Combining prostate health index and multiparametric magnetic resonance imaging in the diagnosis of clinically significant prostate cancer in an Asian population

Determinants of decision-making in biopsy of PI-RADS 3 transition zone lesions

INTRODUCTION

Cancer rates for Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions are low. We aimed to determine the clinical and magnetic resonance imaging (MRI) parameters that can provide risk stratification for PI-RADS 3 transition zone (TZ) lesions to guide decision for biopsy, which can improve the cost-effectiveness of resource utilisation.

METHODS

The MRI scans of all patients who underwent MRI-ultrasound fusion targeted biopsy from 1 May 2016 to 31 December 2022 were retrospectively assessed by two board-certified abdominal radiologists. The following data were collected and analysed serum prostate-specific antigen, Prostatic Health Index (PHI), prostate volume, histological results, lesion size, location, diffusion-weighted imaging (DWI) parameter scores and overall PI-RADS score.

RESULTS

Two hundred and fourteen TZ lesions were included. Among 131 PI-RADS 3 lesions, those with marked restricted diffusion (DWI score ≥4), diameter ≥1 cm, prostrate-specific antigen density (PSAD) ≥0.11 and PHI ≥34 were more likely to contain clinically significant prostate cancer (csPCa; P = 0.04, 0.02, 0.049 and 0.05, respectively), with areas under the receiver operating characteristics curve of 0.9, 0.76, 0.84 and 0.80, respectively. Apical lesions were more likely to contain csPCa compared to midgland or basal lesions (P = 0.01).

CONCLUSION

Clinical parameters (PSAD and PHI) and MRI features (lesion size, DWI score, lesion location) can be used to risk stratify PI-RADS 3 TZ lesions and guide decision for targeted biopsy.

Diagnostic Accuracy of Liquid Biomarkers for Clinically Significant Prostate Cancer Detection: A Systematic Review and Diagnostic Meta-analysis of Multiple Thresholds

CONTEXT

Many liquid biomarkers have entered clinical practice with the praise to improve the detection of clinically significant prostate cancer (csPCa), helping avoid unnecessary prostate biopsies.

OBJECTIVE

We aimed to assess the diagnostic accuracy of multianalyte biomarkers for csPCa detection using multiple thresholds.

EVIDENCE ACQUISITION

A comprehensive literature search was done through PubMed, Web of Science, and Scopus in March 2023 for prospective and retrospective studies reporting the diagnostic performance of liquid biomarkers for detecting csPCa. The outcomes of interest were the diagnostic performance of liquid biomarkers for csPCa detection and identification of optimal thresholds for each biomarker.

EVIDENCE SYNTHESIS

Overall, 49 studies were eligible for this meta-analysis. Using each representative threshold based on the Youden Index, the pooled sensitivity and specificity for detecting csPCa were 0.85 and 0.37 for prostate cancer gene 3 (PCA3), 0.85 and 0.52 for prostate health index (PHI), 0.87 and 0.58 for four kallikrein (4K), 0.82 and 0.56 for SelectMDx, 0.85 and 0.54 for ExoDx, and 0.82 and 0.59 for mi prostate score (MPS), respectively. The diagnostic odds ratio was highest for 4K (8.84), followed by MPS (7.0) and PHI (6.28). According to the meta-analysis incorporating multiple thresholds, the corresponding sensitivity was 0.77 for 4K, 0.69 for PHI, and 0.63 for PCA3; specificity was 0.72 for PHI, 0.70 for 4K, and 0.69 for PCA3.

CONCLUSIONS

Regarding the detection of csPCa, 4K had the highest diagnostic performance among the commercial liquid biomarkers. Based on the optimal thresholds calculated by the present meta-analysis, 4K had the highest sensitivity and PHI had the highest specificity for detecting csPCa. Nevertheless, clinical decision-making requires combination strategies between liquid and imaging biomarkers.

PATIENT SUMMARY

Novel biomarkers for prostate cancer detection were useful for more accurate diagnosis of clinically significant prostate cancer to avoid unnecessary biopsies.

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.

