|Ahead of print publication
Prostate volume does not provide additional predictive value to prostate health index for prostate cancer or clinically significant prostate cancer: results from a multicenter study in China
Da Huang1, Yi-Shuo Wu2,3, Ding-Wei Ye4, Jun Qi5, Fang Liu3, Brian T Helfand6, Siqun L Zheng7, Qiang Ding2,3, Dan-Feng Xu1, Rong Na1,7, Jian-Feng Xu3,7, Ying-Hao Sun8
1 Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
2 Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China
3 Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China
4 Department of Urology, Shanghai Cancer Center, Fudan University, Shanghai 200032, China
5 Department of Urology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
6 Division of Urology, NorthShore University Health System, Evanston, IL 60201, USA
7 Program for Personalized Cancer Care, NorthShore University Health System, Evanston, IL 60201, USA
8 Department of Urology, Shanghai Changhai Hospital, The Second Military Medical University, Shanghai 200433, China
|Date of Submission||14-May-2019|
|Date of Acceptance||14-Oct-2019|
|Date of Web Publication||10-Jan-2020|
Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Program for Personalized Cancer Care, NorthShore University Health System, Evanston, IL 60201, USA
Source of Support: None, Conflict of Interest: None
To evaluate whether prostate volume (PV) would provide additional predictive utility to the prostate health index (phi) for predicting prostate cancer (PCa) or clinically significant prostate cancer, we designed a prospective, observational multicenter study in two prostate biopsy cohorts. Cohort 1 included 595 patients from three medical centers from 2012 to 2013, and Cohort 2 included 1025 patients from four medical centers from 2013 to 2014. Area under the receiver operating characteristic curves (AUC) and logistic regression models were used to evaluate the predictive performance of PV-based derivatives and models. Linear regression analysis showed that both total prostate-specific antigen (tPSA) and free PSA (fPSA) were significantly correlated with PV (all P < 0.05). [-2]proPSA (p2PSA) was significantly correlated with PV in Cohort 2 (P< 0.001) but not in Cohort 1 (P= 0.309), while no significant association was observed between phi and PV. When combining phi with PV, phi density (PHID) and another phi derivative (PHIV, calculated as phi/PV0.5) did not outperform phi for predicting PCa or clinically significant PCa in either Cohort 1 or Cohort 2. Logistic regression analysis also showed that phi and PV were independent predictors for both PCa and clinically significant PCa (all P < 0.05); however, PV did not provide additional predictive value to phi when combining these derivatives in a regression model (all models vs phi were not statistically significant, all P > 0.05). In conclusion, PV-based derivatives (both PHIV and PHID) and models incorporating PV did not improve the predictive abilities of phi for either PCa or clinically significant PCa.
Keywords: China; prostate cancer; prostate health index; prostate volume
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|How to cite this URL:|
Huang D, Wu YS, Ye DW, Qi J, Liu F, Helfand BT, Zheng SL, Ding Q, Xu DF, Na R, Xu JF, Sun YH. Prostate volume does not provide additional predictive value to prostate health index for prostate cancer or clinically significant prostate cancer: results from a multicenter study in China. Asian J Androl [Epub ahead of print] [cited 2020 Jul 6]. Available from: http://www.ajandrology.com/preprintarticle.asp?id=275509
Da Huang, Yi-Shuo Wu
These authors contributed equally to this study.
| Introduction|| |
With the estimated 1 276 106 new cases and 358 989 deaths worldwide, prostate cancer (PCa) has become the second most common cancer and the fifth leading cause of cancer-specific death in males. Along with widespread prostate-specific antigen (PSA) screening and an in-depth understanding of PCa, PSA has been gradually considered an unspecific tumor biomarker, leading to large numbers of unnecessary prostate biopsies., Moreover, nonaggressive or low-grade PCa (also known as clinically insignificant disease) may not cause clinical consequences throughout the lifetime of a patient if left untreated or under surveillance, according to the results from several autopsy and active surveillance studies.,,,,,,,,,, These clinical issues are also known as overdiagnosis and overtreatment.
