Tag Archive for: predictive models

You are here: / predictive models

Posts

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

/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 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

 

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

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

/by

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.

Read the full article
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

/by

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
© 2024 BJU International. All Rights Reserved.