Prostate health index density aids the diagnosis of prostate cancer detected using magnetic resonance imaging targeted prostate biopsy in Taiwanese multicenter study

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) followed by MRI-targeted prostate biopsy is the current standard for diagnosing prostate cancer (PCa). However, studies evaluating the value of biomarkers, including prostate health index (PHI) and its derivatives using this method are limited. We aimed to investigate the efficacy of PHI density (PHID) in guiding MRI-targeted prostate biopsies to identify clinically significant PCas (csPCa).

METHODS

The multicenter prospectively registered prostate biopsy database from three medical centers in Taiwan included patients with PHI and MRI-targeted and/or systematic prostate biopsies. We assessed the required values of prostate-specific antigen (PSA), prostate volume, PHI, PHID, and Prostate Imaging Reporting & Data System (PI-RADS) score using multivariable analyses, receiver operating characteristic curve analysis, and decision curve analyses (DCA). csPCa was defined as the International Society of Urological Pathology Gleason group ≥2 PCa, with an emphasis on reducing unwarranted biopsies.

RESULTS

The study cohort comprised 420 individuals. Diagnoses of PCa and csPCa were confirmed in 62.4% and 47.9% of the participants, respectively. The csPCa diagnosis rates were increased with increasing PI-RADS scores (20.5%, 44.2%, and 73.1% for scores 3, 4, and 5, respectively). Independent predictors for csPCa detection included PHI, prostate volume, and PI-RADS scores of 4 and 5 in multivariable analyses. The area under the curve (AUC) for csPCa of PHID (0.815) or PHI (0.788) was superior to that of PSA density (0.746) and PSA (0.635) in the entire cohort, and the superiority of PHID (0.758) was observed in PI-RADS 3 lesions. DCA revealed that PHID achieved the best net clinical benefit in PI-RADS 3-5 and 4/5 cases. Among PI-RADS 3 lesions, cutoff values of PHID 0.70 and 0.43 could eliminate 51.8% and 30.4% of omitted biopsies, respectively.

CONCLUSION

PHI-derived biomarkers, including PHID, performed better than other PSA-derived biomarkers in diagnosing PCa in MRI-detected lesions.

Different diagnostic strategies combining prostate health index and magnetic resonance imaging for predicting prostate cancer: A multicentre study

OBJECTIVE

To explore how prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) should be used in concert to improve diagnostic capacity for clinically significant prostate cancers (CsCaP) in patients with prostate-specific antigen (PSA) between 4 and 20 ng/ml.

METHODS

About 426 patients fulfilling the inclusion criteria were included in this study. Univariable and multivariable logistic analyses were performed to analyze the association between the clinical indicators and CaP/CsCaP. We used the Delong test to compare the differences in the area under the curve (AUC) values of four models for CaP and CsCaP. Decision curve analysis (DCA) and calibration plots were used to assess predictive performance. We compared clinical outcomes of different diagnostic strategies constructed using different combinations of the models by the chi-square test and the McNemar test.

RESULTS

The AUC of PHI-MRI (a risk prediction model based on PHI and mpMRI) was 0.859, which was significantly higher than those of PHI (AUC = 0.792, P < 0.001) and mpMRI (AUC = 0.797, P < 0.001). PHI-MRI had a higher net benefit on DCA for predicting CaP and CsCaP in comparison to PHI and mpMRI. Adding the PHI-MRI in diagnostic strategies for CsCaP, such as use PHI-MRI alone or sequential use of PHI followed by PHI-MRI, could reduce the number of biopsies by approximately 20% compared to use PHI followed by mpMRI (256 vs 316, 257 vs 316, respectively).

CONCLUSIONS

The PHI-MRI model was superior to PHI and MRI alone. It may reduce the number of biopsies and ensure the detection rate of CsCaP under an appropriate sensitivity at the cost of an increased number of MRI scans.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis

Importance

Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion.

Objective

To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data.

Data Sources

PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022.

Study Selection

English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data.

Data Extraction and Synthesis

Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

Main Outcomes and Measures

Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis.

Results

The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15).

Conclusions and Relevance

These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.

Clinical value of prostate health index as an indicator for recommending magnetic resonance imaging in patients with gray-zone prostate-specific antigen level

PURPOSE

To evaluate the usefulness of prostate health index (PHI) as an indicator for recommending magnetic resonance imaging (MRI) in patients with prostate-specific antigen (PSA) gray zone level < 10 ng/mL.

