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

Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer

Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

Molecular diagnostics of prostate cancer: impact of molecular tests

ABSTRACT

Prostate cancer (PCa) is the second leading cause of cancer-related death among men. Prostate-specific antigen (PSA) testing is used in screening programs for early detection with a consequent reduction of PCa-specific mortality at the cost of overdiagnosis and overtreatment of the nonaggressive PCa. Recently, several assays have been commercially developed to implement PCa diagnosis, but they have not been included in both screening and diagnosis of PCa. This review aims to describe the actual and novel commercially available molecular biomarkers that can be used in PCa management to implement and tailor the screening and diagnosis of PCa.

Prostate Health Index (PHI) as a triage tool for reducing unnecessary magnetic resonance imaging (MRI) in patients at risk of prostate cancer

INTRODUCTION

The aim of this study is to assess the usefulness of the Prostate Health Index (PHI) as a triage tool for selecting patients at risk of prostate cancer (PCa) who should undergo multiparametric Magnetic Resonance Imaging (mpMRI).

MATERIAL AND METHODS

We enrolled 204 patients with suspected PCa. For each patient, a blood sample was collected before mpMRI to measure PHI. Findings on mpMRI were assessed according to the Prostate Imaging Reporting & Data System version 2.0 (PI-RADSv2) category scale.

RESULTS

According to PI-RADSv2, patients were classified into two groups: PI-RADS < 3 (48 %) and ≥ 3 (52 %). PHI showed the best performance for predicting PI-RADS ≥ 3 [AUC: 0,747 (0,679-0,815), 0,680(0,607-0,754), and 0,613 (0,535-0,690) for PHI, PSA ratio, and total PSA, respectively]. The best PHI cut-off was 30, with a sensitivity of 90%. At the univariate logistic regression, total PSA (p = 0.007), PSA ratio (p = 0.001), [-2]proPSA (p = 0.019) and PHI (p < 0.001) were associated with PI-RADS ≥ 3; however, at the multivariate analysis, only PHI (p < 0.001) was found to be an independent predictor of PI-RADS ≥ 3.

CONCLUSION

PHI could represent a reliable noninvasive tool for selecting patients to undergo mpMRI.

Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4-20 ng/mL

PURPOSE

The study aimed to investigate the diagnostic accuracy of prostate health index (PHI) and apparent diffusion coefficient (ADC) values in predicting prostate cancer (PCa) and construct a nomogram for the prediction of PCa and clinically significant PCa (CSPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) three lesions cohort.

METHODS

This study prospectively enrolled 301 patients who underwent multiparametric magnetic resonance (mpMRI) and were scheduled for prostate biopsy. The receiver operating characteristic curve (ROC) was performed to estimate the diagnostic accuracy of each predictor. Univariable and multivariable logistic regression analysis was conducted to ascertain hidden risk factors and constructed nomograms in PI-RADS three lesions cohort.

RESULTS

In the whole cohort, the area under the ROC curve (AUC) of PHI is relatively high, which is 0.779. As radiographic parameters, the AUC of PI-RADS and ADC values was 0.702 and 0.756, respectively. The utilization of PHI and ADC values either individually or in combination significantly improved the diagnostic accuracy of the basic model. In PI-RADS three lesions cohort, the AUC for PCa was 0.817 in the training cohort and 0.904 in the validation cohort. The AUC for CSPCa was 0.856 in the training cohort and 0.871 in the validation cohort. When applying the nomogram for predicting PCa, 50.0% of biopsies could be saved, supplemented by 6.9% of CSPCa being missed.

CONCLUSION

PHI and ADC values can be used as predictors of CSPCa. The nomogram included PHI, ADC values and other clinical predictors demonstrated an enhanced capability in detecting PCa and CSPCa within PI-RADS three lesions cohort.

