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BY 4.0 license Open Access Published by De Gruyter May 16, 2022

Prostate health index (PHI) as a reliable biomarker for prostate cancer: a systematic review and meta-analysis

Luisa Agnello, Matteo Vidali ORCID logo, Rosaria Vincenza Giglio, Caterina Maria Gambino, Anna Maria Ciaccio, Bruna Lo Sasso and Marcello Ciaccio

Abstract

Objectives

Prostate cancer (PCa) represents the second most common solid cancer in men worldwide. In the last decades, the prostate health index (PHI) emerged as a reliable biomarker for detecting PCa and differentiating between non-aggressive and aggressive forms. However, before introducing it in clinical practice, more evidence is required. Thus, we performed a systematic review and meta-analysis for assessing the diagnostic performance of PHI for PCa and for detecting clinically significant PCa (csPCa).

Methods

Relevant publications were identified by a systematic literature search on PubMed and Web of Science from inception to January 11, 2022.

Results

Sixty studies, including 14,255 individuals, met the inclusion criteria for our meta-analysis. The pooled sensitivity and specificity of PHI for PCa detection was 0.791 (95%CI 0.739–0.834) and 0.625 (95%CI 0.560–0.686), respectively. The pooled sensitivity and specificity of PHI for csPCa detection was 0.874 (95%CI 0.803–0.923) and 0.569 (95%CI 0.458–0.674), respectively. Additionally, the diagnostic odds ratio was 6.302 and 9.206, respectively, for PCa and csPCa detection, suggesting moderate to good effectiveness of PHI as a diagnostic test.

Conclusions

PHI has a high accuracy for detecting PCa and discriminating between aggressive and non-aggressive PCa. Thus, it could be useful as a biomarker in predicting patients harbouring more aggressive cancer and guiding biopsy decisions.

Introduction

Prostate cancer (PCa) represents the most common solid tumour in men over 60 years and the second leading cause of cancer death in men, after lung cancer [1].

PCa is a very heterogeneous disease characterised by a wide spectrum of clinical manifestations, ranging from clinically insignificant forms to lethal castration-resistant ones. It has been estimated that more than 50% of patients has a low risk of progression [2]. In these patients, active surveillance instead of a radical surgery procedure is recommended. Noteworthy, the over-diagnosis and over-treatment of indolent tumours is major trouble associated with PCa. Thus, the early identification and the appropriate management of the patients is fundamental. In this scenario, laboratory medicine has a key role. Worldwide, the PCa screening is based on the use of the prostate-specific antigen (PSA). It is a serine protease, which physiologically dissolves seminal clots. The circulating PSA consists of 80–95% complexed forms and the small remaining proportion of free form. The test for measuring total PSA (tPSA) levels, including both complexed and free PSA (fPSA), was developed and approved by the Food and Drug Administration for PCa over 30 years ago [3]. However, the PSA-based screening has several drawbacks. First, PSA is organ-specific and not cancer-specific. Although it has high sensitivity, it has poor specificity and low positive predictive value (PPV), resulting in unnecessary biopsies. Additionally, PSA cannot accurately identify aggressive PCa [4], leading to over-diagnosing and over-treatment in patients with low-risk disease that may not require active clinical intervention. Indeed, up to 42% of PCa detected based on PSA are clinically insignificant. Consequently, the identification of patients with clinically significant PCa (csPCa), which requires treatment, is one of the main concerns in daily practice. Finally, PSA levels are influenced by several factors, such as benign prostatic hyperplasia, infection, age, and drug [5, 6]. Thus, there is active research for identifying reliable biomarkers to guide Clinicians in the detection of PCa and its aggressive forms to appropriately treat the patient.

In the last decades, a role for the different forms of PSA has emerged. In the early 1990s, literature evidence showed that increased levels of fPSA are commonly associated with benign conditions [7, 8]. Noteworthy, fPSA consists of three different forms: benign PSA, intact inactive PSA, and proPSA. Among these, proPSA is the form associated with PCa. proPSA has several molecular isoforms, including [−2], [−4], and [−5, −7] [9]. The [−2] proPSA (p2PSA) is the most stable in serum. In 2010, the Beckman Coulter introduced an automated immunoassay for its detection and developed an index, namely the prostate health index (PHI), which is calculated by a mathematical combination of the values of tPSA, fPSA, and p2PSA, according to the following formula: (proPSA/fPSA)×√tPSA. In 2012, the FDA approved PHI for PCa detection in men with the following characteristics: (i) older than 50 years; (ii) PSA between 4 and 10 µg/L; (iii) or a non-suspicious digital rectal examination (DRE) [10]. Additionally, some Authors showed that PHI outperforms tPSA and fPSA in the detection of csPCa [11, 12].

Although several Authors showed that PHI has good analytical performance for detecting PCa, the European Association of Urology stated that there is too limited evidence to implement these tests into routine screening programs [13]. Also, the American Urological Association has declared that more evidence is required to confirm the reliability of PHI to decrease the number of unnecessary biopsies while keeping the capacity to detect csPCa [14].

The aim of this study was to assess the accuracy of PHI for detecting PCa and identifying csPCa.

Materials and methods

We followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines 2020 [15]. All studies investigating the diagnostic efficacy of PHI for PCa detection were searched for inclusion.

Literature search strategy

Two reviewers systematically and independently (LA and MV) performed a comprehensive electronic search of PubMed and Web of Science. The following Medical Subject Heading (MeSH) terms “Prostate Health Index”, “PHI”, “cancer prostate” and “tumor prostate” were used to search articles. No publication date restriction was applied, and the date of our search was until January 11, 2022.

Study selection

The inclusion criteria were: (i) retrospective and prospective study design; (ii) English language; (iii) sufficient data provided to calculate the outcome; (iv) PCa diagnosis confirmed on biopsy.

Exclusion criteria were: (i) evaluation of only the prognostic role of PHI; (ii) lack of evaluation of PHI accuracy; (iii) letters, case reports, animal studies, reviews, and meta-analysis (vi) other languages than English; (v) full-text not found.

Data collection

Two authors (LA and MV) independently collected data referring to studies and patient characteristics. The extracted information from each study included the first author’s name and year of publication, study design, inclusion criteria, study population, nr of positive biopsy, nr of csPCa, the calibration system used (WHO vs. Hybritech), PHI cut-off value, outcome data [sensitivity, specificity, true positive (TP), false negative (FP), true negative (TN), false positive (FP)].

Statistical analysis

Meta-analytical summaries of PHI performance were calculated following the bivariate binomial approach by fitting a generalized linear mixed model (GLMM) [16], [17], [18]. Summary pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio (DOR) were calculated by R Language v. 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria) and RStudio IDE v.1.3.1093 (RStudio, PBC, Boston, MA) with the lme4, mada and meta packages [19]. Pooled results were confirmed by importing data into the interactive application MetaDTA (Diagnostic Test Accuracy Meta-Analysis v. 2.01) hosted on the shinyapps server and available at https://crsu.shinyapps.io/dta_ma/ [20, 21]. Hierarchical summary receiver operating characteristic (HSROC) model parameters estimated by MetaDTA (lambda or accuracy parameter, theta or cut-point parameter, beta or shape parameter, the variance of the accuracy parameter and the variance of threshold parameter) were imported into the software Review Manager (RevMan) v. 5.4.1 (The Cochrane Collaboration, 2020) to obtain the HSROC plots [22]. Heterogeneity across the studies was evaluated by plotting sensitivities and specificities, together with their 95%CI, by Forest and Crosshair plots [23] and by inconsistency index (I2), calculated as 100%*(Q − df)/Q, where Q is Cochran’s heterogeneity statistic and df the degrees of freedom. Publication bias was evaluated by funnel plot and Deeks’s formal test.

Results

Study selection

The process of study selection is schematically presented in the PRISMA flow diagram (Figure 1). After the removal of duplicates, a total of 371 articles were obtained. After screening the title and abstracts, 273 studies were excluded because they were literature review, case reports, letters, or meta-analysis; they did not measure PHI; they did not evaluate the diagnostic accuracy of PHI for PCa detection. The full text of 92 studies was further evaluated. Finally, a total of 60 studies were included, 42 for PCa and 18 for csPCa analysis.

Figure 1: 
PRISMA 2020 study selection flow diagram.

Figure 1:

PRISMA 2020 study selection flow diagram.

Study characteristics and quality assessment

The main characteristics of all the studies included in the meta-analysis are reported in Table 1.

Table 1:

Characteristics of all the studies included in the meta-analysis.