To improve PSA-based diagnostic ability, the prostate health index (phi), derived from total PSA (tPSA), free PSA (fPSA), and [-2]proPSA (p2PSA), has been introduced and shown to be a better predictor than tPSA and %fPSA (fPSA/tPSA) for both PCa and clinically significant PCa. Using phi as a supplementary tool on tPSA may also reduce the number of unnecessary biopsies.,,,,,,
Since many studies have reported that prostate volume (PV) is associated with prostate cancer and tPSA, PSA density (PSAD, tPSA/PV) was introduced to adjust this influence and was shown to have better predictive value for PCa., However, phi, as a multivariable formula, did not include PV. Two previous single-center studies by Tosoian et al.and Druskin et al. demonstrated that phi density (PHID, calculated as phi/PV) outperformed phi in the diagnosis of clinically significant cancer (Gleason score, GS ≥7). A recent study by Vendrami et al. demonstrated that PHID has a greater predictive value than phi when prostate biopsies were guided by image fusion of magnetic resonance (MR) and transrectal ultrasound. However, due to the relatively small sample sizes and the study design, the association between phi and PV was not clear enough in these studies. Therefore, we conducted this study to investigate the associations among p2PSA, phi, and PV. We also investigated whether PV would provide additional predictive utility when combined with phiin the Chinese population.
| Participants and Methods|| |
Study design and cohort
This study was a prospective, observational multicenter study in two prostate biopsy cohorts., Cohort 1 recruited consecutive 635 patients from 2012 to 2013 in three tertiary hospitals in Shanghai, China (Huashan Hospital, Shanghai Cancer Center, and Xinhua Hospital). Cohort 2 recruited consecutive 1538 patients from 2013 to 2014 in four tertiary hospitals in Shanghai, China (the above three hospitals and Shanghai Changhai Hospital). All the patients underwent initial prostate biopsies. The indications for prostate biopsy were the same across different tertiary hospitals: (1) tPSA level >4.0 ng ml−1; (2) %fPSA <0.16; and (3) presence of suspicious prostate nodules detected by digital rectal examination (DRE) or ultrasound. Transrectal ultrasound (TRUS)-guided biopsy was performed using a 10-core scheme in Cohort 1 and a 10- to 14-core scheme in Cohort 2. All biopsy specimens were reviewed in the Department of Pathology at each hospital. The study was approved by the institutional review board of each hospital, and written informed consent was obtained from each participant.
Patients were excluded in the present study if (1) there were no records of age and PV or (2) the records of any serum antigen levels (tPSA, fPSA, or p2PSA) were missing. Clinically significant PCa was defined as PCa with Gleason score (GS) ≥7.
Derivative variables were calculated as follows: (1) %fPSA: fPSA/tPSA; (2) PSAD: tPSA/PV; (3) %p2PSA: p2PSA/fPSA; (4) phi: (p2PSA/fPSA) × tPSA0.5; (5) PHID: phi/PV; (6) PHIV: phi/PV0.5
In univariate analysis, continuous variables were compared using a Mann–Whitney U test for nonnormal distributed variables or Student's t-test for normal distributed variables. Categorical variables were compared using a Chi-square test. Linear regression was used to measure the association between serum antigen levels (tPSA, fPSA, and p2PSA) and PV after log-transformation. In multivariate analysis, we performed four multivariate logistic regression (LR) models for predicting PCa and clinically significant PCa, including age, PV, and phi (or correlative serum antigen levels). Predictive abilities were evaluated using the area under the receiver operating characteristic curve (AUC). Statistical differences between AUCs were evaluated using the DeLong method.
