Abstract
Objective
To evaluate the predictive value of magnetic resonance imaging (MRI)-defined prostate-specific
antigen (PSA) density and MRI interpretation for the detection of clinically significant
prostate cancer (PCa).
Methods
We retrospectively reviewed our institutional database of men who received prostate
MRI prior to biopsy at our institution between September 2014 and December 2016, excluding
those on active surveillance (n = 372). Logistic regression models to predict clinically
significant PCa on biopsy were developed using (1) MRI-defined PSA density; (2) PSA
alone; and (3) the Prostate Cancer Prevention Trial (PCPT) risk calculator. Additional
logistic regression models were then generated by combining the previous clinical
variables with MRI interpretation (ie, prostate imaging reporting and data system
[PI-RADS] classification): (1) MRI-defined PSA density + MRI interpretation; (2) PSA + MRI
interpretation; and (3) PCPT + MRI interpretation. Receiving operator characteristic
curves for each of the 6 models were generated, and the area under the curves (AUC)
were compared.
Results
MRI-defined PSA density (AUCPSAD = 0.77) significantly outperformed the PSA (AUCPSA = 0.66,
P < .01) and PCPT models (AUCPCPT = 0.70, P < .01). When combined with MRI interpretation (ie, PI-RADS classification), the MRI-defined
PSA density model (AUCPSAD + MRI = 0.80) significantly outperformed the PSA (AUCPSA + MRI = 0.75,
P < .01) and PCPT models (AUCPCPT + MRI = 0.76, P = .01).
Conclusion
In addition to the PI-RADS classification, prebiopsy multiparametric magnetic resonance
imaging also provides an accurate assessment of prostate volume. By utilizing this
additional data to determine MRI-defined PSA density, we find that risk discrimination
for clinically significant PCa on biopsy can be significantly improved.
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Article info
Publication history
Published online: December 20, 2018
Accepted:
December 11,
2018
Received:
July 20,
2018
Footnotes
Financial Disclosure: The authors declare that they have no relevant financial interests.
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
