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Article of the Week: Testing the diagnostic accuracy of %p2PSA and PHI

Every Week the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

Finally, the third post under the Article of the Week heading on the homepage will consist of additional material or media. This week we feature a video from Dr. Martin Boegemann, discussing his paper. 

If you only have time to read one article this week, it should be this one.

The percentage of prostate-specific antigen (PSA) isoform [–2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged ≤65 years

Martin Boegemann*, Carsten Stephan†‡, Henning Cammann§Sebastien Vincendeau¶, Alain Houlgatte**, Klaus Jung†‡, Jean-Sebastien Blanchet†† and Axel Semjonow*

 

*Department of Urology, Prostate Center, University Medical Centre, Munster, Germany, Department of Urology, Charite Universitatsmedizin Berlin, Berlin, Germany, Berlin Institute for Urologic Research, Berlin, Germany, §Institute of Medical Informatics, Charite Universitatsmedizin Berlin, Berlin, Germany,
Department of Urology, Hospital Pontchallou, Rennes, France, **Department of Urology, HIA du Val de Grace, Paris, France, and ††Department of Scientic Affairs, Beckman Coulter Eurocenter, Nyon, Switzerland

 

OBJECTIVES

To prospectively test the diagnostic accuracy of the percentage of prostate specific antigen (PSA) isoform [–2]proPSA (%p2PSA) and the Prostate Health Index (PHI), and to determine their role for discrimination between significant and insignificant prostate cancer at initial and repeat prostate biopsy in men aged ≤65 years.

PATIENTS AND METHODS

The diagnostic performance of %p2PSA and PHI were evaluated in a multicentre study. In all, 769 men aged ≤65 years scheduled for initial or repeat prostate biopsy were recruited in four sites based on a total PSA (t-PSA) level of 1.6–8.0 ng/mL World Health Organization (WHO) calibrated (2–10 ng/mL Hybritech-calibrated). Serum samples were measured for the concentration of t-PSA, free PSA (f-PSA) and p2PSA with Beckman Coulter immunoassays on Access-2 or DxI800 instruments. PHI was calculated as (p2PSA/f-PSA × √t-PSA). Uni- and multivariable logistic regression models and an artificial neural network (ANN) were complemented by decision curve analysis (DCA).

RESULTS

In univariate analysis %p2PSA and PHI were the best predictors of prostate cancer detection in all patients (area under the curve [AUC] 0.72 and 0.73, respectively), at initial (AUC 0.67 and 0.69) and repeat biopsy (AUC 0.74 and 0.74). t-PSA and %f-PSA performed less accurately for all patients (AUC 0.54 and 0.62). For detection of significant prostate cancer (based on Prostate Cancer Research International Active Surveillance [PRIAS] criteria) the %p2PSA and PHI equally demonstrated best performance (AUC 0.70 and 0.73) compared with t-PSA and %f-PSA (AUC 0.54 and 0.59). In multivariate analysis PHI we added to a base model of age, prostate volume, digital rectal examination, t-PSA and %f-PSA. PHI was strongest in predicting prostate cancer in all patients, at initial and repeat biopsy and for significant prostate cancer (AUC 0.73, 0.68, 0.78 and 0.72, respectively). In DCA for all patients the ANN showed the broadest threshold probability and best net benefit. PHI as single parameter and the base model + PHI were equivalent with threshold probability and net benefit nearing those of the ANN. For significant cancers the ANN was the strongest parameter in DCA.

CONCLUSION

The present multicentre study showed that %p2PSA and PHI have a superior diagnostic performance for detecting prostate cancer in the PSA range of 1.6–8.0 ng/mL compared with t-PSA and %f-PSA at initial and repeat biopsy and for predicting significant prostate cancer in men aged ≤65 years. They are equally superior for counselling patients before biopsy.