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

Clinical Biofluid Assays for Prostate Cancer

This mini review summarizes the currently available clinical biofluid assays for PCa. The second most prevalent cancer worldwide is PCa. PCa is a heterogeneous disease, with a large percentage of prostate tumors being indolent, and with a relatively slow metastatic potential. However, due to the high case numbers, the absolute number of PCa-related deaths is still high. In fact, it causes the second highest number of cancer deaths in American men. As a first step for the diagnosis of PCa, the PSA test has been widely used. However, it has low specificity, which results in a high number of false positives leading to overdiagnosis and overtreatment. Newer derivatives of the original PSA test, including the Food and Drug Administration (FDA)-approved 4K (four kallikreins) and the PHI (Prostate Health Index) blood tests, have higher specificities. Tissue-based PCa tests are problematic as biopsies are invasive and have limited accuracy due to prostate tumor heterogeneity. Liquid biopsies offer a minimally or non-invasive choice for the patients, while providing a more representative reflection of the spatial heterogeneity in the prostate. In addition to the abovementioned blood-based tests, urine is a promising source of PCa biomarkers, offering a supplementary avenue for early detection and improved tumor classification. Four urine-based PCa tests are either FDA- or CLIA-approved: PCA3 (PROGENSA), ExoDX Prostate Intelliscore, MiPS, and SelectMDx. We will discuss these urine-based, as well as the blood-based, clinical PCa tests in more detail. We also briefly discuss a few promising biofluid marker candidates (DNA methylation, micro-RNAs) which are not in clinical application. As no single assay is perfect, we envision that a combination of biomarkers, together with imaging, will become the preferred practice.

Evaluating the Role of Morphological Parameters in the Prostate Transition Zone in PHI-Based Predictive Models for Detecting Gray Zone Prostate Cancer

Background

The early detection of clinically significant prostate cancer (csPCa) through the integration of multidimensional parameters presents a promising avenue for improving survival outcomes for this fatal disease. This study aimed to assess the contribution of prostate transition zone (TZ) to predictive models based on the prostate health index (PHI), with the goal of enhancing early detection of csPCa in the prostate-specific antigen (PSA) gray zone.

Methods

In this observational cross-sectional study, a total of 177 PSA gray zone patients (total prostate-specific antigen [tPSA] level ranging from 4.0 to 10.0 ng/mL) were recruited and received PHI detections from August 2020 to March 2022. Prostatic morphologies especially the TZ morphological parameters were measured by transrectal ultrasound (TRUS).

Results

Univariable logistic regression indicated prostatic morphological parameters including total prostate volume (PV) indexes and transitional zone volume indexes were all associated with csPCa (P < .05), while the multivariable analysis demonstrated that C-reactive protein (CRP), PHI, PHI density (PHID), and PHI transition zone density (PHI-TZD) were the 4 independent risk factors. The receiver-operating characteristic (ROC) curve analysis suggested that integrated predictive models (PHID, PHI-TZD) yield area under the curves (AUCs) of 0.9135 and 0.9105 in csPCa prediction, which shows a relatively satisfactory predictive capability compared with other predictors. Moreover, the PHI-TZD outperformed PHID by avoiding 30 patients' unnecessary biopsies while maintaining 74.36% specificity at a sensitivity of 90%. Decision-curve analysis (DCA) confirmed the comparable performance of the multivariable full-risk prediction models, without the inclusion of the net benefit, thereby highlighting the superior diagnostic efficacy of PHID and PHI-TZD in comparison with other diagnostic models, in both univariable and multivariable models.

Conclusion

Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.

Comparison of Prostate-Specific Antigen and Its Density and Prostate Health Index and Its Density for Detection of Prostate Cancer

As the incidence of prostate cancer (PCa) has increased, screening based on prostate-specific antigen (PSA) has become controversial due to the low specificity of PSA. Therefore, we investigated the diagnostic performance of prostate health index (PHI) density (PHID) for the detection of PCa and clinically significant PCa (csPCa) compared to PSA, PSA density (PSAD), and PHI as a triaging test. We retrospectively reviewed 306 men who underwent prostate biopsy for PSA levels of 2.5 to 10 ng/mL between January 2020 and April 2023. Of all cohorts, 86 (28.1%) and 48 (15.7%) men were diagnosed with PCa and csPCa, respectively. In ROC analysis, the highest AUC was identified for PHID (0.812), followed by PHI (0.791), PSAD (0.650), and PSA (0.571) for PCa. A similar trend was observed for csPCa: PHID (AUC 0.826), PHI (AUC 0.796), PSAD (AUC 0.671), and PSA (0.552). When the biopsy was restricted to men with a PHID ≥ 0.56, 26.5% of unnecessary biopsies could be avoided; however, 9.3% of PCa cases and one csPCa case (2.1%) remained undiagnosed. At approximately 90% sensitivity for csPCa, at the given cut-off values of PHI ≥ 36.4, and PHID ≥ 0.91, 48.7% and 49.3% of unnecessary biopsies could be avoided. In conclusion, PHID had a small advantage over PHI, about 3.6%, for the reduction in unnecessary biopsies for PCa. The PHID and PHI showed almost the same diagnostic performance for csPCa detection. PHID can be used as a triaging test in a clinical setting to pre-select the risk of PCa and csPCa.