All statistical analyses were performed using Stata® 15.1 Special Edition (StataCorp, College Station, TX, USA). A two-tailed P< 0.05 was considered statistically significant.
| Results|| |
The characteristics of the two cohorts were described in our previous studies., Based on the exclusion criteria, 40 and 513 patients were excluded from Cohort 1 and 2, respectively, because of incomplete records [Supplementary Figure 1 [Additional file 1]]. Finally, a total of 595 patients were included in Cohort 1 and 1025 patients were included in Cohort 2. The demographic information of the study populations is shown in [Supplementary Table 1 [Additional file 2]]. Two hundred and fifty-five out of 595 patients were diagnosed with PCa (42.9%) and 193 patients were diagnosed with clinically significant PCa (32.4%) in Cohort 1. In Cohort 2, 437 (42.6%) patients were diagnosed with PCa and 346 (33.8%) with clinically significant PCa.
The correlations between serum antigen indices (tPSA, p2PSA, fPSA, and phi) and PV were evaluated by simple linear regression analysis [Supplementary Table 2 [Additional file 3]]. In Cohort 1, we found that both tPSA and fPSA were significantly correlated with PV (P = 0.005 and P< 0.001, respectively). However, no significant association was found between p2PSA and PV and between phi and PV (P = 0.309 and P = 0.107, respectively). In Cohort 2, tPSA, p2PSA, and fPSA were significantly correlated with PV (all P< 0.001). Similarly, no significant association was found between phi and PV (P = 0.434).
In the entire study population and separate cohorts, the median PV was approximately 40 ml (entire Cohort: 41 ml; Cohort 1: 42 ml; Cohort 2: 41 ml; [Supplementary Table 1]. We then performed a stratified analysis for patients with different PV (≤40 ml and >40 ml). When stratified using 40 ml as a threshold, patients with smaller PV had significantly lower tPSA, lower %fPSA, and higher %p2PSA in two separate cohorts (all P< 0.05, [Supplementary Table 3 [Additional file 4]] and [Supplementary Table 4 [Additional file 5]]. In Cohort 1, there was no significant difference in phi between the two volume groups (P = 0.081; [Supplementary Table 3]. However, marginally significant differences in phi were found between patients with PV ≤40 ml and >40 ml in Cohort 2 (P = 0.047; [Supplementary Table 4].
The association between phi-PV derivatives and PCa or clinically significant PCa was also evaluated. In univariable logistic regression [Table 1], both PHID and PHIV (another phi derivative, calculated as phi/PV0.5) were significantly associated with PCa and clinically significant PCa in the two cohorts (all P< 0.001). Notably, PHID had higher odds ratios (ORs) than phi when predicting PCa (ORPHID= 1.90, ORphi= 1.02) and clinically significant PCa (ORPHID= 1.43, ORphi= 1.01). Similar results were observed in Cohort 2 for PCa (ORPHID= 1.63, ORphi= 1.02) and clinically significant PCa (ORPHID= 1.37, ORphi= 1.01).
|Table 1: Univariable logistic regression models for the prediction of prostate cancer/clinically significant prostate cancer (Gleason score ≥7) in two cohorts|
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Multivariable LR showed that age, phi, and PV were all independent predictors for PCa and clinically significant PCa in both cohorts (all P< 0.001; [Table 2]. To evaluate whether PV would provide additional predictive value to phi, we established predictive models based on phior phi-related variables (%p2PSA and tPSA), PV, and age in Cohort 1 and then validated the models in Cohort 2. The four LR models (LR-1, 2, 3, and 4) were described as follows: (a) model LR-1/LR-2 predicted PCa/clinically significant PCa using the variables of age, PV, and phi; (b) model LR-3/LR-4 predicted PCa/clinically significant PCa using the variables of age, PV, %p2PSA, and tPSA.