Authors [ref] Study design Country Inclusion criteria Study population Positive biopsy csPCa Calibrators
Chiu et al. 2021 [24] Monocentric prospective observational Taiwan PSA 2–10 μg/L, and/or a suspicious DRE 412 134 94 Hybrithech
Ferro et al. 2021 [25] Monocentric prospective observational Italy PSA 2–10 μg/L, and/or a suspicious DRE 196 142 90 WHO
Garrido et al. 2021 [26] Monocentric prospective observational Portugal PSA between 2 and 10 μg/L and no previous history of PCa, irrespective of the DRE findings 237 118 100 Hybritech
Stejskal et al. 2021 [27] Multicentric prospective observational Czech republic Men planned for a prostate biopsy for elevated total PSA levels with negative DRE at four different hospitals 395 296 NA
Kim et al. 2020 [28] Multicentric prospective observational UK Increased PSA and mpMRI 545 395 256 NA
Nassir et al. 2020 [29] Monocentric retrospective observational Saudi Arabia tPSA of 4–10 μg/L, who were initially underwent prostate biopsy 194 71 NA
Othman et al. 2020 [30] Monocentric prospective observational Malaysia Consecutive men undergoing TRUS prostate biopsy for suspected PCa with tPSA level of ≤20 μg/L 84 25 8 Hybritech
Barisiene et al. 2020 [31] Multicentric prospective observational Lithuania Males older than 50 years old with tPSA range from 2 to 10 μg/L and normal DRE referred for prostate biopsies 210 112 81 Hybritech
Ito et al. 2020 [32] Multicentric prospective observational Japan (1) serum PSA higher than the age stratified cut-offs of 3 ng/mL at ages 50–64 years, 3.5 μg/L at 65–69 years and 4 ng/mL at 70 years old or older, and 10 ng/mL or less; (2) an initial prostate systematic biopsy within 3 months after informed consent; (3) the number of biopsy cores restricted to 12 to 20 with acceptance of an additional target biopsy of a hypoechoic region by transrectal ultrasound or a suspicious region by MRI; (4) men between ages 50 and 79 years; (5) TRUS findings of abnormality, and total and transition zone prostate volume within 6 months before prostate biopsy; and (6) optional MRI before prostate biopsy 363 179 NA
Kopecký et al. 2020 [33] Monocentric prospective observational Poland Patients suspected of having PCa 55 31 NA
Stojadinovic et al. 2020 [34] Monocentric retrospective observational Serbia Men with PSA ≤10.0 μg/L who underwent transrectal, ultrasound guided prostate biopsy and PHI testing 200 88 35 NA
Hsieh et al. 2020 [35] Monocentric prospective observational Asian Patients who were more than 40 years and underwent prostate biopsy for suspicious PCa due to elevated serum PSA level (PSA >4 μg/L) and/or abnormal findings on DRE 102 39 24 NA
Lopes et al. 2019 [36] Monocentric retrospective observational NA Patients with PHI test and 3 T MR exam with at least one suspicious MR identified lesion with a PI-RADS score of ≥3 prior to biopsy. 233 82 NA
Jagalarmudi et al. 2019 [37] Monocentric prospective observational NA Suspicion of PCa owing to a serum PSA level between 2 and 10 μg/L 140 49 WHO
Cheng et al. 2019 [38] Monocentric prospective observational Taiwan Patients underwent TRUSP biopsy for suspected prostate cancer, including patients with abnormal tPSA >4 μg/L <10 μg/L, or patients with abnormal DRE findings, whose tPSA <10 ng/mL 121 33 21 Hybritech
Sriplakich et al. 2018 [39] Monocentric prospective observational All patients with a serum PSA of 4 and 10 ng/mL and nonsuspicious DRE of prostate cancer 101 16 NA
Hsieh et al. 2018 [40] Monocentric prospective observational China Patients aged 50–75 years and a serum total PSA level 4.0 and 10.0 μg/L, with or without an abnormal DRE 154 36 26 NA
Dolejsova et al. 2018 [41] Monocentric prospective observational Czech republic Patients with the biopsy and following radical prostatectomy 320 320 225 NA
Park et al. 2018 [42] Multicentric prospective observational Korea Consecutive men aged 60–75 years with tPSA ≥3.5 μg/L who underwent their first prostate biopsy for suspected PCa 246 125 NA
Al Saidi et al. 2017 [43] Multicentric prospective observational Oman All men scheduled for prostate biopsy in their workup management during the study period 136 28 17 Hybritech
Na et al. 2017 [44] Multicentric prospective observational China (1) tPSA level >10.0 μg/L;(2) tPSA level >4.0 μg/L with confirmation after 2–3 months; (3) %fPSA<0.16 when patients had a total PSA level >4.0 μg/L; and (4) suspicious lesions detected by DRE or ultrasound at any level of tPSA 1,538 618 NA
Vukovic et al. 2017 [45] Monocentric prospective observational Serbia Patients with age over 50 years, no previous history of PC, normal DRE findings, serum PSA in interval between 2 and 10 μg/L, and minimally 12 biopsy cores taken from patient 129 65 NA
Furuya et al. 2017 [46] Monocentric prospective observational Japan tPSA values of 2.0–10.0 μg/L and the performance of MRI before the biopsy 50 33 NA
Friedl et al. 2017 [47] Monocentric retrospective observational Austria Suspicious prostate MRI 112 62 31 NA
Tan et al. 2017 (A) [48] Monocentric prospective observational Japan Patients with at least one PI-RADS 3 or higher lesion on mpMRI and who underwent both targeted and systematic prostatic biopsies in the same session 115 51 40 NA
Tan et al. 2017 (B) [49] Multicentric prospective observational Malaysia Patients 50–75 years of age with normal DRE in a total PSA range of 4–10 μg/L 157 30 19 NA
Chiu et al. 2016 [50] Monocentric prospective observational China Patients with PSA 4–10 μg/L and non-suspicious DRE, with or without lower urinary tract symptoms, who consented before prostate biopsy. 569 62 16 Hybritech
Morote et al. 2016 [51] Monocentric retrospective observational Spain Men younger than 75 and tPSA between 3 and 10 μg/L, scheduled to their first TRUS guided biopsy 183 68 45 NA
Lazzeri et al. 2016 [52] Multicentric retrospective observational Italy, France, Spain, Germany, UK Patients >45 years of age with or without a positive DRE, with or without a previous negative biopsy with a tPSA 4–10 μg/L 262 136 106 NA
Yu et al. 2016 [53] Multicentric prospective observational China (1) tPSA >4.0 μg/L; (2) %fPSA ratio <0.16; (3) PSAD >0.15; or (4) presence of prostate nodules detected by DRE or ultrasound 261 67 30 NA
Fuchsova et al. 2015 [54] Monocentric prospective observational Czech republic Patients suspected of having PCa, with total PSA ranging from 0 to 20 μg/L, and underwent TRUS biopsies. 263 113 NA
Mearini et al. 2015 [55] Monocentric prospective observational Italy NA 43 43 14 NA
Loeb et al. 2015 [56] Multicentric prospective observational USA Men 50 years old or older with PSA 2–10 μg/L and benign findings on DRE 658 324 160 NA
Seisen et al. 2015 [57] Monocentric prospective observational France Consecutive patients undergoing a first prostate biopsy for suspected PCa, based on at PSA ranging from 4 to 20 μg/L and/or an abnormal DRE 138 62 39 Hybritech
Fossati et al. 2015 [58] Multicentric retrospective observational Italy, France, Spain, Germany, UK Patients undergoing prostate biopsy for suspected PCa according to indications from their referring physicians, enrolled in the PROMEtheuS project who were aged<60 years 238 67 NA
Mearini et al. 2014 [59] Monocentric prospective observational Italy Patients with a tPSA between 2.0 and 10 μg/L 275 86 NA
Filella et al. 2014 [60] Monocentric prospective and retrospective observational Spain Patients selected for biopsy because of an elevated serum PSA level and/or abnormal DRE, as well as patients diagnosed of prostate cancer and referred to our hospital for treatment 354 175 70 NA
Porpiglia et al. 2014 [61] Monocentric prospective observational Italy Persistently increased PSA and/or positive DRE 170 52 24 Hybritech
Ng et al. 2014 [62] Monocentric retrospective observational China Patients who are suspected of having PCa, because of either an elevated level of serum PSA or an abnormal DRE 320 21 WHO
Lazzeri et al. 2014 [63] Monocentric prospective observational Italy, France, Spain, Germany, UK Patients >45 yr of age with or without a positive DRE in a total PSA range of 2–10 μg/L 646 264 NA
Scattoni et al. 2013 [64] Multicentric prospective observational Italy PSA between 2 and 15 μg/L, and/or positive DRE, who performed PBx 211 70 Hybritech
Ferro et al. 2013 [65] Monocentric prospective observational Italy Men aged over 50 years, no prior prostate surgery and biopsy, no bacterial acute or chronic prostatitis, no use of 5-a reductase inhibitors, PSA values included between 2 and 10 μg/L, availability of serum samples and corresponding clinical data and completion of at least a 16 core template biopsy after enrolment 300 108 WHO
Lazzeri et al. 2013 [66] Multicentric retrospective observational Italy, France, Spain, Germany, UK Sub-analysis of PRO-PSA multicentric European study (PROMEtheuS). The overall study population included patients undergoing prostate biopsy for suspected PCa according to indications from their referring physicians. Inclusion was limited to patients enrolled in thePROMEtheuS project who had a first-degree relative(father, brother, son) with PCa 158 71 47 NA
Stephan et al. 2013 (A) [67] Multicentric prospective and retrospective observational France, Germany tPSA results between 1.6 and 8.0 μg/L 1,362 668 228 WHO
Stephan et al. 2013 (B) [68] Multicentric prospective observational Germany Men scheduled for prostate biopsy owing to suspicious DRE, suspicious TRUS, or increased PSA concentration or PSA velocity 246 110 43 WHO
Perdonà et al. 2013 [69] Monocentric prospective observational Italy Men undergoing first biopsy 160 47 19
Ferro et al. 2012 [70] Monocentric prospective observational Italy Men aged over 50 years, no prior prostate surgery and biopsy, no bacterial acute or chronic prostatitis, no use of 5-α reductase inhibitors in the previous six months, PSA values included between 2 and 20 μg/L, negative DRE 151 48 36
Guazzoni et al. 2011 [71] Monocentric prospective observational Italy Men with tPSA 2.0–10 μg/L and negative DRE who were scheduled for prostate biopsy 268 107 52
Liang et al. 2011 [72] Monocentric retrospective observational USA PSA exceeding 2.5 μg/L, abnormal DRE or a family history of PCa 474 227 69

