Critical review of prostate cancer predictive tools

Abstract

Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.

Fig 1

Decision curve for a statistical prediction tool predicting the outcome of prostate biopsy. The thin grey line is the net benefit of biopsying all men; the thin black line is the net benefit of biopsying men on the basis of the statistical prediction tool; the thick black line is the net benefit of biopsying no man. This curve was derived from 740 men undergoing biopsy based on an elevated total PSA. The men had never been previously screened for prostate cancer. Their free PSA was measured and a digital rectal exam was performed. Approximately one-quarter (192) of the men were diagnosed with cancer. Interpretation of the decision curve depends on comparing the net benefit of the test, prediction tool, or marker with that of a strategy of “treat all” (the thin grey line) and “treat none” (parallel to the x axis at net benefit of zero). The strategy with the highest net benefit at a particular threshold probability (pt) is optimal, irrespective of the size of the difference. Determining which men should be biopsied using the statistical prediction tool was superior to biopsying all men with elevated PSA once the threshold probability reached about 10%, and was superior to the strategy of biopsying no man up to a threshold probability of about 90%. To interpret this result, one needs to consider the sort of probability for prostate cancer that men would need before they would decide to have a biopsy. A very risk-averse man might opt for biopsy even if he had only a 10% risk of cancer. However, it seems unlikely that many men would demand, say, a 50% risk of cancer before they had a biopsy; this threshold would imply that an unnecessary biopsy is just as bad as a missed cancer. Reprinted with permission from Vickers et al. [46]

Fig 2

A) Pre-operative nomogram estimating the 1- to 10-year biochemical recurrence-free probability after radical prostatectomy alone. B) Calibration plot of the nomogram in external validation. The 45° line represents an ideal prediction tool, in which estimates of recurrence are perfectly calibrated with outcome. Vertical bars are 95% confidence intervals for quintiles in the validation set. Reprinted with permission from Stephenson et al [37].

Fig 3

Post-operative nomogram predicting 10-year biochemical recurrence-free probability after radical prostatectomy. Reprinted with permission from Stephenson et al [36].

Fig 4

Pre-treatment nomogram for predicting 5-year biochemical recurrence–free probability after three-dimensional conformal radiation therapy (3D-CRT). Reprinted with permission from Kattan et al [81]. XRT= external beam radiation therapy.

Fig 5

Pre-treatment nomogram for predicting 5-year biochemical recurrence–free probability after permanent prostate brachytherapy without neo-adjuvant androgen ablative therapy. Reprinted with permission from Kattan et al [84].

Fig 6

Pre-treatment nomogram for predicting 5-year biochemical recurrence-free probability after radical prostatectomy including pre-operative plasma levels of transforming growth factor β1 and interleukin-6 soluble receptor. Reprinted with permission from Kattan et al [100].