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Article of the Week: Using The PHI to improve Prostate Cancer Risk Assessment

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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 Mr. Robert Foley, discussing his paper.

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

Improving Multivariable Prostate Cancer Risk Assessment Using The Prostate Health Index

Robert W. Foley*†, Laura Gorman*, Neda Sharifi‡, Keefe Murphy§, Helen Moore‡, Alexandra V. Tuzova**, Antoinette S. Perry**, T. Brendan Murphy§, Dara J. Lundon*††† and R. William G. Watson*†

 

*Conway Institute of Biomolecular and Biomedical Research, University College Dublin, †UCD School of Medicine and Medical Science, University College Dublin, ‡Department of Biochemistry, Beaumont Hospital, §UCD School of Mathematical Sciences, University College Dublin, Insight Centre for Data Analytics, University College Dublin, **Prostate Molecular Oncology, Institute of Molecular Medicine, Trinity College Dublin, and ††Department of Urology, Mater Misericordiae University Hospital, Dublin, Ireland

 

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Objectives

To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment.

Patients and Methods

Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total prostate-specific antigen (PSA), free PSA (fPSA) and p2PSA. From this, the Prostate Health Index (PHI) score was calculated (PHI = (p2PSA/fPSA)*√tPSA). The men’s clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial (PCPT) risk model. Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal examination, previous negative biopsy and either PSA or PHI score, respectively. Calibration plots, receiver-operating characteristic (ROC) curves and decision curves were generated to assess the performance of the three models.

AOTWMAR£

Results

The PSA model and PHI model were both well calibrated in this cohort, with the PHI model showing the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the PHI model, PSA model and PCPT model were 0.77, 0.71 and 0.69, respectively, for the prediction of prostate cancer (PCa) and 0.79, 0.72 and 0.72, respectively, for the prediction of high grade PCa. Decision-curve analysis showed a superior net benefit of the PHI model over both the PSA model and the PCPT risk model in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities.

Conclusion

A logical and standardized approach to the use of clinical risk factors can allow more accurate risk stratification of men under investigation for PCa. The measurement of p2PSA and the integration of this biomarker into a clinical prediction model can further increase the accuracy of risk stratification, helping to better inform the decision for prostate biopsy in a referral population.

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Editorial: Prostate biopsy decisions: one-size-fits-all approach with total PSA is out and a multivariable approach with the PHI is in

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The days of using one PSA threshold to trigger a biopsy for all men are over, and the field has moved toward a more individualized approach to prostate biopsy decisions, taking into account each patient’s specific set of risk factors. Foley et al. [1] provide compelling evidence supporting the use of the Prostate Health Index (PHI) as part of this multivariable approach to prostate biopsy decisions.

There is now a large body of evidence showing that the PHI is more specific for prostate cancer than total PSA and percent free PSA, as was concluded in a 2014 systematic review [2]. Moreover, several recent studies have confirmed the superiority of the PHI over its individual components [3, 4] and compared with other markers such as PCA3 [5], for predicting clinically significant prostate cancer.

The present new study by Foley et al. [1] builds on this literature by providing clinically useful data on the role of the PHI in prostate biopsy decisions. Specifically, they examined 250 men with elevated age-specific PSA and/or abnormal DRE who were referred for ≥12-core prostate biopsy as part of the Irish Rapid Access Clinic. The median PHI was 48.6 in men with prostate cancer, vs 33.4 in men without prostate cancer on biopsy. On receiver-operating characteristic analysis, the PHI had a higher area under the curve (AUC) for overall prostate cancer compared with total and percent free PSA (AUCs 0.71, 0.62 and 0.64, respectively), as well as for high grade prostate cancer (AUC 0.78, 0.70 and 0.67, respectively). Compared with the PHI, even the combination of total and percent free PSA had a lower AUC of 0.67 for overall prostate cancer and 0.75 for high grade prostate cancer.

Next, the authors developed a multivariable prediction model incorporating age, family history, DRE and previous biopsy history, along with either PSA or the PHI. Using the PHI in this model rather than total PSA resulted in greater predictive accuracy for the detection of overall and Gleason ≥7 disease. The PHI-based model also showed superior net benefit to the PSA-based multivariable models on decision curve analysis.

These findings are exactly what we would expect, as studies have consistently shown that the PHI outperforms PSA [2, 6]. Other groups from the European Randomized Study of Screening for Prostate Cancer (ERSPC) have also integrated the PHI into multivariable risk prediction through the development of a user-friendly smartphone app called the Rotterdam Risk Calculator [7]. Because our goal is to provide each patient with the best information from which to make decisions about biopsy, it only makes sense to use the best possible combination of markers that we have.