  1. csPCA, clinically significant prostate cancer; DRE, digital rectal exam; PSA, prostate specific antigen; tPSA, total PSA; mpMRI, multiparametric magnetic resonance imaging; TRUS, transrectal ultrasound; PHI, prostate health index; PI-RADS, prostate imaging reporting and data system; TRUSP, transrectal ultrasound-guided prostate biopsy; PSAD, PSA density; PBx, prostate biopsy; NA, not available information

The diagnostic performances of the studies for PCa and csPCa analysis are described in Tables 2 and 3, respectively.

Table 2:

Characteristics of the studies included in the meta-analysis for PCa analysis.

First author and year of publication [ref] PHI cut-off Se Sp n PCa TP FN TN FP
Guazzoni et al. 2011 [71] 48.5 0.429 0.9 268 107 46 61 145 16
Liang et al. 2011 [72] 39.09 0.38 0.8 474 227 86 141 198 49
Ferro et al. 2012 [70] 38.7 0.85 0.61 151 48 41 7 63 40
Ferro et al. 2013 [65] 31.6 0.9 0.4 300 108 97 11 77 115
Lazzeri et al. 2013 [66] 40.3 0.648 0.713 158 71 46 25 62 25
Perdonà et al. 2013 [69] 43.77 0.66 0.72 160 47 31 16 81 32
Scattoni et al. 2013 [64] 28.3 0.9 0.31 116 40 36 4 24 52
Stephan et al. 2013 (A) [67] NA 0.9 0.354 1,362 668 601 67 246 448
Stephan et al. 2013 (B) [68] 27.5 0.9 0.213 246 110 99 11 29 107
Filella et al. 2014 [60] 46.89 0.663 0.715 354 175 116 59 128 51
Lazzeri et al. 2014 [61] 41.5 0.629 0.623 646 264 166 98 238 144
Mearini et al. 2014 [59] 37.1 0.919 0.386 275 86 79 7 73 116
Ng et al. 2014 [62] 26.54 0.9 0.4976 230 21 19 2 104 105
Porpiglia et al. 2014 [61] 48.9 0.409 0.78 170 52 21 31 92 26
Fossati et al. 2015 [58] 41.2 0.642 0.632 238 67 43 24 108 63
Fuchsova et al. 2015 [54] 40 0.84 0.63 263 113 95 18 95 55
Loeb et al. 2015 [56] 31.3 0.8 0.461 658 324 259 65 154 180
Seisen et al. 2015 [57] 40 0.435 0.671 138 62 27 35 51 25
Chiu et al. 2016 [50] 35 0.613 0.822 569 62 38 24 417 90
Lazzeri et al. 2016 [52] 63.9 0.728 0.731 262 136 99 37 92 34
Morote et al. 2016 [51] 28.98 0.9 0.278 183 68 61 7 32 83
Yu et al. 2016 [53] 38.59 0.91 0.567 261 67 61 6 110 84
Al Saidi et al. 2017 [43] 41.9 0.821 0.806 136 28 23 5 87 21
Tan et al. 2017 [48] 26.75 0.9 0.5827 157 30 27 3 74 53
Friedl et al. 2017 [47] 40 0.92 0.33 112 62 57 5 17 33
Furuya et al. 2017 [46] 38.7 0.636 0.765 50 33 21 12 13 4
Na et al. 2017 [44] 32 0.945 05,228 1,538 618 584 34 481 439
Vukovic et al. 2017 [45] 41.67 0.641 0.625 129 65 42 23 40 24
Hsieh et al. 2018 [40] 29.6 0.778 0.678 154 36 28 8 80 38
Park et al. 2018 [42] 22.9 0.9 0.683 246 125 113 12 83 38
Sriplakich et al. 2018 [39] 34.14 0.75 0.753 101 16 12 4 64 21
Cheng et al. 2019 [38] 21.62 0.9 0.273 121 33 30 3 24 64
Jagalarmudi et al. 2019 [37] 21.33 0.9 0.24 140 49 44 5 22 69
Lopes et al. 2019 [36] 45.9 0.73 0.77 121 33 24 9 68 20
Barisiene et al. 2020 [31] 44.49 0.563 0.837 210 112 63 49 82 16
Ito el a. 2020 [32] NA 0.9 0.358 363 179 161 18 66 118
Kopecky et al. 2020 [33] 36.4 0.774 0.667 55 31 24 7 16 8
Othman et al. 2020 [30] 30.2 0.76 0.641 84 25 19 6 38 21
Nassir et al. 2020 [29] 33.14 0.831 0.797 194 71 59 12 98 25
Ferro et al. 2021 [25] 42.7 0.908 0.963 196 142 129 13 52 2
Garrido et al. 2021 [26] 37.96 0.7034 0.7899 237 118 83 35 94 25
Stejskal et al. 2021 [27] 40.775 0.657 0.763 395 296 194 102 76 23

  1. Se, sensitivity; Sp, specificity; n, number; PCa, prostate cancer; TP, true positive; FN, false negative; TN, true negative; FP, false positive; NA, not available information.

Table 3:

Characteristics of the studies included in the meta-analysis for csPCa analysis.

First author and year of publication [ref] PHI cut-off Se Sp n csPCa TP FN TN FP
Loeb et al. 2015 [56] 33.8 0.8 0.455 639 160 128 32 218 261
Mearini et al. 2015 [55] 67.6 0.8667 0.857 43 14 12 2 25 4
Seisen et al. 2015 [57] 40 0.667 0.737 138 39 26 13 73 26
Chiu et al. 2016 [50] 35 0.813 0.754 569 16 13 3 417 136
Morote et al. 2016 [51] 17.83 0.95 0.244 183 45 43 2 34 104
Tan et al. 2017 [48] 26.75 0.9 0.551 157 19 17 2 76 62
Tan et al. 2017 [49] 27 1 0.44 115 40 40 0 33 42
Furuya et al. 2017 [46] 30.7 0.857 0.345 50 21 18 3 10 19
Na et al. 2017 [44] 32 0.9754 0.479 1,538 488 476 12 503 547
Dolejsova et al. 2018 [41] 34.36 0.9511 0.2105 320 225 214 11 20 75
Barisiene et al. 2020 [31] 44.47 0.691 0.814 210 81 56 25 105 24
Hsieh et al. 2020 [36] 30 0.917 0.436 102 24 22 2 34 44
Stojadinovic et al. 2020 [34] 30.7 0.971 0.376 200 35 34 1 62 103
Chiu et al. 2021 [24] 31 0.9 0.453 412 94 85 9 144 174
Ferro et al. 2021 [25] 61.68 0.533 0.885 196 90 48 42 94 12
Garrido et al. 2021 [26] 37.96 0.78 0.781 237 100 78 22 107 30
Kim et al. 2021 [28] 33.4 0.9 0.4 140 48 43 5 37 55
Stejskal et al. 2021 [27] 49.47 0.595 0.73 395 364 217 147 23 8

  1. Se, sensitivity; Sp, specificity; N, number; csPCa, clinically significant prostate cancer; TP, true positive; FN, false negative; TN, true negative; FP, false positive; NA, not available information.

For PCa studies (n=42), the sample size included was between 50 and 1,538, with cut-off, sensitivity and specificity ranging, respectively, from 21.3 to 63.9, from 0.380 to 0.945 and from 0.213 to 0.963 (Table 2). For csPCa studies (n=18), the sample size included was between 43 and 1,538, with cut-off, sensitivity and specificity ranging, respectively, from 17.8 to 67.6, from 0.533 to 1.000 and from 0.211 to 0.885 (Table 3). The forest plots and the crosshair plots for sensitivity and specificity across the studies for PCa and csPCa, are reported in Figures 2 6. The plots suggest high variability for both sensitivity and specificity. No publication bias was detected by inspection of the funnel plot and formal Deeks’s test (p=0.659 and p=0.065, respectively for PCa and csPCa studies).