Do PHI and PHI density improve detection of clinically significant prostate cancer only in the PSA gray zone?

OBJECTIVES

Prostate health index (PHI) is a predictive biomarker of positive prostate biopsy. The majority of evidence refers to its use in the PSA gray zone (4-10 ng/mL) and negative digital rectal exam (DRE). We aim to evaluate and compare the predictive accuracy of PHI and PHI density (PHId) with PSA, percentage of free PSA and PSA density, in a wider range of patients for the detection of clinically significant prostate cancer (csPCa).

METHODS

Multicenter prospective study that included patients suspicious of harboring prostate cancer. Non-probabilistic convenience sampling, where men who attended the urology consultation were tested for PHI before prostate biopsy. To evaluate and compare diagnostic accuracy AUC and decision curve analysis (DCA) were calculated. All these procedures were performed for the overall sample and the following subsamples: PSA < 4 ng/ml; PSA 4-10 ng/ml; PSA 4-10 ng/ml plus negative DRE and PSA > 10 ng/ml.

RESULTS

Among the 559 men included, 194 (34.7%) were diagnosed of csPCa. PHI and PHId outperfomed PSA in all subgroups. PHI best diagnostic performance was found in PSA 4-10 ng/ml with negative DRE (sensitivity 93.33, NPV 96.04). Regarding AUC, significant differences were found between PHId and PSA in the subgroup of PSA 4-10 ng/ml, whatever DRE status. In DCA, PHI density shows the highest net benefit.

CONCLUSIONS

PHI and PHId outperfom PSA in csPCa detection, not only in the PSA grey zone with negative DRE, but also in a wider range of PSA values. There is an urgent need of prospective studies to established a validated threshold and its incorporation in risk calculators.

Tutorial: statistical methods for the meta-analysis of diagnostic test accuracy studies

This tutorial shows how to perform a meta-analysis of diagnostic test accuracy studies (DTA) based on a 2 × 2 table available for each included primary study. First, univariate methods for meta-analysis of sensitivity and specificity are presented. Then the use of univariate logistic regression models with and without random effects for e.g. sensitivity is described. Diagnostic odds ratios (DOR) are then introduced to combine sensitivity and specificity into one single measure and to assess publication bias. Finally, bivariate random effects models using the exact binomial likelihood to describe within-study variability and a normal distribution to describe between-study variability are presented as the method of choice. Based on this model summary receiver operating characteristic (sROC) curves are constructed using a regression model logit-true positive rate (TPR) over logit-false positive rate (FPR). Also it is demonstrated how to perform the necessary calculations with the freely available software R. As an example a meta-analysis of DTA studies using Procalcitonin as a diagnostic marker for sepsis is presented.

Utility of Prostate-Specific Antigen Isoforms and Prostate Health Index in the Diagnosis of Metastatic Prostate Cancer

Objective The current study was undertaken to investigate the utility of total prostate-specific antigen (tPSA), its isoform [-2] proPSA (p2PSA), and prostate health index (PHI) in the diagnosis of metastatic prostate cancer (PCa).

Materials and Methods This study was conducted from March 2016 to May 2019. Eighty-five subjects who were diagnosed with PCa for the first time, following transrectal ultrasound-guided prostate biopsy, were included in the study. The prebiopsy blood samples were analyzed in Beckman Coulter Access-2 Immunoanalyzer for tPSA, p2PSA, and free PSA (fPSA), and the calculated parameters included %p2PSA, %fPSA, and PHI. Mann–Whitney's U test was used as test of significance, and p-value less than 0.05 was considered statistically significant.

Results Of the 85 participants, 81.2% (n = 69) had evidence of metastasis, both clinically and pathologically. The median tPSA (ng/mL), p2PSA (pg/mL), %p2PSA, and PHI were significantly higher in the group with evidence of metastasis (46.5 vs. 13.76; 198.0 vs. 35.72; 3.25 vs. 1.51; 237.58 vs. 59.74, respectively). The sensitivity (%), specificity (%), negative predictive value (%), and positive predictive value (%) to diagnose metastatic PCa of tPSA at a cutoff of 20 ng/mL, PHI at a cutoff of 55, and %p2PSA at a cutoff of 1.66 were 92.7, 98.5, and 94.2; 37.5, 43.7, and 62.5; 54.5, 87.5, and 71.4; and 86.4, 88.3, and 91.5, respectively.

Conclusion Using tests such as %p2PSA and PHI in the standard armamentarium for the diagnosis of metastatic PCa in addition to PSA will help in selecting the appropriate treatment strategy, including active surveillance.