Editorial: %p2PSA and PHI improve diagnostic accuracy for prostate cancer

Serum samples from patients with prostate cancer (PCa) are measured routinely for total PSA (tPSA) and complexed PSA as a part of the diagnostic evaluation. In addition, a variety of molecular isoforms of the PSA molecule can be assayed, including free PSA (fPSA), %freePSA, [-2]proPSA, %[-2]proPSA and the new combined calculation, termed the Prostate Health Index (PHI), which is calculated using [-2]proPSA, fPSA and tPSA. The PHI was developed by Beckman Coulter immunoassays and is performed on Access-2- or DxI800-instruments. Numerous clinical studies have been conducted with the isoforms of PSA (fPSA, %fPSA, [-2]proPSA and %[-2]ProPSA) to test the PCa diagnosis and prognosis predictions, and to predict the need for rebiopsies [1]. In addition to these various molecular isoforms of US Food and Drug Administration-approved clinical bioassays for PSA, several PSA-computed derivatives have also been developed to assess kinetics such as PSA velocity, PSA density and PSA doubling time, and all of these tests have been used to evaluate mostly prognostic events (i.e. disease progression, metastasis and death) in patients with PCa [2].

In the recent publication by Boegemann et al. in BJUI [3], the PHI is calculated as ([-2]proPSA/fPSA) × √tPSA). The PHI has been investigated previously with regard to its use in the diagnosis and prognosis of PCa, and key findings have included its role in defining PCa tumour aggressiveness [1, 4, 5], its value in predicting active surveillance upstaging [6, 7], and its ability to predict the need for a rebiopsy [1]. When authors decide that a new PSA test may be valuable in the management of PCa, they usually include historic PSA and PSA isoforms for statistically relevant comparisons. It is most gratifying, therefore, that Boegemann et al. studied a total of 769 men ≤ 65 years old, scheduled for initial or repeat prostate biopsy and that they were recruited from four test sites. Their in-depth analysis included total PSA as well as its isoforms, generating univariate analysis. Results for %[-2]proPSA and the PHI yielded similar areas under the curve (0.72 and 0.73, respectively) in all patients (P < 0.001). They also showed that the PHI had the best performance in predicting the initial biopsy (0.67 and 0.68, respectively) and repeat biopsy (0.79 and 0.78, respectively) results. To substantiate their observations, the authors tested several multivariate models using logistic regression and artificial neural network (ANN) methods. Both worked, and in multivariate analysis PHI was added to a base model that included age, prostate volume and DRE results, tPSA and %fPSA. The PHI was strongest in predicting PCa in all patients, at initial and repeat biopsy and for significant PCa (AUC 0.73, 0.68, 0.78 and 0.72, respectively). All results were strongly supported by decision-curve analysis tools for ANN and logistic regression modelling, as well as single variable analysis for the PHI, %fPSA, tPSA, ‘treat all’ and ‘treat none’ data.

In summary, the multicentre study by Boegemann et al. assessed 769 men aged ≤ 65 years at their initial and repeat prostate biopsy diagnoses and %[-2]proPSA and PHI were shown to be the strongest predictors of biopsy outcome. The two multivariate prediction ANN models (BM-1 and BM-2) were shown to minimize the risk of missing clinically significant PCa, while reducing the number of unnecessary biopsies in men without PCa or potentially insignificant disease.

Currently, evaluation of high-risk cancer is often based on genomic knowledge and has ~70–75% accuracy to offer personalized treatment regimens. The present manuscript achieved similar accuracy using the PHI and routine clinicopathological features to create models for PCa detection and repeat biopsy decision-making. Furthermore, many patients and their doctors choose definitive treatment that might be unnecessary and can cause incontinence and/or impotence. Active surveillance is an alternative option for very-low- to low-risk PCa cases using a minimal number of positive biopsy cores, PSA density ≥and tPSA ≥ 10 ng/mL for entry criteria [6, 7]. Clearly, a PCa risk predictor containing the best biomarkers, including the PHI, will improve the accuracy in the management of patients on active surveillance. There is also a need at the pretreatment diagnostic step to determine whether the pathological stage (i.e. non-organ-confined status) of the tumour requires definitive treatment (i.e. surgery and/or irradiation), with or without adjuvant therapy. Hence, we will need the best clinicopathological quantitative medical imaging and histomorphological (molecular and histological) information at pretreatment stage to create new and more accurate nomograms or tables and have them validated. Ultimately, the patient requires risk models with an accuracy close to 100% to make treatment decisions safely and with confidence [8].