Figure 2: 
Forest plot of sensitivity of PHI for detecting PCa.
Studies were ordered following date of publication.

Figure 2:

Forest plot of sensitivity of PHI for detecting PCa.

Studies were ordered following date of publication.

Figure 3: 
Forest plot of specificity of PHI for detecting PCa.
Studies were ordered following date of publication.

Figure 3:

Forest plot of specificity of PHI for detecting PCa.

Studies were ordered following date of publication.

Figure 4: 
Forest plot of sensitivity of PHI for detecting csPCa.
Studies were ordered following date of publication.

Figure 4:

Forest plot of sensitivity of PHI for detecting csPCa.

Studies were ordered following date of publication.

Figure 5: 
Forest plot of specificity of PHI for detecting csPCa.
Studies were ordered following date of publication.

Figure 5:

Forest plot of specificity of PHI for detecting csPCa.

Studies were ordered following date of publication.

Figure 6: 
Crosshair plots of the sensitivity and specificity across the studies investigated for PCa (left) or csPCa-3 (right).

Figure 6:

Crosshair plots of the sensitivity and specificity across the studies investigated for PCa (left) or csPCa-3 (right).

Diagnostic accuracy of PHI for detecting PCa and csPCa

Due to the high heterogeneity observed in the sensitivity and specificity data [respectively, I2 93.6% (95%CI 92.1%–94.7%) and 95.3% (95%CI 94.4%–96.1%) for PCa studies; 92.3% (95%CI 89.3%–94.5%) and 95.4% (95%CI 94.0%–96.6%) for csPCa], a random-effects model was applied. Meta-analytical summaries of PHI performances were obtained following a bivariate binomial method by fitting a GLMM.

For PCa studies, penalized or unpenalized goodness-of-fit measures were AIC=688.1, BIC=700.2, LogLikelihood=−339.0 and deviance=678.1. The variance-covariance matrix of parameter estimates showed, respectively, variance of the logit(sensitivity)=0.0213, variance of the log(specificity)=0.0190 and covariance=−0.0129. Pooled results were as follows: sensitivity 0.791 (95%CI 0.739–0.834), specificity 0.625 (95%CI 0.560–0.686), positive likelihood ratio 2.110 (95%CI 1.838–2.424), negative likelihood ratio 0.335 (95%CI 0.280–0.401) and DOR 6.302 (95%CI 4.976–7.980).

For csPCa studies, penalized or unpenalized goodness-of-fit measures were AIC=281.6, BIC=289.5, LogLikelihood=−135.8 and deviance=271.6. The variance-covariance matrix of parameter estimates showed, respectively, variance of the logit(sensitivity)=0.0752, variance of the log(specificity)=0.0517 and covariance=−0.0460. Pooled results were as follows: sensitivity 0.874 (95%CI 0.803–0.923), specificity 0.569 (95%CI 0.458–0.674), positive likelihood ratio 2.030 (95%CI 1.647–2.502), negative likelihood ratio 0.220 (95%CI 0.155–0.314) and DOR 9.206 (95%CI 6.384–13.276).

The HSROC plots in Figures 7 and 8 report the points representing the sensitivity-specificity pairs of the single PCa and csPCA studies, the summary operating point (summary values for sensitivity and specificity) and the summary ROC curve, together with the 95% confidence region around the summary operating point and the 95% prediction region.

Figure 7: 
Hierarchical summary receiver operating characteristic (HSROC) plots for diagnostic accuracy of PHI in detecting PCa.
Open circles represent sensitivity-specificity pairs of the 42 included studies. Black circle indicates the summary operating point (summary values for sensitivity and specificity). The curve solid line represents the summary ROC curve, whereas the solid and dashed closed curves indicate, respectively, the 95% confidence region around the summary operating point and the 95% prediction region. The range of the summary ROC curve was limited from the min to the max specificity of the included studies.

Figure 7:

Hierarchical summary receiver operating characteristic (HSROC) plots for diagnostic accuracy of PHI in detecting PCa.

Open circles represent sensitivity-specificity pairs of the 42 included studies. Black circle indicates the summary operating point (summary values for sensitivity and specificity). The curve solid line represents the summary ROC curve, whereas the solid and dashed closed curves indicate, respectively, the 95% confidence region around the summary operating point and the 95% prediction region. The range of the summary ROC curve was limited from the min to the max specificity of the included studies.

Figure 8: 
Hierarchical summary receiver operating characteristic (HSROC) plots for diagnostic accuracy of PHI in detecting csPCa. Open circles represent sensitivity-specificity pairs of the 18 included studies. Black circle indicates the summary operating point (summary values for sensitivity and specificity). The curve solid line represents the summary ROC curve, whereas the solid and dashed closed curves indicate, respectively, the 95% confidence region around the summary operating point and the 95% prediction region. The range of the summary ROC curve was limited from the min to the max specificity of the included studies.

Figure 8:

Hierarchical summary receiver operating characteristic (HSROC) plots for diagnostic accuracy of PHI in detecting csPCa. Open circles represent sensitivity-specificity pairs of the 18 included studies. Black circle indicates the summary operating point (summary values for sensitivity and specificity). The curve solid line represents the summary ROC curve, whereas the solid and dashed closed curves indicate, respectively, the 95% confidence region around the summary operating point and the 95% prediction region. The range of the summary ROC curve was limited from the min to the max specificity of the included studies.

Discussion

In this systematic review and meta-analysis, we evaluated the accuracy of PHI as a biomarker of PCa and csPCa by analysing results from 60 studies, including a total of 14,255 individuals. The main findings of our meta-analysis can be summarised as follows: (i) the pooled sensitivity and specificity of PHI for PCa detection were 0.791 (95%CI 0.739–0.834) and 0.625 (95%CI 0.560–0.686), respectively; (ii) the pooled sensitivity and specificity of PHI for csPCa detection were 0.874 (95%CI 0.803–0.923) and 0.569 (95%CI 0.458–0.674), respectively; (iii) DOR was 6.302 and 9.206, respectively for PCa and csPCa detection, suggesting moderate to good effectiveness of PHI as a diagnostic test. Overall, our findings suggest that PHI has a high accuracy for detecting PCa and discriminating between aggressive and non-aggressive PCa. Thus, it could be useful as a biomarker in predicting patients harbouring more aggressive cancer and guiding biopsy decisions.

The early detection of PCa and the discrimination between benign and malignant forms is fundamental for the appropriate intervention. The gold standard for PCa diagnosis remains the biopsy. However, the laboratory has a key role in early identifying patients at high risk of PCa, eligible for biopsy. The most widely used screening biomarker worldwide is PSA. In the past, a one-size-fits-all approach based on PSA was used for early identifying PCa and consequently determining the need for prostate biopsy in all men. However, PSA is characterised by a low specificity for PCa and it is not associated with the aggressiveness of cancer. In the last decades, multiparametric magnetic resonance imaging (mpMRI) of the prostate has emerged as the gold standard for predicting positive biopsy [73]. The Prostate Imaging Reporting and Data System (PI-RADS) score released by an international collaboration of the American College of Radiology (ACR) and European Society of Uroradiology (ESUR) in 2015 is a structured reporting schema that helps to determine the risk of csPCa on prostate mpMRI. The PI-RADS score ranges from 1 to 5 and it should be interpreted as follows: 1–2=low risk of PCa; 3=intermediate risk of PCa; and 4–5=high risk of PCa. PI-RADS 3 represents a “gray zone”, with only 15% of patients having PCa. Additionally, PPV has been reported to be 0.49 for csPCa, and a few patients with a negative mpMRI have high-grade PCa [74]. Thus, mrMRI presents some limitations in selecting patients to undergo biopsy [75], [76], [77]. It should also be considered that mpMRI is an expensive tool and requires an experienced radiologist.

The drawbacks of PSA and mpMRI could be overcome by the most recent developed index PHI.

The latter should be used in clinical practice as a complementary test to PSA and mpMRI. Indeed, PHI should be evaluated when PSA has a value within the “gray zone”, between 2 and 10 µg/L, allowing to spare unnecessary biopsies and to select patients for active surveillance. Similarly, it could be used when a PI-RADS 3 is obtained. Some Authors also tested if PHI could be used as an alternative test to mpMRI, but less evidence is available to date [25, 27, 61].

