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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Every week the Editor-in-Chief selects the 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. Alberto Abrate, discussing his paper.
If you only have time to read one article this week, it should be this one.
Alberto Abrate, Massimo Lazzeri, Giovanni Lughezzani, Nicolòmaria Buffi, Vittorio Bini*,Alexander Haese†, Alexandre de la Taille‡, Thomas McNicholas§, Joan Palou Redorta¶,Giulio M. Gadda, Giuliana Lista, Ella Kinzikeeva, Nicola Fossati, Alessandro Larcher,Paolo Dell’Oglio, Francesco Mistretta, Massimo Freschi** and Giorgio Guazzoni
Division of Oncology, Unit of Urology, URI, **Department of Pathology, IRCCS Ospedale San Raffaele, UniversitàVita-Salute San Raffaele, Milan, *Department of Internal Medicine, University of Perugia, Perugia, Italy,†Martini-ClinicProstate Cancer Center, University Clinic Hamburg-Eppendorf, Hamburg, Germany,‡Department of Urology, APHPMondor Hospital, Créteil, France,§South Bedfordshire and Hertfordshire Urological Cancer Centre, Lister Hospital,Stevenage, UK, and¶Urologic Oncology Section of the Department of Urology and Radiology Department, FundaciòPuigvert, Barcelona, Spain
To test serum prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA), p2PSA/free PSA (%p2PSA) and Prostate Health Index (PHI) accuracy in predicting prostate cancer in obese men and to test whether PHI is more accurate than PSA in predicting prostate cancer in obese patients.
The analysis consisted of a nested case-control study from the pro-PSA Multicentric European Study (PROMEtheuS) project. The study is registered at https://www.controlled-trials.com/ISRCTN04707454. The primary outcome was to test sensitivity, specificity and accuracy (clinical validity) of serum p2PSA, %p2PSA and PHI, in determining prostate cancer at prostate biopsy in obese men [body mass index (BMI) ≥30 kg/m2], compared with total PSA (tPSA), free PSA (fPSA) and fPSA/tPSA ratio (%fPSA). The number of avoidable prostate biopsies (clinical utility) was also assessed. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis.
Of the 965 patients, 383 (39.7%) were normal weight (BMI <25 kg/m2), 440 (45.6%) were overweight (BMI 25–29.9 kg/m2) and 142 (14.7%) were obese (BMI ≥30 kg/m2). Among obese patients, prostate cancer was found in 65 patients (45.8%), with a higher percentage of Gleason score ≥7 diseases (67.7%). PSA, p2PSA, %p2PSA and PHI were significantly higher, and %fPSA significantly lower in patients with prostate cancer (P < 0.001). In multivariable logistic regression models, PHI significantly increased accuracy of the base multivariable model by 8.8% (P = 0.007). At a PHI threshold of 35.7, 46 (32.4%) biopsies could have been avoided.
In obese patients, PHI is significantly more accurate than current tests in predicting prostate cancer.
The Prostate Health Index (PHI) has regulatory approval in >50 countries worldwide and is now being incorporated into prostate cancer guidelines; for example, the 2014 National Comprehensive Cancer Network Guidelines for early prostate cancer detection discuss the PHI as a means to improve specificity, using a threshold score of 35 [1]. The PHI is also discussed in the Melbourne Consensus Statement [2], and it has been incorporated into the multivariable Rotterdam risk calculator smartphone app for use in point-of-care decisions [3].
As the use of this test continues to expand, more data on its performance in specific at-risk populations are of great interest. The investigators from the PROMEtheus multicentre European trial have previously validated the use of the PHI in men with a positive family history of prostate cancer [4]. The new study by Abrate et al. in this issue of BJUI instead addresses another high-risk population – obese men – who have previously been shown to have a greater risk of aggressive prostate cancer [5].
Among the 965 participants in the PROMEtheus study, 14.7% were considered obese based on a body mass index ≥30 kg/m2. In this group, 45.8% were diagnosed with prostate cancer from a ≥12-core biopsy, and 67.7% had a Gleason score ≥7. Overall, the PHI significantly outperformed PSA for prostate cancer detection in men with a body mass index ≥30 kg/m2 (area under the curve 0.839 vs 0.694; P < 0.001). At 90% sensitivity, the threshold for PHI in obese men was 35.7, with a specificity of 52.3%. The PHI also had the best performance for the detection of Gleason ≥7 disease, with an area under the curve of 0.89.
These findings add to the highly consistent body of evidence supporting the use of the PHI in early prostate cancer detection and risk stratification. In fact, all published studies to date have shown that the PHI outperforms PSA for detection of overall and high-grade prostate cancer detection on biopsy [6]. Numerous studies have also shown a role for the PHI in patient selection and monitoring during active surveillance [7, 8]. Expanded use of this test is warranted to reduce unnecessary biopsies and better identify cancers with life-threatening potential.
