Short Communication
Considerations Regarding the Predictive Value of PSA Testing, as a Diagnostic Tool for Prostate Cancer in the Current Clinical Practice
Spyropoulos E*
Department of Urology, Naval and Veterans Hospital of Athens, Greece
*Corresponding author: Spyropoulos E, Department of Urology, Naval and Veterans Hospital of Athens, Greece
Published: 20 Feb, 2017
Cite this article as: Spyropoulos E. Considerations
Regarding the Predictive Value of
PSA Testing, as a Diagnostic Tool for
Prostate Cancer in the Current Clinical
Practice. Clin Oncol. 2017; 2: 1209.
Short Communication
To date, more than 30 years after its discovery, serum Prostate Specific Antigen (PSA) testing,
has become the most widely recommended and clinically used predictive factor for identifying men
at increased risk of harbouring Prostatic Carcinoma (PCa) and is recognized as the best diagnostic
tool available for early diagnosis of the disease [1-10]. However, although PSA remains the most
commonly used serum biomarker for prostate cancer, in the past few years considerable ongoing
controversy has emerged regarding its diagnostic performance, mainly based on evidence indicating
that as currently used, this biomarker is insufficiently sensitive and specific as a diagnostic tool
for accurately identifying prostate cancer [3,6,10-15]. The major and most challenging problem
is the difficulty in differentiating prostate cancer from benign prostatic conditions given that, it
is organ-specific rather than tumor-specific biomarker and as such, there is considerable overlap
in PSA levels among men with prostate cancer and benign disease (benign prostatic hyperplasia,
prostatic inflammation, certain activities such as riding a bike or having sex that can trigger a
temporary increase in PSA). According to recent studies, it cannot be considered the ideal tumor
marker (limited by poor specificity)for early detection of PCa as, neither an increased serum PSA
is pathognomonic of prostate cancer nor, low levels necessarily confirm its absence so that, a single
PSA value cannot accurately identify men with and without prostate cancer and no lower limit
exists that can safely predict the absence of PCa [2,5,7,8,12,16-18]. Consequently, as there is no PSA
threshold below which PCa can be ruled out with high accuracy, making thus the interpretation
of an individual PSA value a distinct challenge, it is suggested that the alternative to the use of
cut-points is to accept that PSA is most useful as a continuous variable (risk varies continuously)
providing a spectrum of prostate cancer risk (there is a risk of PCa at all PSA values) and men with
very low levels of PSA can harbour PCa [5,10,13,15,17-21].
Nowadays, increasing prostate cancer awareness motivates many patients, who after an
abnormal PSA test result face the prospect of prostate biopsy, to seek the most objective information
regarding their likely outcomes (probability of harboring PCa). Similarly, clinicians wish to know
the odds of cancer diagnosis when prostate biopsy is recommended, in order to provide accurate
estimates of those outcomes [22,23]. Furthermore, the management of patients with persistently
elevated PSA levels after several negative prostate biopsies, represents a challenging daily problem
for urologists, who face the dilemma to determine who needs to undergo further diagnostic
procedures, depending on a greater number of biopsies with more extensive (up to saturation
biopsy) protocols [24]. Especially for these men, accurate estimates of the likelihood (risk) of cancer
diagnosis are important for patient counseling and informed decision-making and help physicians
to make specific management recommendations. However, in current clinical practice, absolute
PSA thresholds continue to be a central facet for recommending a prostate biopsy, policy resulting
in a high percentage (60% - 80%) of men with elevated PSA showing negative results on initial or
repeat prostate biopsy. Thus, applying a strategy of purely PSA-based indication, is associated with a
high percentage of men undergoing unnecessary multiple repeated prostate biopsies [7,9,19,24-27].
Avoidance of unnecessary prostate needle biopsies is crucial because these invasive procedures may
cause complications, potentially severe, such as discomfort, pain, infections, bleeding or urinary
obstruction, especially in geriatric patients with co-morbidities as well as significant psychological
(considerable anxiety) and emotional distress on the part of the patient and family, detrimental to
patient well-being, not to mention the economic (financial) cost [6,7,9,24,25,27-29].
Therefore, additional, accurate and noninvasive clinical tools (tests) to increase the probability of detecting PCa at biopsy and reduce the number of unnecessary
repeat biopsies, are needed [7,19,22,25,26,28,30]. These tools should
be based on patient stratification techniques (risk-based strategy) as,
currently, risk stratification is considered essential to identify those
men who are at increased risk of having PCa and therefore are proper
candidates for biopsy, as well as to reduce unnecessary biopsies and
over-diagnoses help physicians in making specific management
recommendations (evidence-based decision making) To this aim,
researchers developed several different risk based strategies in the
form of predictive statistical models, aiming to improve the diagnostic
performance of total PSA values ranging between 2.5 to 10.0 ng/mL
(extended diagnostic “gray zone”) [4,19,22,25,26,31]. All these tools
include serum total PSA in combination with several additional PCa
risk factors such as age, race, family history, number and histology
of previous negative biopsies, prostate volume, various molecular
forms of PSA (free PSA, % free/total PSA ratio) and PSA derivatives
such as age-specific (age-adjusted) total PSA, PSA velocity (PSA
kinetics) and total PSA Density (PSAD) [4,12,14,17,18,23,26,27,31].
