer enhanced goodness-of-fit and predictive ability over traditional linear models. However, this potential comes at the price of increased complexity in modeling building and validation. Therefore, despite their promotion in the field of prostate cancer since the early 1990 s, due to less transparency and the need for computational infrastructure, ANNs have become less popular. Their utility hinges on appropriate implementation and validation, which unfortunately is only recently being recognized and addressed. In this chapter, the reader is provided with a descriptive and an analytic tabulation of decision aid criteria for ANNs for prostate cancer detection.