1 National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology. Prostate Cancer Early Detection Version 2014. https://www.nccn.org/professionals/physician_gls/pdf/prostate_detection.pdf.Accessed May 26, 2014
2 Murphy DG, Ahlering T, Catalona WJ et al. The melbourne consensus statement on the early detection of prostate cancer. BJU Int 2014; 113:186–8
3 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
4 Lazzeri M, Haese A, Abrate A et al. Clinical performance of serum prostate-specific antigen isoform [-2]pr oPSA (p2PS A) and its derivatives, %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer: results from a multicentre European study, the PROMEtheuS project. BJU Int 2013; 112:313–21
5 Freedland SJ, Banez LL, Sun LL, Fitzsimons NJ, Moul JW. Obese men have higher-grade and larger tumors: an analysis of the duke prostate center database. Prostate Cancer Prostatic Dis 2009; 12: 259–63
6 Filella X, Gimenez N. Evaluation of [-2] proPSA and Prostate Health Index (phi) for the detection of prostate cancer: a systematic review and meta-analysis. Clin Chem Lab Med 2013; 51: 729–39
7 Tosoian JJ, Loeb S, Feng Z et al. Association of [-2]proPSA with Biopsy Reclassification During Active Surveillance for Prostate Cancer. J Urol2012; 188: 1131–6
8 Hirama H, Sugimoto M, Ito K, Shiraishi T, Kakehi Y. The impact of baseline [-2]proPSA-related indices on the prediction of pathological reclassification at 1 year during active surveillance for low-risk prostate cancer: the Japanese multicenter study cohort. J Cancer Res Clin Oncol2014; 140: 257–63
Alberto Abrate, Massimo Lazzeri, Giovanni Lughezzani, Nicolòmaria Buffi, Vittorio Bini*,Alexander Haese†, Alexandre de la Taille‡, Thomas McNicholas§, Joan Palou Redorta¶,Giulio M. Gadda, Giuliana Lista, Ella Kinzikeeva, Nicola Fossati, Alessandro Larcher,Paolo Dell’Oglio, Francesco Mistretta, Massimo Freschi** and Giorgio Guazzoni
Division of Oncology, Unit of Urology, URI, **Department of Pathology, IRCCS Ospedale San Raffaele, UniversitàVita-Salute San Raffaele, Milan, *Department of Internal Medicine, University of Perugia, Perugia, Italy,†Martini-ClinicProstate Cancer Center, University Clinic Hamburg-Eppendorf, Hamburg, Germany,‡Department of Urology, APHPMondor Hospital, Créteil, France,§South Bedfordshire and Hertfordshire Urological Cancer Centre, Lister Hospital,Stevenage, UK, and¶Urologic Oncology Section of the Department of Urology and Radiology Department, FundaciòPuigvert, Barcelona, Spain
To test serum prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA), p2PSA/free PSA (%p2PSA) and Prostate Health Index (PHI) accuracy in predicting prostate cancer in obese men and to test whether PHI is more accurate than PSA in predicting prostate cancer in obese patients.
The analysis consisted of a nested case-control study from the pro-PSA Multicentric European Study (PROMEtheuS) project. The study is registered at https://www.controlled-trials.com/ISRCTN04707454. The primary outcome was to test sensitivity, specificity and accuracy (clinical validity) of serum p2PSA, %p2PSA and PHI, in determining prostate cancer at prostate biopsy in obese men [body mass index (BMI) ≥30 kg/m2], compared with total PSA (tPSA), free PSA (fPSA) and fPSA/tPSA ratio (%fPSA). The number of avoidable prostate biopsies (clinical utility) was also assessed. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis.
Of the 965 patients, 383 (39.7%) were normal weight (BMI <25 kg/m2), 440 (45.6%) were overweight (BMI 25–29.9 kg/m2) and 142 (14.7%) were obese (BMI ≥30 kg/m2). Among obese patients, prostate cancer was found in 65 patients (45.8%), with a higher percentage of Gleason score ≥7 diseases (67.7%). PSA, p2PSA, %p2PSA and PHI were significantly higher, and %fPSA significantly lower in patients with prostate cancer (P < 0.001). In multivariable logistic regression models, PHI significantly increased accuracy of the base multivariable model by 8.8% (P = 0.007). At a PHI threshold of 35.7, 46 (32.4%) biopsies could have been avoided.
In obese patients, PHI is significantly more accurate than current tests in predicting prostate cancer.