Interestingly, our data show that PHI could reliably detect patients with more aggressive PCa. The association between PHI and PCa aggressiveness is supported by literature evidence. Several Authors reported a significant correlation between PHI levels and histological features of tumor malignancy, such as grade, stage, and volume, evaluated after radical prostatectomy [78, 79]. Additionally, some Authors showed that PHI could predict the biochemical recurrence (BCR) of the PCa [80, 81]. The performance of PHI for predicting csPCa has been evaluated alone or in combination with other tools. Hsieh et al. showed that the combination of mpMRI and PHI has a better predictive power for csPCa than PHI and mpMRI alone and would have avoided up to 50% of biopsies while missing only one csPCa patient [35]. Kim et al. proposed a strategy based on the use of PHI as a triage test for identifying patients eligible for mpMRI and/or biopsy [28]. Such a strategy could be effective, efficient, and cheap, allowing the selection of only high-risk patients for more laborious and expensive investigations. Foj et al. recently developed a nomogram also incorporating PHI to address the individual probability of aggressive PCa in patients at biopsy [82]. Similarly, Loeb et al. developed a nomogram including PHI [56], showing that adding PHI to currently available risk prediction tools significantly improved the prediction of aggressive prostate cancer.

Some observations should be made because some issues hamper the introduction of PHI in clinical practice. First, there is no consensus on the optimal decisional cut-off for both detecting PCa and csPCa, with a high variability of proposed PHI values, ranging from 21.33 to 63.9 for PCa and from 26.7 to 67.6 for csPCa. This could be related to the high heterogeneity among studies in terms of sample size, inclusion criteria adopted, and the use of different calibrations (Table 1). Specifically, the Beckman Coulter gives the possibility to calibrate the PSA according to the Hybritech method or the WHO standard. However, there is a discrepancy of 16–20% between the PHI values obtained using the two calibrations, with WHO calibration turning out lower PHI values than Hybritech ones [83]. Thus, different cut-offs should be adopted according to the calibration method chosen. Additionally, some Authors established the best cut-off PHI according to the Youden Index, others according to the best sensitivity and others according to the best specificity. When selecting a test cut-off, which maximises sensitivity or specificity or a trade-off between them, several elements should be taken into consideration, among them the prevalence of the disease in the population or in a particular subgroup, combination with the result of other biomarkers or procedures (i.e. DRE, PSA), risk of unnecessary further procedures (i.e. biopsy) and potential post-procedure complications, missed diagnoses and economic impact. Although some cost-consequence analysis studies have been performed to assess the impact of different PHI cut-offs, it is not entirely clear if these results are applicable to different populations, at what stage of the diagnostic process or with other biomarkers PHI should be used, or if missed diagnoses are true missed or instead delayed diagnoses [84]. It is reasonable to argue that different cut-offs could be applied to different subgroups of patients based on disease prevalence or a specific diagnostic strategy (rule-in vs. rule-out, single vs. multiple biomarkers, population vs. high-risk patients). Many other studies are needed to evaluate and define specific PHI cut-offs.

Finally, prostate volume (PV) could influence the heterogeneity of PHI results among studies. Interestingly, Filella et al. showed that the diagnostic performance of PHI changes according to PV, with the highest accuracy in patients with small prostate volume [85]. Moreover, several Authors described an association between PV and PCa as well as tPSA. Accordingly, the PHI density (PHID), calculated as PHI/PV, has been introduced. Mearini et al. first assessed the value of PHID in PCa detection showing a good diagnostic accuracy but comparable to those of PHI [86]. Tosoian et al. found that PHID outperformed PHI for detecting csPCa [79]. Conversely, Friedl et al. reported a higher AUC of PHI than PHID [87]. Stephan et al., in a prospective large cohort study showed that PHID had better accuracy than PHI for detecting PCa but not csPCa [88]. Overall, the contrasting literature evidence achieved to date cannot to draw conclusions whether PV could improve the predictive ability of PHI. Thus, more studies are required to evaluate the usefulness of PHID for PCa and csPCa detection.

A cost-effectiveness strategy based on the best combination of PSA, PHI and mpMRI for detecting patients at high risk of PCa eligible for biopsy and with more aggressive forms should be developed, validated, and integrated into the guidelines. For this purpose, large-multicentre randomized-controlled studies are mandatory.

In conclusion, our data show that PHI is a reliable biomarker of PCa and csPCa. Nowadays, Clinicians have valuable tools for triaging patients at risk of PCa. Thus, the clinical paradigm should be shifted toward a more personalized approach to prostate biopsy decisions based on a multiparameter approach integrating biomarkers, including PSA and PHI, and clinical findings from mpMRI.


Corresponding author: Prof. Marcello Ciaccio, Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, University Hospital “P. Giaccone”, Palermo, Italy; and Department of Laboratory Medicine, AOUP “P. Giaccone”, Palermo, Italy, E-mail:
Luisa Agnello and Matteo Vidali contributed equally to this work.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

References

1. EU Science Hub. Cancer incidence and mortality in EU-27 countries; 2020. Available from: https://ec.europa.eu/jrc/en/news/2020-cancer-incidence-and-mortality-eu-27-countries.Search in Google Scholar

2. Klotz, L. Low-risk prostate cancer can and should often be managed with active surveillance and selective delayed intervention. Nat Clin Pract Urol 2008;5:2–3. https://doi.org/10.1038/ncpuro0993.Search in Google Scholar PubMed

3. Haas, GP, Delongchamps, N, Brawley, OW, Wang, CY, de la Roza, G. The worldwide epidemiology of prostate cancer: perspectives from autopsy studies. Can J Urol 2008;15:3866–71.Search in Google Scholar

4. McGrath, S, Christidis, D, Perera, M, Hong, SK, Manning, T, Vela, I, et al.. Prostate cancer biomarkers: are we hitting the mark? Prostate Int 2016;4:130–5. https://doi.org/10.1016/j.prnil.2016.07.002.Search in Google Scholar PubMed PubMed Central

5. US Preventive Services Task Force, Grossman, DC, Curry, SJ, Owens, DK, Bibbins-Domingo, K, Caughey, AB, Davidson, KW, et al.. Screening for prostate cancer: US preventive services task force recommendation statement. JAMA 2018;319:1901–13. https://doi.org/10.1001/jama.2018.3710.Search in Google Scholar PubMed

6. Sharma, S, Zapatero-Rodríguez, J, O’Kennedy, R. Prostate cancer diagnostics: clinical challenges and the ongoing need for disruptive and effective diagnostic tools. Biotechnol Adv 2017;35:135–49. https://doi.org/10.1016/j.biotechadv.2016.11.009.Search in Google Scholar PubMed

7. Lilja, H, Christensson, A, Dahlén, U, Matikainen, MT, Nilsson, O, Pettersson, K, et al.. Prostate-specific antigen in serum occurs predominantly in complex with alpha 1-antichymotrypsin. Clin Chem 1991;37:1618–25. https://doi.org/10.1093/clinchem/37.9.1618.Search in Google Scholar

8. Stenman, UH, Leinonen, J, Alfthan, H, Rannikko, S, Tuhkanen, K, Alfthan, O. A complex between prostate-specific antigen and alpha 1-antichymotrypsin is the major form of prostate-specific antigen in serum of patients with prostatic cancer: assay of the complex improves clinical sensitivity for cancer. Cancer Res 1991;51:222–6.Search in Google Scholar

9. Mikolajczyk, SD, Catalona, WJ, Evans, CL, Linton, HJ, Millar, LS, Marker, KM, et al.. Proenzyme forms of prostatespecifc antigen in serum improve the detection of prostate cancer. Clin Chem 2004;50:1017–25. https://doi.org/10.1373/clinchem.2003.026823.Search in Google Scholar PubMed

10. Ferro, M, De Cobelli, O, Lucarelli, G, Porreca, A, Busetto, GM, Cantiello, F, et al.. Beyond PSA: the role of prostate health index (phi). Int J Mol Sci 2020;21:1184. https://doi.org/10.3390/ijms21041184.Search in Google Scholar PubMed PubMed Central

11. Lepor, A, Catalona, WJ, Loeb, S. The prostate health index: its utility in prostate cancer detection. Urol Clin North Am 2016;43:1–6. https://doi.org/10.1016/j.ucl.2015.08.001.Search in Google Scholar PubMed PubMed Central

12. Stephan, C, Vincendeau, S, Houlgatte, A, Cammann, H, Jung, K, Semjonow, A. Multicenter evaluation of [−2] proprostate-specific antigen and the prostate health index for detecting prostate cancer. Clin Chem 2013;59:306–14. https://doi.org/10.1373/clinchem.2012.195784.Search in Google Scholar PubMed

13. European Association of Urology. EAU guidelines: prostate cancer: Uroweb; 2022. Available from: https://uroweb.org/guideline/prostate-cancer/#5.Search in Google Scholar

14. American Urological Association. Prostate cancer: early detection guideline – American urological association; 2013. Available from: https://www.auanet.org/guidelines/prostate-cancer-early-detection-guideline.Search in Google Scholar

15. Page, MJ, McKenzie, JE, Bossuyt, PM, Boutron, I, Hoffmann, TC, Mulrow, CD, et al.. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. PLoS Med 2021;18:e1003583. https://doi.org/10.1371/journal.pmed.1003583.Search in Google Scholar PubMed PubMed Central

