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our novel tool would allow clinicians to estimate the risk of
LNI in men with low- and intermediate-risk disease, and
identify those who should be considered for this staging
procedure. However, our nomogram might not be able
to completely replace currently available models .
Indeed, it is based on detailed biopsy data obtained at
the central pathologic review performed by high-volume
uropathologists. These include information on the presence
of different Gleason scores at biopsy that might not
be routinely available. Currently available tools to predict
the risk of LNI still play an important role when these data
are not available.
Several aspects of our study are noteworthy. First, we
relied on a large cohort of contemporary patients treated at
a tertiary center with complete data. Second, all patients
received an anatomically defined ePLND. This procedure is
associated with a higher detection rate as compared with
limited approaches and would reduce the risk of false
negative results at final pathology . Finally, all patients
underwent biopsy specimen review performed by a high-
volume dedicated uropathologist. This would reduce the
risk of disease upgrading at final pathology, improving our
ability to identify men at a higher risk of LNI
Despite several strengths, our study is not devoid of
limitations. Information included in our nomogram might
not be routinely available, where only 24% of patients
undergoing RP at our center over the study period had
complete data. This might preclude its applicability to other
clinical contexts It should also be noted that the
excellent calibration characteristics of our model might be
related to the use of internal validation . Although
external validation is needed to confirm the validity of our
nomogram in other settings, the inclusion of patients with
detailed biopsy data re-reviewed by two high-volume
uropathologists precluded us to validate our model in an
external cohort with available biopsy information. Similar-
ly, the need for detailed biopsy information might limit the
diffusion of our novel tool in the community setting.
Moreover, although the characteristics of individuals with
complete biopsy data available after a pathologic review
and those of patients who were excluded due to missing
information did not statistically differ, the inclusion of
highly selected patients might have introduced a bias. In
addition, identification of LNI might vary depending on the
pathologic technique used and the experience of the
pathologist. We tried to circumvent this limitation by
including exclusively patients evaluated by high-volume
uropathologists at a single center. The lack of data on the
presence of tertiary grade patterns at prostate biopsy
prevented us to assess the impact of this variable on the risk
of LNI, where previous studies suggested that it might
predict biochemical recurrence after RP . Finally, our
nomogram does not include data on multiparametric
magnetic resonance imaging or genomic classifiers. Al-
though they represent promising tools in the prediction of
adverse pathologic outcomes and in the identification of
patients at a higher risk of recurrence , they still need
validation for LNI prediction. Therefore, further studies are
needed to assess this issue.
5. Conclusions
An ePLND should be avoided in patients with detailed
biopsy information and a risk of nodal involvement below
7%, according to our newly developed nomogram to
predict LNI in order to spare approximately 70% ePLNDs at
the cost of missing only 1.5% patients with node-positive
PCa.
Author contributions : Alberto Briganti had full access to all the data in the
study and takes responsibility for the integrity of the data and the accuracy
of the data analysis.
Study concept and design: Briganti, Gandaglia.
Acquisition of data: Bandini, Fossati, Bravi, Fallara, Pellegrino, Nocera.
Analysis and interpretation of data: Gandaglia, Briganti, Montorsi.
Drafting of the manuscript: Gandaglia, Briganti.
Critical revision of the manuscript for important intellectual content:
Briganti, Montorsi, Karakiewicz, Freschi, Montironi.
Statistical analysis: Gandaglia, Zaffuto, Tian, Dell’Oglio.
Obtaining funding: Montorsi.
Administrative, technical, or material support: None.
Supervision: Briganti, Montorsi, Karakiewicz, Freschi, Montironi.
Other: None.
Financial disclosures: Alberto Briganti certifies that all conflicts of
interest, including specific financial interests and relationships and
affiliations relevant to the subject matter or materials discussed in the
manuscript (eg, employment/affiliation, grants or funding, consultan-
cies, honoraria, stock ownership or options, expert testimony, royalties,
or patents filed, received, or pending), are the following: None.
Funding/Support and role of the sponsor: None.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at
.
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