Risk stratification is essential for comprehensive patient counselling and evidence-based decision-making. As the PSA level is limited by poor specificity and the DRE by poor sensitivity, algorithms that estimate an individual's risk of having prostate cancer detected might aid the decision to have a prostate biopsy. In the present study of 1108 men, we externally validated the PCPT risk calculator, comparing its performance to PSA alone and to a novel, logistic regression-based model which included %fPSA and number of biopsy cores. The risk calculator gave a modest improvement in the performance characteristics of PSA alone in predicting an individual's risk of prostate cancer or high-grade disease. Our novel regression-based model further improved the predictive accuracy, with AUCs of 71.2% and 78.7% for predicting prostate cancer and high-grade disease, respectively.
Not surprisingly, multivariable regression showed that increasing age, increasing PSA level, low %fPSA, abnormal DRE, African-American race, positive family history, increasing number of biopsy cores and no previous negative prostate biopsy indicate an increasing risk of detecting prostate cancer and high-grade disease on biopsy. It was also apparent that adding a more specific PSA variant (%fPSA) enhanced the AUC compared with the PCPT risk calculator, and that the probability of finding prostate cancer on biopsy depends on the extent of sampling. Our model was similar to previously reported regression-based nomograms and artificial neural networks, the AUCs for which are 69–88% [5
The study by Thompson et al.
] reported AUCs for predicting prostate cancer of 70.2% and 67.8% for the risk calculator and PSA alone, respectively; the AUC for the risk of high-grade disease was 69.8% [21
]. They clearly stated `the independent risk factors of family history, DRE result, and previous prostate biopsy did not appreciably improve the sensitivity and specificity of PSA level.' In the external validation using the SABOR cohort [22
], the AUC for the risk calculator was 65.5%, similar to that for PSA alone (64.0%). Despite a modest to no improvement over the predictive accuracy of PSA alone, both reports advocate the widespread use of this online calculator `incorporating the current best panel of risk factors' [21
]. We show here that the PCPT risk calculator again performs with only a modest improvement over PSA alone, and that %fPSA and number of biopsy cores can be included in the panel of risk factors, as each improves on the predictive accuracy.
We also found that the PCPT risk calculator tended to overestimate the risk of high-grade disease, especially in older, African-American men. For example, an 85-year-old African-American man with a normal DRE, no family history, no previous biopsy and a serum PSA level of 6.0 ng/mL had calculated risks of prostate cancer and high-grade disease of 43% and 41%, respectively. This suggests that the risk of this man having Gleason ≤6 disease is only 2%. If that same man's PSA level was 8.0 ng/mL, his corresponding calculated risks would be 49% and 50%. Interestingly, in the present study cohort, there were 13 (1.2%) men whose calculated risk of high-grade disease was greater than their calculated risk of having any prostate cancer. This inaccuracy might stem from a lack of African-American men and men with elevated serum PSA levels in the PCPT cohort.
Limitations from our retrospective study include the potential for selection bias. Despite the many men assessed from several institutions, the cohort might not be representative of the general population of the USA undergoing screening for prostate biopsy. Men in the present cohort were referred for possible prostate biopsy and their characteristics might be different from those of a screening population. In addition, verification bias might also be present; as the present men were enrolled for clinical indications (unlike in the PCPT, in which end-of-study biopsies were taken), the operating characteristics of PSA were evaluated in a cohort over-represented by men with an elevated PSA level. Thus, our model is ideal for men identified at high risk and referred for possible prostate biopsy. As always, prostate biopsies are limited by the extent of sampling and do not perfectly reflect the true disease status, as up to 30% of clinically significant cancers might be missed on biopsy [25
]. In the present study, family history was defined as positive or negative, with no information on the degree of relation to the individual whose risk was calculated. It might be possible that men enrolled in a prostate cancer study could report a higher incidence of a positive family history (potentially in a more distant relative); this effect would underestimate the effect of having a first-degree relative with prostate cancer. Information on the timing of the previous negative biopsy relative to the repeat biopsy was not available and was thus not considered.
However, the present cohort more closely resembles the target population (of men and of clinically significant tumours). The PCPT cohort used to create the risk calculator is likely to be enriched in small-volume, low-grade cancers, as it excluded men who had a PSA level of >3.0 ng/mL at enrolment. In fact, only 631 (11.4%) had a PSA level of >4.0 ng/mL at the time of biopsy. Another important distinction is in the biopsy regimen, as ≈80% of the men in the PCPT study had sextant biopsies, whereas in the present study only 10.5% had six or fewer cores and 84.8% had ≥10 cores taken. As the PCPT predictive model was based on sextant biopsy information, it must be updated according to the current standard of extended biopsy schemes. In our multivariable model, the number of biopsy cores was indeed found to be an independent predictor of detecting prostate cancer and high-grade disease, and thus is more appropriate for use in contemporary men.
Since the introduction of PSA screening, there has been a significant increase in the proportion of men newly diagnosed with clinically localized, low-risk prostate cancer. Although PSA remains the most commonly used serum biomarker for prostate cancer, it has clearly led to an increasing number of prostate biopsies, and the over-diagnosis and over-treatment in some cases of prostate cancer. As indicated by the results of the PCPT and others, a single PSA value cannot accurately identify men with and without prostate cancer. PSA is instead associated with a range of risk and there is no lower limit at which there is no risk of prostate cancer. Thus, the interpretation of an individual PSA value remains a distinct challenge. The importance of clinical recommendations using PSA levels is amplified by the potential impact on millions of men annually.
Certainly, the decision to take a prostate needle biopsy should integrate numerous clinicopathological features, in addition to the most recent PSA level, including the age, race, PSA kinetics, %fPSA, DRE results, family history, previous needle biopsy findings, and psychosocial factors such as the degree of anxiety and aversion to a cancer diagnosis, treatment or complications. Until molecular biomarkers with improved operating characteristics are developed, clinicians and patients will often be faced with a difficult decision due to flawed and limited prognostic information. This can be improved by multivariable, regression-based prediction tools, which provide a relatively bias-free estimate of an individual's risk with improved predictive accuracy, as compared to mental predictions even by expert clinicians [19
We found that the PCPT risk calculator modestly improves the performance of PSA alone in predicting an individual's risk of prostate cancer or high-grade disease on biopsy. Our novel regression-based model had an improved predictive ability by incorporating %fPSA and number of biopsy cores.