Robert W. Veltri

 

Department of Urology, Brady Urological Institute, Johns Hopkins Univeristy School of Medicine, Baltimore, MD, USA

 

References

 

 

2 Sengupta S, Amling C, DAmico AV, Blute ML. Prostate speciantigen kinetics in the management of prostate cancer. J Urology 2008; 179: 8216

 

 

 

5 Heidegger I , Klocker H, Steiner V et al. [-2]proPSA is an early marker for prostate cancer aggressiveness. Prostate Cancer Prostatic Dis 2014; 17: 704

 

 

 

 

Video: The diagnostic performance of %p2PSA and PHI

The percentage of prostate-specific antigen (PSA) isoform [–2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged ≤65 years

Martin Boegemann*, Carsten Stephan†‡, Henning Cammann§Sebastien Vincendeau¶, Alain Houlgatte**, Klaus Jung†‡, Jean-Sebastien Blanchet†† and Axel Semjonow*

 

*Department of Urology, Prostate Center, University Medical Centre, Munster, Germany, Department of Urology, Charite Universitatsmedizin Berlin, Berlin, Germany, Berlin Institute for Urologic Research, Berlin, Germany, §Institute of Medical Informatics, Charite Universitatsmedizin Berlin, Berlin, Germany,
Department of Urology, Hospital Pontchallou, Rennes, France, **Department of Urology, HIA du Val de Grace, Paris, France, and ††Department of Scientic Affairs, Beckman Coulter Eurocenter, Nyon, Switzerland

 

OBJECTIVES

To prospectively test the diagnostic accuracy of the percentage of prostate specific antigen (PSA) isoform [–2]proPSA (%p2PSA) and the Prostate Health Index (PHI), and to determine their role for discrimination between significant and insignificant prostate cancer at initial and repeat prostate biopsy in men aged ≤65 years.

PATIENTS AND METHODS

The diagnostic performance of %p2PSA and PHI were evaluated in a multicentre study. In all, 769 men aged ≤65 years scheduled for initial or repeat prostate biopsy were recruited in four sites based on a total PSA (t-PSA) level of 1.6–8.0 ng/mL World Health Organization (WHO) calibrated (2–10 ng/mL Hybritech-calibrated). Serum samples were measured for the concentration of t-PSA, free PSA (f-PSA) and p2PSA with Beckman Coulter immunoassays on Access-2 or DxI800 instruments. PHI was calculated as (p2PSA/f-PSA × √t-PSA). Uni- and multivariable logistic regression models and an artificial neural network (ANN) were complemented by decision curve analysis (DCA).

RESULTS

In univariate analysis %p2PSA and PHI were the best predictors of prostate cancer detection in all patients (area under the curve [AUC] 0.72 and 0.73, respectively), at initial (AUC 0.67 and 0.69) and repeat biopsy (AUC 0.74 and 0.74). t-PSA and %f-PSA performed less accurately for all patients (AUC 0.54 and 0.62). For detection of significant prostate cancer (based on Prostate Cancer Research International Active Surveillance [PRIAS] criteria) the %p2PSA and PHI equally demonstrated best performance (AUC 0.70 and 0.73) compared with t-PSA and %f-PSA (AUC 0.54 and 0.59). In multivariate analysis PHI we added to a base model of age, prostate volume, digital rectal examination, t-PSA and %f-PSA. PHI was strongest in predicting prostate cancer in all patients, at initial and repeat biopsy and for significant prostate cancer (AUC 0.73, 0.68, 0.78 and 0.72, respectively). In DCA for all patients the ANN showed the broadest threshold probability and best net benefit. PHI as single parameter and the base model + PHI were equivalent with threshold probability and net benefit nearing those of the ANN. For significant cancers the ANN was the strongest parameter in DCA.

CONCLUSION

The present multicentre study showed that %p2PSA and PHI have a superior diagnostic performance for detecting prostate cancer in the PSA range of 1.6–8.0 ng/mL compared with t-PSA and %f-PSA at initial and repeat biopsy and for predicting significant prostate cancer in men aged ≤65 years. They are equally superior for counselling patients before biopsy.

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