16. Hamza, TH, van Houwelingen, HC, Stijnen, T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol 2008;61:41–51. https://doi.org/10.1016/j.jclinepi.2007.03.016.Search in Google Scholar PubMed

17. Jackson, D, Law, M, Stijnen, T, Viechtbauer, W, White, IR. A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Stat Med 2018;37:1059–85. https://doi.org/10.1002/sim.7588.Search in Google Scholar PubMed PubMed Central

18. Chu, H, Cole, SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 2006;59:1331–2. https://doi.org/10.1016/j.jclinepi.2006.06.011.Search in Google Scholar PubMed

19. Software for meta-analysis of DTA studies; 2012. Available from: https://methods.cochrane.org/sdt/software-meta-analysis-dta-studies [Accessed 5 Feb 2022].Search in Google Scholar

20. Patel, A, Cooper, NJ, Freeman, SC, Sutton, AJ. Graphical enhancements to summary receiver operating characteristic plots to facilitate the analysis and reporting of meta-analysis of diagnostic test accuracy data. Res Synth Methods 2021;12:34–44. https://doi.org/10.1002/jrsm.1439.Search in Google Scholar PubMed

21. Freeman, SC, Kerby, CR, Patel, A, Cooper, NJ, Quinn, T, Sutton, AJ. Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Med Res Methodol 2019;81:1–11. https://doi.org/10.1186/s12874-019-0724-x.Search in Google Scholar PubMed PubMed Central

22. Arends, LR, Hamza, TH, van Houwelingen, JC, Heijenbrok-Kal, MH, Hunink, MG, Stijnen, T. Bivariate random effects meta-analysis of ROC curves. Med Decis Making 2008;28:621–38. https://doi.org/10.1177/0272989x08319957.Search in Google Scholar

23. Phillips, B, Stewart, LA, Sutton, AJ. ‘Cross hairs’ plots for diagnostic meta-analysis. Res Synth Methods 2010;1:308–15. https://doi.org/10.1002/jrsm.26.Search in Google Scholar PubMed

24. Chiu, ST, Cheng, YT, Pu, YS, Lu, YC, Hong, JH, Chung, SD, et al.. Prostate health index density outperforms prostate health index in clinically significant prostate cancer detection. Front Oncol 2021;11:772182. https://doi.org/10.3389/fonc.2021.772182.Search in Google Scholar PubMed PubMed Central

25. Ferro, M, Crocetto, F, Bruzzese, D, Imbriaco, M, Fusco, F, Longo, N, et al.. Prostate health index and multiparametric MRI: partners in crime fighting overdiagnosis and overtreatment in prostate cancer. Cancers 2021;13:4723. https://doi.org/10.3390/cancers13184723.Search in Google Scholar PubMed PubMed Central

26. Garrido, MM, Marta, JC, Bernardino, RM, Guerra, J, Fernandes, F, Pereira, MH, et al.. The percentage of [−2] pro-prostate-specific antigen and the prostate health index outperform prostate-specific antigen and the percentage of free prostate-specific antigen in the detection of clinically significant prostate cancer and can be used as reflex tests. Arch Pathol Lab Med 2021. https://doi.org/10.5858/arpa.2021-0079-OA.Search in Google Scholar PubMed

27. Stejskal, J, Adamcová, V, Záleský, M, Novák, V, Čapoun, O, Fiala, V, et al.. The predictive value of the prostate health index vs. multiparametric magnetic resonance imaging for prostate cancer diagnosis in prostate biopsy. World J Urol 2021;39:1889–95. https://doi.org/10.1007/s00345-020-03397-4.Search in Google Scholar PubMed

28. Kim, L, Boxall, N, George, A, Burling, K, Acher, P, Aning, J, et al.. Clinical utility and cost modelling of the phi test to triage referrals into image-based diagnostic services for suspected prostate cancer: the PRIM (Phi to RefIne Mri) study. BMC Med 2020;18:95. https://doi.org/10.1186/s12916-020-01548-3.Search in Google Scholar PubMed PubMed Central

29. Nassir, AM, Kamel, HFM. Explication of the roles of prostate health index (PHI) and urokinase plasminogen activator (uPA) as diagnostic and predictor tools for prostate cancer in equivocal PSA range of 4–10 ng/mL. Saudi J Biol Sci 2020;27:1975–84. https://doi.org/10.1016/j.sjbs.2020.04.004.Search in Google Scholar PubMed PubMed Central

30. Othman, H, Yamin, AHA, Isa, ND, Bahadzor, B, Zakaria, SZS. Diagnostic performance of prostate health index (PHI) in predicting prostate cancer on prostate biopsy. Malays J Pathol 2020;42:209–14.Search in Google Scholar

31. Barisiene, M, Bakavicius, A, Stanciute, D, Jurkeviciene, J, Zelvys, A, Ulys, A, et al.. Prostate health index and prostate health index density as diagnostic tools for improved prostate cancer detection. BioMed Res Int 2020;2020:9872146. https://doi.org/10.1155/2020/9872146.Search in Google Scholar PubMed PubMed Central

32. Ito, K, Yokomizo, A, Tokunaga, S, Arai, G, Sugimoto, M, Akakura, K, et al.. Diagnostic impacts of clinical laboratory based p2PSA indexes on any grade, gleason grade group 2 or greater, or 3 or greater prostate cancer and prostate specific antigen below 10 ng/mL. J Urol 2020;203:83–91. https://doi.org/10.1097/ju.0000000000000495.Search in Google Scholar PubMed

33. Kopecký, J, Navláčilová, V, Janoutová, J, Janout, V. Epidemiological study on more accurate diagnosis of prostate cancer. Cent Eur J Publ Health 2020;28:65–9.10.21101/cejph.a5720Search in Google Scholar PubMed

34. Stojadinovic, M, Vukovic, I, Ivanovic, M, Stojadinovic, M, Milovanovic, D, Pantic, D, et al.. Optimal threshold of the prostate health index in predicting aggressive prostate cancer using predefined cost-benefit ratios and prevalence. Int Urol Nephrol 2020;52:893–901. https://doi.org/10.1007/s11255-019-02367-z.Search in Google Scholar PubMed

35. Hsieh, PF, Li, WJ, Lin, WC, Chang, H, Chang, CH, Huang, CP, et al.. Combining prostate health index and multiparametric magnetic resonance imaging in the diagnosis of clinically significant prostate cancer in an Asian population. World J Urol 2020;38:1207–14. https://doi.org/10.1007/s00345-019-02889-2.Search in Google Scholar PubMed PubMed Central

36. Vendrami, CL, McCarthy, RJ, Chatterjee, A, Casalino, D, Schaeffer, EM, Catalona, WJ, 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. Urol 2019;129:153–9. https://doi.org/10.1016/j.urology.2019.03.018.Search in Google Scholar PubMed PubMed Central

37. Jagarlamudi, KK, Zupan, M, Kumer, K, Fabjan, T, Hlebič, G, Eriksson, S, et al.. The combination of AroCell TK 210 ELISA with prostate health index or prostate-specific antigen density can improve the ability to differentiate prostate cancer from noncancerous conditions. Prostate 2019;79:856–63. https://doi.org/10.1002/pros.23791.Search in Google Scholar PubMed

38. Cheng, YT, Chiang, CH, Pu, YS, Liu, SP, Lu, YC, Chang, YK, et al.. The application of p2PSA% and prostate health index in prostate cancer detection: a prospective cohort in a Tertiary Medical Center. J Formos Med 2019;118:260–7. https://doi.org/10.1016/j.jfma.2018.05.001.Search in Google Scholar PubMed

39. Sriplakich, S, Lojanapiwat, B, Chongruksut, W, Phuriyaphan, S, Kitirattakarn, P, Jun-Ou, J, et al.. Prospective performance of the prostate health index in prostate cancer detection in the first prostate biopsy of men with a total prostatic specific antigen of 4–10 ng/mL and negative digital rectal examination. Prostate Int 2018;6:136–9. https://doi.org/10.1016/j.prnil.2018.02.002.Search in Google Scholar PubMed PubMed Central

40. Hsieh, PF, Chang, CH, Yang, CR, Huang, CP, Chen, WC, Yeh, CC, et al.. Prostate health index (PHI) improves prostate cancer detection at initial biopsy in Taiwanese men with PSA 4–10 ng/mL. Kaohsiung J Med Sci 2018;34:461–6. https://doi.org/10.1016/j.kjms.2018.02.007.Search in Google Scholar PubMed

41. Dolejsova, O, Kucera, R, Fuchsova, R, Topolcan, O, Svobodova, H, Hes, O, et al.. The ability of prostate health index (PHI) to predict gleason score in patients with prostate cancer and discriminate patients between gleason score 6 and gleason score higher than 6-A study on 320 patients after radical prostatectomy. Technol Cancer Res Treat 2018;17. https://doi.org/10.1177/1533033818787377.Search in Google Scholar PubMed PubMed Central