|Table 2: Multivariable logistic regression including age, prostate volume, and prostate health index for the prediction of prostate cancer/clinically significant prostate cancer (Gleason score ≥7)|
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Comparisons of the AUCs among the phi-PV derivatives, models, and phi are shown in [Table 3] and [Table 4]. Briefly, the AUCs of PHID and PHIV did not outperform phi for predicting PCa or clinically significant PCa [Table 3] and [Table 4]; ROC curves, [Supplementary Figure 2 [Additional file 6]], [Supplementary Figure 3 [Additional file 7]], [Supplementary Figure 4 [Additional file 8]]. Despite the overfitting effect of the models in Cohort 1, all models in Cohort 2 did not outperform phi(all P > 0.05) for predicting either PCa or clinically significant PCa. Similar results are shown in [Supplementary Table 5 [Additional file 9]] when predicting PCa with GS ≥8. These results indicated that PV would not provide additional predictive value to phi.
|Table 3: Area under receiver operating characteristic curves of different measurements for predicting prostate cancer in entire cohorts and subsets grouped by total prostate-specific antigen|
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|Table 4: Area under receiver operating characteristic curves of different measurements for predicting clinically significant prostate cancer (Gleason score .7) in entire cohorts and subsets|
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| Discussion|| |
The objective of this study was to evaluate the association between phi and PV and to determine whether PV would provide additional predictive value to phi. We found that (1) p2PSA was significantly associated with PV, while there was no association between phi and PV, and (2) neither phi-PV derivatives (PHID or PHIV) nor phi-PV multivariate models outperformed phi for predicting PCa or clinically significant PCa. These results suggested that phi might not be influenced by PV and adding PV might not provide additional predictive value to phi.
A single-center study demonstrated that PHID outperformed phi in predicting clinically significant PCa. The highest discriminative ability was observed for PHID in predicting clinically significant disease (with an AUC of 0.84), which was significantly higher than phi (AUC = 0.76). That study only included 118 men with elevated PSA and negative DRE who underwent a phi test and prostate biopsy, while all patients had a phi test in our study. In our multicenter study, with a two-step external validation, PHID did not significantly outperform phi for predicting PCa or clinically significant PCa. The differences observed by Tosoianet al. might be due to its relatively small sample size.
Based on our results, PV would not improve the predictive abilities of phi, suggesting that regardless of PV, phi alone could independently predict PCa and clinically significant PCa. Although the molecular mechanisms are not clear, there are several assumptions that might explain these results. First, p2PSA is considered a “prostate cancer-specific antigen” rather than a prostate-specific antigen. One study showed that p2PSA had higher immunostaining in prostate tumor tissues than in benign prostate tissues. Therefore, tumor volume rather than PV would be a major factor for p2PSA value. Second, both tPSA and fPSA have a positive linear association with PV. However, the influence of PV might have been partially adjusted using tPSA0.5/fPSA.
There were several limitations of this study. First, the PVs were all calculated through a transrectal approach (TRUS), which might cause subjective error among different ultrasonologists. However, all volumes of prostate were measured by skilled ultrasonologists, with a minimum of 5 years of working experience in our study. A recent study demonstrated that PHID appears to have greater predictive performance than phi when prostate biopsies were guided by image fusion of MR and transrectal ultrasound. However, we were not able to perform similar analyses in the present study due to the lack of MRI data from our study subjects. MR-TRUS fusion biopsy will be applied in future studies to address this problem. Second, all medical centers participating in the present study were located in Shanghai, a large city in East China, which may cause selection bias. However, individuals all over the country seek the services of these tertiary hospitals.
