Tag Archive for: biomarkers

Posts

Podcast: Prostate Health Index and mpMRI to predict PCa grade reclassification in AS

Part of the BURST/BJUI Podcast Series

Mr Joseph Norris is a Specialty Registrar in Urology in the London Deanery. He is currently undertaking an MRC Doctoral Fellowship at UCL, under the supervision of Professor Mark Emberton. His research interest is prostate cancer that is inconspicuous on mpMRI.

RSM Urology Winter Meeting 2017, Northstar, California

rsm-2017-blogThis year’s Annual RSM Urology Section Winter Meeting, hosted by Roger Kirby and Matt Bultitude, was held in Lake Tahoe, California.

A pre-conference trip to sunny Los Angeles provided a warm-up to the meeting for a group of delegates who flew out early to visit Professor Indy Gill at the Keck School of Medicine.  We were treated to a diverse range of live open, endourological and robotic surgery; highlights included a salvage RARP with extended lymph node dissection and a robotic simple prostatectomy which was presented as an alternative option for units with a robot but no/limited HoLEP expertise.

1-1

On arrival to Northstar, Dr Stacy Loeb (NYU) officially opened the meeting by reviewing the social media urology highlights from 2016. Next up was Professor Joseph Smith (Nashville) who gave us a fascinating insight into the last 100 years of urology as seen through the Journal of Urology. Much like today, prostate cancer and BPH were areas of significant interest although, in contrast, early papers focused heavily on venereal disease, TB and the development of cystoscopy. Perhaps most interesting was a slightly hair-raising description of the management of IVC bleeding from 1927; the operating surgeon was advised to clamp as much tissue as possible, close and then return to theatre a week later in the hopes the bleeding had ceased!

1-21-3

 

 

 

 

 

With the promise of beautifully groomed pistes and stunning views of Lake Tahoe, it was hardly surprising that the meeting was attended by a record number of trainees. One of the highlights of the trainee session was the hilarious balloon debate which saw participants trying to convince the audience of how best to manage BPH in the newly inaugurated President Trump. Although strong arguments were put forward for finasteride, sildenafil, Urolift, PVP and HoLEP, TURP ultimately won the debate. A disclaimer: this was a fictional scenario and, to the best of my knowledge, Donald Trump does not have BPH.

The meeting also provided updates on prostate, renal and bladder cancer. A standout highlight was Professor Nick James’ presentation on STAMPEDE which summarized the trial’s key results and gave us a taste of the upcoming data we can expect to see in the next few years.

1-4

We were fortunate to be joined by prominent American faculty including Dr Trinity Bivalacqua (Johns Hopkins) and Dr Matt Cooperberg (UCSF) who provided state-of-the-art lectures on potential therapeutic targets and biomarkers in bladder and prostate cancer which promise to usher in a new era of personalized therapy.

1-5
1-6

A personal highlight was Tuesday’s session on learning from complications. It was great to hear some very senior and experienced surgeons speaking candidly about their worst complications. As a trainee, it served as a reminder that complications are inevitable in surgery and that it is not their absence which distinguishes a good surgeon but rather the ability to manage them well.

There was also plenty for those interested in benign disease, including topical discussions on how to best provide care to an increasingly ageing population with multiple co-morbidities. This was followed by some lively point-counterpoint sessions on robot-assisted versus open renal transplantation (Ravi Barod and Tim O’Brien), Urolift vs TURP (Tom McNicholas and Matt Bultitude) and HOLEP vs prostate artery embolization for BPH (Ben Challacombe and Rick Popert). Professor Culley Carson (University of North Carolina) concluded the session with a state-of-the art lecture on testosterone replacement.

1-71-8

In addition to the excellent academic programme, delegates enjoyed fantastic skiing with perfect weather and unparalleled views of the Sierra Nevada Mountains. For the more adventurous skiiers, there was also a trip to Squaw Valley, the home of the 1960 Winter Olympics. Another highlight was a Western-themed dinner on the shores of Lake Tahoe which culminated in almost all delegates trying their hand at line dancing to varying degrees of success! I have no doubt that next year’s meeting in Corvara, Italy will be equally successful and would especially encourage trainees to attend what promises to be another excellent week of skiing and urological education.

1-9
1-10
1-11

Miss Niyati Lobo
ST3 Urology Trainee, Brighton and Sussex University Hospitals NHS Trust

@niyatilobo

 

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

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 Shari, Keefe Murphy§, Helen MooreAlexandra 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

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, NYUSA

 

References

 

1 Foley RW, Gorman L, Shari N et al. Improving multivariable prostate cancer risk assessment using the prostate health index. BJU Int 2016; 117:40917

 

 

3 Loeb S, Sanda MG, Broyles DL et al. The prostate health index selectively identies clinically signicant prostate cancer. J Urol 2015; 193: 11639

 

 

 

 

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

Improving Multivariable Prostate Cancer Risk Assessment Using The Prostate Health Index

Robert W. Foley*, Laura Gorman*, Neda Shari, Keefe Murphy§, Helen MooreAlexandra 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
© 2022 BJU International. All Rights Reserved.