42. Park, H, Lee, SW, Song, G, Kang, TW, Jung, JH, Chung, HC, et al.. Diagnostic performance of %[−2]proPSA and prostate health index for prostate cancer: prospective, multi-institutional study. J Korean Med Sci 2018;33:e94. https://doi.org/10.3346/jkms.2018.33.e94.Search in Google Scholar PubMed PubMed Central

43. Al Saidi, SS, Al Riyami, NB, Al Marhoon, MS, Al Saraf, MS, Al Busaidi, SS, Bayoumi, R, et al.. Validity of prostate health index and percentage of [−2] pro-prostate-specific antigen as novel biomarkers in the diagnosis of prostate cancer: Omani tertiary hospitals experience. Oman Med J 2017;32:275–83. https://doi.org/10.5001/omj.2017.55.Search in Google Scholar PubMed PubMed Central

44. Na, R, Ye, D, Qi, J, Liu, F, Helfand, BT, Brendler, CB, 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. https://doi.org/10.1002/pros.23382.Search in Google Scholar PubMed

45. Vukovic, I, Djordjevic, D, Bojanic, N, Babic, U, Soldatovic, I. Predictive value of [−2]propsa (p2psa) and its derivatives for the prostate cancer detection in the 2.0 to 10.0ng/mL PSA range. Int Braz J Urol 2017;43:48–56. https://doi.org/10.1590/s1677-5538.ibju.2016.0256.Search in Google Scholar

46. Furuya, K, Kawahara, T, Narahara, M, Tokita, T, Fukui, S, Imano, M, et al.. Measurement of serum isoform [−2]proPSA derivatives shows superior accuracy to magnetic resonance imaging in the diagnosis of prostate cancer in patients with a total prostate-specific antigen level of 2–10 ng/mL. Scand J Urol 2017;51:251–7. https://doi.org/10.1080/21681805.2017.1298155.Search in Google Scholar PubMed

47. Friedl, A, Stangl, K, Bauer, W, Kivaranovic, D, Schneeweiss, J, Susani, M, et al.. Prostate-specific antigen parameters and prostate health index enhance prostate cancer prediction with the in-bore 3-T magnetic resonance imaging-guided transrectal targeted prostate biopsy after negative 12-core biopsy. Urol 2017;110:148–53. https://doi.org/10.1016/j.urology.2017.08.019.Search in Google Scholar PubMed

48. Tan, TW, Png, KS, Lee, CH, Yuwono, A, Yeow, Y, Chong, KT, et al.. MRI fusion-targeted transrectal prostate biopsy and the role of prostate-specific antigen density and prostate health index for the detection of clinically significant prostate cancer in southeast Asian men. J Endourol 2017;31:1111–6. https://doi.org/10.1089/end.2017.0485.Search in Google Scholar PubMed

49. Tan, LG, Tan, YK, Tai, BC, Tan, KM, Gauhar, V, Tiong, HY, et al.. Prospective validation of %p2PSA and the prostate health index, in prostate cancer detection in initial prostate biopsies of Asian men, with total PSA 4–10 ng mL−1. Asian J Androl 2017;19:286–90. https://doi.org/10.4103/1008-682X.168687.Search in Google Scholar PubMed PubMed Central

50. Chiu, PK, Teoh, JY, Lee, WM, Yee, CH, Chan, ES, Hou, SM, et al.. Extended use of prostate health index and percentage of [−2]pro-prostate-specific antigen in Chinese men with prostate specific antigen 10–20 ng/mL and normal digital rectal examination. Investig Clin Urol 2016;57:336–42. https://doi.org/10.4111/icu.2016.57.5.336.Search in Google Scholar PubMed PubMed Central

51. Morote, J, Celma, A, Planas, J, Placer, J, Ferrer, R, de Torres, I, et al.. Diagnostic accuracy of prostate health index to identify aggressive prostate cancer. An institutional validation study. Actas Urol Esp 2016;40:378–85. https://doi.org/10.1016/j.acuroe.2016.05.005.Search in Google Scholar

52. Lazzeri, M, Lughezzani, G, Haese, A, McNicholas, T, de la Taille, A, Buffi, NM, et al.. Clinical performance of prostate health index in men with tPSA>10ng/mL: results from a multicentric European study. Urol Oncol 2016;34:415.e13–9. https://doi.org/10.1016/j.urolonc.2016.04.003.Search in Google Scholar PubMed

53. Yu, GP, Na, R, Ye, DW, Qi, J, Liu, F, Chen, HT, et al.. Performance of the prostate health index in predicting prostate biopsy outcomes among men with a negative digital rectal examination and transrectal ultrasonography. Asian J Androl 2016;18:633–8. https://doi.org/10.4103/1008-682X.172823.Search in Google Scholar PubMed PubMed Central

54. Fuchsova, R, Topolcan, O, Windrichova, J, Hora, M, Dolejsova, O, Pecen, L, et al.. PHI in the early detection of prostate cancer. Anticancer Res 2015;35:4855–7.Search in Google Scholar

55. Mearini, L, Nunzi, E, Ferri, C, Bellezza, G, Lolli, C, Porrozzi, C, et al.. Use of the prostate health index for the detection of aggressive prostate cancer at radical prostatectomy. Urol Int 2015;95:390–9. https://doi.org/10.1159/000379758.Search in Google Scholar PubMed

56. Loeb, S, Sanda, MG, Broyles, DL, Shin, SS, Bangma, CH, Wei, JT, et al.. The prostate health index selectively identifies clinically significant prostate cancer. J Urol 2015;193:1163–9. https://doi.org/10.1016/j.juro.2014.10.121.Search in Google Scholar PubMed PubMed Central

57. Seisen, T, Rouprêt, M, Brault, D, Léon, P, Cancel-Tassin, G, Compérat, E, et al.. Accuracy of the prostate health index versus the urinary prostate cancer antigen 3 score to predict overall and significant prostate cancer at initial biopsy. Prostate 2015;75:103–11. https://doi.org/10.1002/pros.22898.Search in Google Scholar PubMed

58. Fossati, N, Buffi, NM, Haese, A, Stephan, C, Larcher, A, McNicholas, T, et al.. Preoperative prostate-specific antigen isoform p2PSA and its derivatives, %p2PSA and prostate health index, predict pathologic outcomes in patients undergoing radical prostatectomy for prostate cancer: results from a multicentric European prospective study. Eur Urol 2015;68:132–8. https://doi.org/10.1016/j.eururo.2014.07.034.Search in Google Scholar PubMed

59. Mearini, L, Ferri, C, Lazzeri, M, Bini, V, Nunzi, E, Fiorini, D, et al.. Evaluation of prostate-specific antigen isoform p2PSA and its derivates, %p2PSA, prostate health index and prostate dimension-adjusted related index in the detection of prostate cancer at first biopsy: an exploratory, prospective study. Urol Int 2014;93:135–45. https://doi.org/10.1159/000356240.Search in Google Scholar PubMed

60. Filella, X, Foj, L, Augé, JM, Molina, R, Alcover, J. Clinical utility of %p2PSA and prostate health index in the detection of prostate cancer. Clin Chem Lab Med 2014;52:1347–55. https://doi.org/10.1515/cclm-2014-0027.Search in Google Scholar PubMed

61. Porpiglia, F, Russo, F, Manfredi, M, Mele, F, Fiori, C, Bollito, E, et al.. The roles of multiparametric magnetic resonance imaging, PCA3 and prostate health index-which is the best predictor of prostate cancer after a negative biopsy? J Urol 2014;192:60–6. https://doi.org/10.1016/j.juro.2014.01.030.Search in Google Scholar PubMed

62. Ng, CF, Chiu, PK, Lam, NY, Lam, HC, Lee, KW, Hou, SS. The prostate health index in predicting initial prostate biopsy outcomes in Asian men with prostate-specific antigen levels of 4–10 ng/mL. Int Urol Nephrol 2014;46:711–7. https://doi.org/10.1007/s11255-013-0582-0.Search in Google Scholar PubMed

63. Lazzeri, M, Abrate, A, Lughezzani, G, Gadda, GM, Freschi, M, Mistretta, F, et al.. Relationship of chronic histologic prostatic inflammation in biopsy specimens with serum isoform [−2]proPSA (p2PSA), %p2PSA, and prostate health index in men with a total prostate-specific antigen of 4–10 ng/mL and normal digital rectal examination. Urol 2014;83:606–12. https://doi.org/10.1016/j.urology.2013.10.016.Search in Google Scholar PubMed

64. Scattoni, V, Lazzeri, M, Lughezzani, G, Luca, SD, Passera, R, Bollito, E, et al.. Head-to-head comparison of prostate health index and urinary PCA3 for predicting cancer at initial or repeat biopsy. J Urol 2013;190:496–501. https://doi.org/10.1016/j.juro.2013.02.3184.Search in Google Scholar PubMed