In conclusion, PV-based derivatives (both PHIV and PHID) and correlative models do not improve the predictive abilities of phi for both PCa and clinically significant PCa.
| Author Contributions|| |
RN, DFX, JFX, and YHS conceived and designed the study. DWY, JQ, FL, BTH, SLZ, and QD contributed materials and collected the data. DH, YSW, RN, and DFX analyzed the data. DH, YSW, RN, and DFX wrote the manuscript. RN, DFX, JFX, and YHS supervised the study. DH and YSW contributed equally to this study. All authors have read and approved the final manuscript.
| Competing Interests|| |
In the present study, we declare that Beckman Coulter, Inc., provided the tests for tPSA, fPSA, and p2PSA. All the sample tests, data analyses, and manuscript writing were performed by the researchers, independent from Beckman Coulter, Inc. There are no other potential competing interests to be declared.
| Acknowledgments|| |
We thank all subjects included in this study. We thank Beckman Coulter, Inc., for the measurements of tPSA, fPSA, and p2PSA. This work was in part supported by grants from the innovation grant by Shanghai Hospital Development Center (SHDC12015105) to Jianfeng Xu, the National Natural Science Foundation of China (Grant No. 81772741), Shanghai Rising-Star Program (Grant No. 18QA1402800), the “Chen Guang” project supported by Shanghai Municipal Education Commission, and Shanghai Education Development Foundation, Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (Grant No. 20181701) to Rong Na.
Supplementary Information is linked to the online version of the paper on the Asian Journal of Andrology website.
| References|| |
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, et al.
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin
2018; 68: 394–424.
Welch HG, Schwartz LM, Woloshin S. Prostate-specific antigen levels in the United States: implications of various definitions for abnormal. J Natl Cancer Inst
2005; 97: 1132–7.
Ankerst DP, Thompson IM. Sensitivity and specificity of prostate-specific antigen for prostate cancer detection with high rates of biopsy verification. Arch Ital Urol Androl
2006; 78: 125–9.
Sakr WA, Grignon DJ, Crissman JD, Heilbrun LK, Cassin BJ, et al.
High grade prostatic intraepithelial neoplasia (HGPIN) and prostatic adenocarcinoma between the ages of 20-69: an autopsy study of 249 cases. In Vivo
(Athens, Greece) 1994; 8: 439–43.
Sánchez-Chapado M, Olmedilla G, Cabeza M, Donat E, Ruiz A. Prevalence of prostate cancer and prostatic intraepithelial neoplasia in Caucasian Mediterranean males: an autopsy study. Prostate
2003; 54: 238–47.
Soos G, Tsakiris I, Szanto J, Turzo C, Haas PG, et al.
The prevalence of prostate carcinoma and its precursor in Hungary: an autopsy study. Eur Urol
2005; 48: 739–44.
Stamatiou K, Alevizos A, Perimeni D, Sofras F, Agapitos E. Frequency of impalpable prostate adenocarcinoma and precancerous conditions in Greek male population: an autopsy study. Prostate Cancer Prostatic Dis
2006; 9: 45.
Haas GP, Delongchamps NB, Jones RF, Chandan V, Serio AM, et al.
Needle biopsies on autopsy prostates: sensitivity of cancer detection based on true prevalence. J Natl Cancer Inst
2007; 99: 1484–9.
Zlotta AR, Egawa S, Pushkar D, Govorov A, Kimura T, et al.
Prevalence of prostate cancer on autopsy: cross-sectional study on unscreened Caucasian and Asian men. J Natl Cancer Inst
2013; 105: 1050–8.
Draisma G, Boer R, Otto SJ, van der Cruijsen IW, Damhuis RA, et al.
Lead times and overdetection due to prostate-specific antigen screening: estimates from the European randomized study of screening for prostate cancer. J Natl Cancer Inst
2003; 95: 868–78.
Zappa M, Ciatto S, Bonardi R, Mazzotta A. Overdiagnosis of prostate carcinoma by screening: an estimate based on the results of the Florence Screening Pilot Study. Ann Oncol
1998; 9: 1297–300.
Carter HB. Prostate cancers in men with low PSA levels-must we find them? N Engl J Med
2004; 350: 2292.
Etzioni R, Penson DF, Legler JM, Di Tommaso D, Boer R, et al.
Overdiagnosis due to prostate-specific antigen screening: lessons from US prostate cancer incidence trends. J Natl Cancer Inst
2002; 94: 981–90.