These predictive “systems” (mathematical models) use statistical
techniques and/or advanced medical informatics to analyze data
from past clinical experience trying to make predictions about future
outcomes and include: Kattan-type nomograms, risk groupings,
Artificial Neural Networks (ANNs), probability tables (‘Partin
staging tables’), Classification And Regression Tree (CART) analyses,
probability formulas, look-up and propensity scoring tables, risk-class
stratification models and multivariate risk calculators [4,23,26,31,32].
Other technologies currently being utilized to improve the diagnosis
of PCa in case of abnormal PSA values, include: 1) The PCA3 test,
a molecular biology assay that measures the expression of PCA3
(prostate cancer gene 3) mRNA in urine samples. PCA3 is specific to
the prostate and is significantly up-regulated in prostatic cancerous
cells. The test quantitatively measures PCA3 mRNA as well as PSA
mRNA and determines their ratio. High ratios have been shown
to be indicative of prostate cancer [33] the Prostate Health Index
(phi), a new simple, noninvasive blood test that results in a score, or
“phi score.” This score gives more accurate information (3X more
specific for prostate cancer than PSA) on what an elevated PSA level
might mean and the probability of finding cancer on biopsy [34] the
Confirm MDx test, an epigenetic assay to help distinguish patients
who have a true-negative biopsy from those at risk for occult cancer.
The test helps urologists rule out prostate cancer-free men from
undergoing unnecessary repeat biopsies and, helps rule in high risk
patients who may require repeat biopsies and potential treatment
[34,35] the multiparametric (mp) prostate MRI, a rapidly evolving
imaging technology—diagnostic test, that can detect significant
prostate cancer as well as can, with high degree of safety, exclude
indolent disease. By enabling targeted biopsies that exclusively detect
significant cancer, mpMRI may provide the diagnostic accuracy that
has been so sorely lacking [36].
With the intention of increasing our clinical ability in making
individualized predictions (impact on biopsy decision making)
regarding the outcome of prostate biopsy in men at risk for prostate
cancer (abnormal serum PSA values) and in determining the need
(weighing the magnitude of effort required) to perform repeat
biopsies (avoiding unnecessary procedures), by stratifying individuals
in those who need intensive follow-up and those who do not, in cases
with negative initial prostate biopsies, we developed the PCP-SMART
model. The Prostate Cancer Risk - Simulation Modelling, Assessing
the Risk, Technique (PCP-SMART) is a novel, linear regression-based
multivariable mathematical, simulation modelling method, designed
to estimate the probability of detecting PCa (predict the outcome) on
prostate needle biopsy. It was constructed by incorporating routinely
available and easily determined clinical variables (patient age, total
PSA, free/total PSA ratio, prostate volume, PSA Density [PSAD]),
all established independent risk factors of prostatic carcinoma. Key
derivative of this multivariable model is the PCRD (Prostate Cancer
Risk Determinator), a novel mathematical index for estimating
the risk of PCa, which has shown promising results, increasing the
potential of better identifying men with PCa and equally important,
those who may avoid unnecessary biopsy as it exhibited good
diagnostic performance characteristics and high discriminative
accuracy for predicting the outcome of prostate biopsy, correctly
identifying 9 out of 10 patients with prostate cancer as well as, 9 in
10 of those without the disease. Also, it outperformed other, clinically
established and commonly used variables in predicting prostate
biopsy outcome in the initial and repeat settings as well as, it added
significant information to combinations with PCa risk factors highly
improving risk stratification of men prior to biopsy. By further
employing multiple variable logistic regression model analysis, we
formulated a mainly PCRD based mathematic equation, that allows
calculation of a single value, enabling measurement of the probability
of finding prostate cancer on biopsy, in an individual basis. The
formulated logistic regression based mathematic equation, allowed
calculation with 91% accuracy of a single probability value, enabling
individualized measurement of the risk of finding prostate cancer on
biopsy. In conclusion, our model was shown to be promising, simple
and practical, exhibiting good diagnostic performance characteristics
and high overall discriminative accuracy, providing significantly
improved ability in predicting an individual’s risk of prostate cancer
on biopsy. However, it lacks external validation while, meaningful
interpretation has yet to be uniformly accepted within clinical
practice. Thus, as generalizability of our results to community practice
populations remains to be determined, larger and multi-institutional
studies will be needed and external validation of the PCRD index is
recommended prior to its routine clinical use. Regardless of these
limitations, we anticipate that our model and its key derivatives will
become a widely used tool providing highly accurate, reproducible
and individualized disease related risk estimations to facilitate
management decisions in clinical practice, possibly easily accessible
via web application, that might aid urologists in selecting most
suitable candidates for initial or repeat prostate biopsy [37].
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