65. Ferro, M, Bruzzese, D, Perdonà, S, Marino, A, Mazzarella, C, Perruolo, G, et al.. Prostate health index (Phi) and prostate cancer antigen 3 (PCA3) significantly improve prostate cancer detection at initial biopsy in a total PSA range of 2–10 ng/mL. PLoS One 2013;8:e67687. https://doi.org/10.1371/journal.pone.0067687.Search in Google Scholar PubMed PubMed Central

66. Lazzeri, M, Haese, A, Abrate, A, de la Taille, A, Redorta, JP, McNicholas, T, et al.. Clinical performance of serum prostate-specific antigen isoform [−2]proPSA (p2PSA) 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. https://doi.org/10.1111/bju.12217.Search in Google Scholar PubMed

67. Stephan, C, Vincendeau, S, Houlgatte, A, Cammann, H, Jung, K, Semjonow, A. Multicenter evaluation of [−2]proprostate-specific antigen and the prostate health index for detecting prostate cancer. Clin Chem 2013;59:306–14. https://doi.org/10.1373/clinchem.2012.195784.Search in Google Scholar PubMed

68. Stephan, C, Jung, K, Semjonow, A, Schulze-Forster, K, Cammann, H, Hu, X, et al.. Comparative assessment of urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion with the serum [−2]proprostate-specific antigen-based prostate health index for detection of prostate cancer. Clin Chem 2013;59:280–8. https://doi.org/10.1373/clinchem.2012.195560.Search in Google Scholar PubMed

69. Perdonà, S, Bruzzese, D, Ferro, M, Autorino, R, Marino, A, Mazzarella, C, et al.. Prostate health index (phi) and prostate cancer antigen 3 (PCA3) significantly improve diagnostic accuracy in patients undergoing prostate biopsy. Prostate 2013;73:227–35. https://doi.org/10.1002/pros.22561.Search in Google Scholar PubMed

70. Ferro, M, Bruzzese, D, Perdonà, S, Mazzarella, C, Marino, A, Sorrentino, A, et al.. Predicting prostate biopsy outcome: prostate health index (phi) and prostate cancer antigen 3 (PCA3) are useful biomarkers. Clin Chim Acta 2012;413:1274–8. https://doi.org/10.1016/j.cca.2012.04.017.Search in Google Scholar PubMed

71. Guazzoni, G, Nava, L, Lazzeri, M, Scattoni, V, Lughezzani, G, Maccagnano, C, 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. https://doi.org/10.1016/j.eururo.2011.03.052.Search in Google Scholar PubMed

72. Liang, Y, Ankerst, DP, Ketchum, NS, Ercole, B, Shah, G, Shaughnessy, JDJr, et al.. Prospective evaluation of operating characteristics of prostate cancer detection biomarkers. J Urol 2011;185:104–10. https://doi.org/10.1016/j.juro.2010.08.088.Search in Google Scholar PubMed PubMed Central

73. Mottet, N, van den Bergh, RCN, Briers, E, Van den Broeck, T, Cumberbatch, MG, Santis, MD, et al.. EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 2021;79:243–62. https://doi.org/10.1016/j.eururo.2020.09.042.Search in Google Scholar PubMed

74. Grey, AD, Chana, MS, Popert, R, Wolfe, K, Liyanage, SH, Acher, PL. Diagnostic accuracy of magnetic resonance imaging (MRI) prostate imaging reporting and data system (PI-RADS) scoring in a transperineal prostate biopsy setting. BJU Int 2015;115:728–35. https://doi.org/10.1111/bju.12862.Search in Google Scholar PubMed

75. Santoro, AA, Gianfrancesco, LD, Racioppi, M, Pinto, F, Palermo, G, Sacco, E, et al.. Multiparametric magnetic resonance imaging of the prostate: lights and shadows. Urologia 2021;88:280–6. https://doi.org/10.1177/03915603211019982.Search in Google Scholar PubMed

76. Rapisarda, S, Bada, M, Crocetto, F, Barone, B, Arcaniolo, D, Polara, A, et al.. The role of multiparametric resonance and biopsy in prostate cancer detection: comparison with definitive histological report after laparoscopic/robotic radical prostatectomy. Abdom Radiol (NY) 2020;45:4178–84. https://doi.org/10.1007/s00261-020-02798-8.Search in Google Scholar PubMed PubMed Central

77. Massanova, M, Robertson, S, Barone, B, Dutto, L, Caputo, VF, Bhatt, JR, et al.. The comparison of imaging and clinical methods to estimate prostate volume: a single-centre retrospective study. Urol Int 2021;105:804–10. https://doi.org/10.1159/000516681.Search in Google Scholar PubMed

78. Guazzoni, G, Lazzeri, M, Nava, L, Lughezzani, G, Larcher, A, Scattoni, V, et al.. Preoperative prostate-specific antigen isoform p2PSA and its derivatives, %p2PSA and prostate health index, predict pathologic outcomes in patients undergoing radical prostatectomy for prostate cancer. Eur Urol 2012;61:455–66. https://doi.org/10.1016/j.eururo.2011.10.038.Search in Google Scholar PubMed

79. Tosoian, JJ, Druskin, SC, Andreas, D, Mullane, P, Chappidi, M, Joo, S, et al.. Prostate health index density improves detection of clinically significant prostate cancer. BJU Int 2017;120:793–8. https://doi.org/10.1111/bju.13762.Search in Google Scholar PubMed

80. Lughezzani, G, Lazzeri, M, Buffi, NM, Abrate, A, Mistretta, FA, Hurle, R, et al.. Preoperative prostate health index is an independent predictor of early biochemical recurrence after radical prostatectomy: results from a prospective single-center study. Urol Oncol 2015;33:337.e7–14. https://doi.org/10.1016/j.urolonc.2015.05.007.Search in Google Scholar PubMed

81. Maxeiner, A, Kilic, E, Matalon, J, Friedersdorff, F, Miller, K, Jung, K, et al.. The prostate health index PHI predicts oncological outcome and biochemical recurrence after radical prostatectomy – analysis in 437 patients. Oncotarget 2017;8:79279–88. https://doi.org/10.18632/oncotarget.17476.Search in Google Scholar PubMed PubMed Central

82. Foj, L, Filella, X. Development and internal validation of a novel PHI-nomogram to identify aggressive prostate cancer. Clin Chim Acta 2020;501:174–8. https://doi.org/10.1016/j.cca.2019.10.039.Search in Google Scholar PubMed

83. Stenner, E, Micheli, W, Bussani, A, Gotti, A, Biasioli, B. Comparison of Hybritech and WHO standardization applied to access hybritech total PSA assay on UniCel®. IJLaM 2008;4:43–6.Search in Google Scholar

84. Bouttell, J, Teoh, J, Chiu, PK, Chan, KS, Ng, CF, Heggie, R, et al.. Economic evaluation of the introduction of the prostate health index as a rule-out test to avoid unnecessary biopsies in men with prostate specific antigen levels of 4–10 in Hong Kong. PLoS One 2019;14:e0215279. https://doi.org/10.1371/journal.pone.0215279.Search in Google Scholar PubMed PubMed Central

85. Filella, X, Foj, L, Alcover, J, Augé, JM, Molina, R, Jiménez, W. The influence of prostate volume in prostate health index performance in patients with total PSA lower than 10 μg/L. Clin Chim Acta 2014;436:303–7. https://doi.org/10.1016/j.cca.2014.06.019.Search in Google Scholar PubMed

86. Mearini, L, Ferri, C, Lazzeri, M, Bini, V, Nunzi, E, Fiorini, D, et al.. Evaluation of prostate-specific antigen isoform p2PSA and its derivates, %p2PSA, prostate health index and prostate dimension-adjusted related index in the detection of prostate cancer at first biopsy: an exploratory, prospective study. Urol Int 2014;93:135–45. https://doi.org/10.1159/000356240.Search in Google Scholar PubMed

87. Friedl, A, Stangl, K, Bauer, W, Kivaranovic, D, Schneeweiss, J, Susani, M, et al.. Prostate-specific antigen parameters and prostate health index enhance prostate cancer prediction with the in-bore 3-T magnetic resonance imaging-guided transrectal targeted prostate biopsy after negative 12-core biopsy. Urol 2017;110:148–53. https://doi.org/10.1016/j.urology.2017.08.019.Search in Google Scholar PubMed

88. Stephan, C, Jung, K, Lein, M, Rochow, H, Friedersdorff, F, Maxeiner, A. PHI density prospectively improves prostate cancer detection. World J Urol 2021;39:3273–9. https://doi.org/10.1007/s00345-020-03585-2.Search in Google Scholar PubMed PubMed Central

Received: 2022-04-12
Accepted: 2022-05-03
Published Online: 2022-05-16
Published in Print: 2022-07-26

© 2022 Luisa Agnello et al., published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.