Hugosson J, Carlsson S, Aus G, Bergdahl S, Khatami A, et al.
Mortality results from the Göteborg randomised population-based prostate-cancer screening trial. Lancet Oncol
2010; 11: 725–32.
Na R, Ye D, Liu F, Chen H, Qi J, et al.
Performance of serum prostate specific antigen isoform [-2]proPSA (p2PSA) and the prostate health index (PHI) in a Chinese hospital-based biopsy population. Prostate
2014; 74: 1569–75.
Chiu PK, Roobol MJ, Teoh JY, Lee WM, Yip SY, et al.
Prostate health index (PHI) and prostate-specific antigen (PSA) predictive models for prostate cancer in the Chinese population and the role of digital rectal examination-estimated prostate volume. Int Urol Nephrol
2016; 48: 1631–7.
Na R, Ye D, Qi J, Liu F, Helfand BT, et al.
Prostate health index significantly reduced unnecessary prostate biopsies in patients with PSA 2-10 ng/ml and PSA>10 ng/ml: results from a multicenter study in China. Prostate
2017; 77: 1221–9.
Catalona WJ, Partin AW, Sanda MG, Wei JT, Klee GG, et al.
A multicenter study of [-2]pro-prostate specific antigen combined with prostate specific antigen and free prostate specific antigen for prostate cancer detection in the 2.0 to 10.0 ng/ml prostate specific antigen range. J Urol
2011; 185: 1650–5.
Guazzoni G, Nava L, Lazzeri M, Scattoni V, Lughezzani G, et al.
Prostate-specific antigen (PSA) isoform p2PSA significantly improves the prediction of prostate cancer at initial extended prostate biopsies in patients with total PSA between 2.0 and 10 ng/ml: results of a prospective study in a clinical setting. Eur Urol
2011; 60: 214–22.
Lazzeri M, Briganti A, Scattoni V, Lughezzani G, Larcher A, et al.
Serum index test %[-2]proPSA and Prostate Health Index are more accurate than prostate specific antigen and %fPSA in predicting a positive repeat prostate biopsy. J Urol
2012; 188: 1137–43.
Tosoian JJ, Druskin SC, Andreas D, Mullane P, Chappidi M, et al.
Use of the Prostate Health Index for detection of prostate cancer: results from a large academic practice. Prostate Cancer Prostatic Dis
2017; 20: 228.
Benson MC, Seong Whang I, Pantuck A, Ring K, Kaplan SA, et al.
Prostate specific antigen density: a means of distinguishing benign prostatic hypertrophy and prostate cancer. J Urol
1992; 147: 815–6.
Benson MC, Whang IS, Olsson CA, McMahon DJ, Cooner WH. The use of prostate specific antigen density to enhance the predictive value of intermediate levels of serum prostate specific antigen. J Urol
1992; 147: 817–21.
Tosoian JJ, Druskin SC, Andreas D, Mullane P, Chappidi M, et al.
Prostate Health Index density improves detection of clinically significant prostate cancer. BJU Int
2017; 120: 793–8.
Druskin SC, Tosoian JJ, Young A, Collica S, Srivastava A, et al.
Combining Prostate Health Index density, magnetic resonance imaging and prior negative biopsy status to improve the detection of clinically significant prostate cancer. BJU Int
2018; 121: 619–26.
Vendrami CL, McCarthy RJ, Chatterjee A, Casalino D, Schaeffer EM, et al.
The utility of prostate specific antigen density, prostate health index and prostate health index density in predicting positive prostate biopsy outcome is dependent on the prostate biopsy methods. Urology
2019; 129: 153–9.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics
1988; 44: 837–45.
Mikolajczyk SD, Marker KM, Millar LS, Kumar A, Saedi MS, et al.
A truncated precursor form of prostate-specific antigen is a more specific serum marker of prostate cancer. Cancer Res
2001; 61: 6958–63.
[Table 1], [Table 2], [Table 3], [Table 4]