898 1030
colleagues
[7]showed that prostate MRI as a primary
screening test was better in predicting prostate cancer than
PSA measurement. In addition, a meta-analysis revealed
that prostate MRI and subsequent MRI-targeted prostate
biopsies improved the diagnostic accuracy for significant
prostate cancer
[8]. More recently, Ahmed and colleagues
[9]showed in a prospective multicenter study that mpMRI,
used as a triage test for prostate biopsy in men presenting
with elevated PSA, improved the accuracy of prostate
biopsies for significant prostate cancer, and allowed a
quarter of all biopsies to be avoided. Given these findings, it
seems more than reasonable to incorporate mpMRI results
into prostate cancer RCs.
However, what price are we willing to pay for improved
risk prediction? Should mpMRI become a standard investi-
gation for all men with elevated PSA or a suspicious DRE?
The use of this approach would take advantage of both of the
above-mentioned benefits of mpMRI. However, although
fewer unnecessary biopsies would be performed and fewer
men with significant prostate cancer would be missed, a
relevant number of men would undergo unnecessary
mpMRI, which is a time-consuming and costly investigation.
Should we not instead evaluate a stepwise and probably
more cost-effective work-up for our patients and health
systems? Alberts and colleagues
[10]have recently shown
that half of all mpMRI scans could be avoided in men with a
previous biopsy if RC-based patient selection was per-
formed. If upfront selection recommends further work-up,
MRI findings could still be incorporated into risk prediction
models to decide whether to perform a biopsy or not.
The results presented in the current study nicely
illustrate the benefit of improved risk prediction by addition
of mpMRI results. However, given the comparable perfor-
mance of the novel RM and the RM based on the ERSPC RCs
and PI-RADS data, it remains unclear which RM should be
used in clinical practice. External validation of the two RMs
would probably help to determine the preferred model for
risk prediction. However, the current study does not give
answer whether we should simply add PI-RADS data to a
well-validated and robust RC that is based on historical data
(ie, ERSPC RC) or if it would be better to use novel RCs, which
include mpMRI data and are based on contemporary clinical
but rather small patient cohorts.
The work by Radke and colleagues shows us how
important it is to work on the development of next-
generation prostate cancer prediction models based on
contemporary cohorts, up-to-date biopsy regimens, and
promising novel parameters. It should motivate us to
support large-scale multi-institutional databases (eg, of
the Prostate Biopsy Collaborative Group). These projects are
most likely to provide data robust enough to determine the
best pathways and optimal RMs for prostate cancer risk
prediction in the future.
Conflicts of interest:
The authors have nothing to disclose.
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