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Stacy Loeb
Department of Urology, Population Health and Laura and Isaac Perlmutter Cancer Center, New York University and Manhattan Veterans Affairs Medical Center, New York, NY, USA

 

References

 

1 Foley RW, Gorman L, Sharifi N et al. Improving multivariable prostate cancer risk assessment using the prostate health index. BJU Int 2016; 117:409–17

 

2 Huang YQ, Sun T, Zhong WD, Wu CL. Clinical performance of serum [-2]proPSA derivatives, %p2PSA and PHI, in the detection and management of prostate cancer. Am J Clin Exp Urol 2014; 2: 343–50

 

3 Loeb S, Sanda MG, Broyles DL et al. The prostate health index selectively identifies clinically significant prostate cancer. J Urol 2015; 193: 1163–9

 

4 de la Calle C, Patil D, Wei JT et al. Multicenter evaluation of the prostate health index to detect aggressive prostate cancer in biopsy naıve men. J Urol 2015; 194: 65–72

 

5 Ferro M, Lucarelli G, Bruzzese D et al. Improving the prediction of pathologic outcomes in patients undergoing radical prostatectomy: the value of prostate cancer antigen 3 (PCA3), prostate health index (phi) and sarcosine. Anticancer Res 2015; 35: 1017–23

 

6 Loeb S. Time to replace prostate-specific antigen (PSA) with the Prostate Health Index (PHI)? Yet more evidence that the PHI consistently outperforms PSA across diverse populations. BJU Int 2015; 115: 500

 

7 Roobol M, Salman J, Azevedo N. Abstract 857: The Rotterdam prostate cancer risk calculator: improved prediction with more relevant pre-biopsy information, now in the palm of your hand. Stockholm: European Association of Urology, 2014

 

Video: Improving Prostate Cancer Risk Assessment Using The PHI

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Improving Multivariable Prostate Cancer Risk Assessment Using The Prostate Health Index

Robert W. Foley*†, Laura Gorman*, Neda Sharifi‡, Keefe Murphy§, Helen Moore‡, Alexandra V. Tuzova**, Antoinette S. Perry**, T. Brendan Murphy§, Dara J. Lundon*††† and R. William G. Watson*†

 

*Conway Institute of Biomolecular and Biomedical Research, University College Dublin, †UCD School of Medicine and Medical Science, University College Dublin, ‡Department of Biochemistry, Beaumont Hospital, §UCD School of Mathematical Sciences, University College Dublin, Insight Centre for Data Analytics, University College Dublin, **Prostate Molecular Oncology, Institute of Molecular Medicine, Trinity College Dublin, and ††Department of Urology, Mater Misericordiae University Hospital, Dublin, Ireland

 

Read the full article

Objectives

To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment.

Patients and Methods

Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total prostate-specific antigen (PSA), free PSA (fPSA) and p2PSA. From this, the Prostate Health Index (PHI) score was calculated (PHI = (p2PSA/fPSA)*√tPSA). The men’s clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial (PCPT) risk model. Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal examination, previous negative biopsy and either PSA or PHI score, respectively. Calibration plots, receiver-operating characteristic (ROC) curves and decision curves were generated to assess the performance of the three models.

AOTWMAR£

Results

The PSA model and PHI model were both well calibrated in this cohort, with the PHI model showing the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the PHI model, PSA model and PCPT model were 0.77, 0.71 and 0.69, respectively, for the prediction of prostate cancer (PCa) and 0.79, 0.72 and 0.72, respectively, for the prediction of high grade PCa. Decision-curve analysis showed a superior net benefit of the PHI model over both the PSA model and the PCPT risk model in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities.

Conclusion

A logical and standardized approach to the use of clinical risk factors can allow more accurate risk stratification of men under investigation for PCa. The measurement of p2PSA and the integration of this biomarker into a clinical prediction model can further increase the accuracy of risk stratification, helping to better inform the decision for prostate biopsy in a referral population.

Read more articles of the week

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

/2 Comments/by

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 Scientific Affairs, Beckman Coulter Eurocenter, Nyon, Switzerland

 

Read the full article
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.

Read more articles of the week

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

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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].

Read the full article
Robert W. Veltri

 

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

 

References

 

1 Hori S, Blanchet JS, McLoughlin J. From prostate-specific antigen (PSA) to precursor PSA (proPSA) isoforms: a review of the emerging role of proPSAs in the detection and management of early prostate cancer. BJU Int 2013; 112: 717–28

 

2 Sengupta S, Amling C, D’Amico AV, Blute ML. Prostate specific antigen kinetics in the management of prostate cancer. J Urology 2008; 179: 821–6

 

3 Boegemann M, Stephan C, Cammann H et al. %[-2]proPSA and “prostate health index” (PHI) improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared to t-PSA and %f-PSA in men ≤ 65 years old. BJU Int 2015; 117: 72–9

 

4 Sokoll LJ, Sanda MG, Feng Z et al. A prospective, multicenter, National Cancer Institute Early Detection Research Network study of [-2] proPSA: improving prostate cancer detection and correlating with cancer aggressiveness. Cancer Epidemiol Biomarkers Prev 2010; 19: 1193–200

 

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: 70–4

 

6 Tosoian JJ, Loeb S, Feng Z et al. Association of [-2]proPSA with biopsy reclassification during active surveillance for prostate cancer. J Urol 2012; 188: 1131–6

 

7 Makarov DV, Isharwal S, Sokoll LJ et al. Pro-prostate-specific antigen measurements in serum and tissue are associated with treatment necessity among men enrolled in expectant management for prostate cancer. Clin Cancer Res 2009; 15: 7316–21

 

8 VeltriRW, Guangjing ZG, Lee G, Ali S, Madabhushi A. Histomorphometry of digital pathology: case study in prostate cancer. In Chen CH ed. Frontiers of Medical Imaging, Chapt 15. Singapore: World Scientific, 2014

 

Video: The diagnostic performance of %p2PSA and PHI

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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 Scientific Affairs, Beckman Coulter Eurocenter, Nyon, Switzerland

 

Read the full article
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.

Read more articles of the week
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