An updated catalog of prostate cancer predictive tools

Abstract

Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with prostate cancer. Accurate risk estimates also are required for clinical trial design to ensure that homogeneous, high-risk patient groups are used to investigate new cancer therapeutics. Using the MED-LINE database, a literature search was performed on prostate cancer predictive tools from January 1966 to July 2007. The authors recorded input variables, the prediction form, the number of patients used to develop prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed. Each prediction tool was classified into patient clinical disease state and the outcome being predicted. First, the authors described the criteria for evaluation (predictive accuracy, calibration, generalizability, head-to-head comparison, and level of complexity) and the limitations of current predictive tools. The literature search generated 109 published prediction tools, including only 68 that had undergone validation. An increasing number of predictive tools addressed important endpoints, such as disease recurrence, metastasis, and survival. Despite their limitations and the limitations of data, predictive tools are essential for individualized, evidence-based medical decision making. Moreover, the authors recommend wider adoption of risk-prediction models in the design and implementation of clinical trials. Among prediction tools, nomograms provide superior, individualized, disease-related risk estimations that facilitate management-related decisions. Nevertheless, many more predictive tools, comparisons between them, and improvements to existing tools are needed.

Multi-institutional Validation of the CAPRA-S Score to Predict Disease Recurrence and Mortality After Radical Prostatectomy

“…Risk assessment tools that can accurately predict cancer recurrence and progression would allow a more timely institution of additional treatments that might be beneficial for selected patients [23][24][25]. Among the 109 published prediction tools [3], only 8 take advantage of pathologic information gained from RP, and very few of these tools have been validated. Among these nomograms, the updated Stephenson postoperative nomogram is well known and commonly referenced and thus was used for comparison with CAPRA-S [17].…”

“…Numerous nomograms exist to characterize patients by disease risk to facilitate clinical decision making [3]. Most of these nomograms rely on various pretreatment variables to predict the likelihood of disease recurrence.…”

Management of Biochemical Recurrence After Primary Treatment of Prostate Cancer: A Systematic Review of the Literature

“…Differentiating between the two, although difficult, is critical, because men with local recurrence only are excellent candidates for salvage local treatment with curative intent, whereas those with distant failure may require systemic therapy and are unlikely to have a durable response to local treatment alone. Numerous riskprediction models are available to characterize disease risk and determine the probability of recurrence after primary treatment [20]. In a recent update on PCa predictive tools, 17 nomograms that predict the likelihood of metastasis and survival were identified [20].…”

“…Numerous riskprediction models are available to characterize disease risk and determine the probability of recurrence after primary treatment [20]. In a recent update on PCa predictive tools, 17 nomograms that predict the likelihood of metastasis and survival were identified [20]. However, these nomograms displayed a predictive accuracy of 59-93%, suggesting a level of certainty somewhere between a near-perfect score and a coin toss, and only three had been externally validated.…”

Cancer/testis antigens: novel tools for discerning aggressive and non-aggressive prostate cancer

“…[9][10][11][12][13][14][15][17][18][19][20] Although these clinicopathological variables are useful in predicting biochemical recurrence, these prognostic indicators do not accurately predict clinical outcome for individual patients.…”