PROSTATE HEALTH INDEX (PHI)IS MORE ACCURATE THAN PCA3 ASSAY IN THE
PREDICTION OF PROSTATE BIOPSY (PBX) IN PATIENTS WHO HAVE UNDERGONE INITIAL
AND REPEATED PROSTATIC BIOPSIES
Vincenzo Scattoni, Massimo Lazzeri, Stefano De Luca*, Enrico Bollito*, Donato Randone*, Firas
Abdollah, Carmen Maccagnano, Giovanni Lughezzani, Alessandro Larcher, Giuliana Lista,
GiulioMaria Gadda, Patrizio Rigatti and Giorgio Guazzoni.
Department of Urology - University Vita-Salute,Scientific Institute H San Raffaele, Milan, Italy
*Department of Urology, OspedaleGradenigo, Torino, ITALY
Introduction
Beckman Coulter PHI and PCA3-Score have shown to improve the prediction of prostate cancer
(PCa) in men undergoing first and/or repeated extended PBx. However, a head-to-head
comparison of the PCa prediction accuracy of these two molecules is still lacking.
Material & Methods: The performance characteristics of Beckman PHI [(p2PSA/fPSA) x √tPSA)]
and PCA3 across different PSA ranges was evaluated in a combined dataset of 210 European
men (median age 67 yrs, median PSA: 7.2 ng/ml±7.1) undergoing first (n=116 cases) or (multiple)
repeat PBx (n=94 cases) (12-22 cores) in two large academic centers. Both tests were performed
prior to DRE and PBx. The blood samples were processed using UniCel DxI800 Immunoassay
System analyzer (Beckman & Coulter, Brea, CA, USA). The PCA3 score was determined by the
PROGENSA ™ PCA3 Assay. PHI and PCA3 were also compared to age, total PSA, prostate
volume, DRE, PSA density (PSAD), and %fPSA. Statistical analysis encompassed descriptive
statistics, nonparametric analysis of differences in PBx+ vs. PBx- men. A basic multivariable
model (BM) was developed, and consisted of age, PSA, %fPSA, and DRE. The area-under-thecurve (AUC) was used to quantify the discrimination of the basic model, and the HosmerLemeshow statistic was used to measure its calibration. The same benchmarks were used to
measure the improvement of the prediction accuracy (discrimination and calibration) of the basic
model by adding PCA3 and PHI separately. Finally, we relied on decision curve analysis (DCA) to
compare the various models directly in a head-to-head fashion.
Results: Cancer was detected in 70/210 patients (33.3 %) in the whole group (35.1% and 30.9%
in the initial and repeat group, respectively). In the entire cohort, PHI had the highest AUC value
(0.69), which was significantly higher than PCA3 (0.58) and %FPSA (0.59). Likewise, in the initial
setting, AUC was highest for PHI (0.69) followed by %fPSA (0.67), PCA3 (0.58).Similarly, in the
repeat setting, AUC was highest for PHI (0.72) compared to PCA3 (0.63), %fPSA (0.52).Adding
the PHI index and PCA3 to the basic model (AUC 0.77) improved its predictive accuracy (AUC 82
and 78, respectively),but this increase reacheda full statistical significance only for PHI. Adding
PCA3 to the basic model + PHI did not increase the predictive accuracy in the whole population, as
well as in the two settings.
In DCA, the highest net-benefit was observed when PHI was added to the basic model. This holds
true in the initial as well as in the re-biopsy setting. Adding PCA3 to the basic model + PHI did not
result in a further improvement of the net-benefit.
Conclusions: PHI seems to be the strongest parameter to predict PBx outcome in the initial as
well as in the repeated biopsy. Adding PCA3 to the basic model +PHI did not improve the
prediction accuracy in both settings. PHI may therefore be useful in guiding biopsy decisions.