METHODS

443 patients who underwent prostate biopsy (PB) after serum PHI test and MRI between April 2019 and December 2022 were enrolled. For patients with visible lesion on MRI with Prostate Imaging Reporting and Data System Score (PI-RADS) ≥ 3, MRI-targeted PB was performed in addition to systematic 12-core PB.

RESULTS

The optimal cutoff value of PHI for predicting PI-RADS ≥ 3 lesions was 39.6, which was significantly associated with overall prostate cancer (OR 3.07, p = 0.018) and clinically significant prostate cancer (csPCa) (OR 4.15, p = 0.006) at MRI-targeted PB cores. When MRI was restricted to patients with PHI ≥ 39.6 alone, 28.7% of unnecessary MRI could be saved at the cost of missing 13.6% of csPCa. When omitting MRI for patients with PHI < 39.6 and PSAD < 0.12 ng/mL2, unnecessary MRI could be reduced by 20.1% with the risk of missing 6.2% of csPCa. With addition of systematic PB, 21.0% of patients with negative MRI-targeted PB were diagnosed as csPCa.

CONCLUSIONS

For patients in PSA gray zone, PHI of 39.6 might be an indicator for MRI and further MRI-targeted PB in additional to PSAD of 0.12 ng/mL2, reducing 20.1% of unnecessary MRI with the minimal risk of missing 6.2% of csPCa. To maximize csPCa detection, combining both MRI-targeted and systematic PB should be also considered.

Biomarkers for Prostate Cancer: From Diagnosis to Treatment

Prostate cancer (PCa) is a widespread malignancy with global significance, which substantially affects cancer-related mortality. Its spectrum varies widely, from slow-progressing cases to aggressive or even lethal forms. Effective patient stratification into risk groups is crucial to therapeutic decisions and clinical trials. This review examines a wide range of diagnostic and prognostic biomarkers, several of which are integrated into clinical guidelines, such as the PHI, the 4K score, PCA3, Decipher, and Prolaris. It also explores the emergence of novel biomarkers supported by robust preclinical evidence, including urinary miRNAs and isoprostanes. Genetic alterations frequently identified in PCa, including BRCA1/BRCA2, ETS gene fusions, and AR changes, are also discussed, offering insights into risk assessment and precision treatment strategies. By evaluating the latest developments and applications of PCa biomarkers, this review contributes to an enhanced understanding of their role in disease management.

A prospective study of the prostate health index density and multiparametric magnetic resonance imaging in diagnosing clinically significant prostate cancer

PURPOSE

To evaluate the predictive performance of the prostate health index (PHI) and PHI density (PHID), for clinically significant prostate cancer (csPCa) in patients with a PI-RADS score ≤3.

MATERIALS AND METHODS

Patients tested for total prostate-specific antigen (tPSA, ≤100 ng/mL), free PSA (fPSA), and p2PSA at Peking University First Hospital were prospectively enrolled. Possible predictive factors of csPCa were analyzed using the receiver operating characteristic (ROC) curve. Results were expressed as area under the curve (AUC) with 95% confidence intervals (CI). The cutoff values of PHI and PHID were determined.

RESULTS

We enrolled 222 patients in this study. The prevalence of csPCa in the PI-RADS ≤3 subgroup (n=89) was 22.47% (20/89). Age, tPSA, F/T, prostate volume, PSA density, PHI, PHID, and PI-RADS score were significantly associated with csPCa. PHID (AUC: 0.829 [95% CI: 0.717-0.941]) was the best predictor of csPCa. PHID >0.956 was set as the threshold of suspicious csPCa with a sensitivity of 85.00% and a specificity of 73.91%, avoiding 94.44% of unnecessary biopsies but missing 15.00% csPCa. A threshold of PHI ≥52.83 showed the same sensitivity but a rather lower specificity of 65.22% that avoided 93.75% of unnecessary biopsies.

CONCLUSIONS

PHI and PHID have the best predictive performance of csPCa in patients with PI-RADS score ≤3. A threshold value of PHID ≥0.956 may be used as the criterion for biopsy in these patients.

Learning Curve of Transperineal MRI/US Fusion Prostate Biopsy: 4-Year Experience

This study aimed to evaluate the learning curve of transperineal magnetic resonance imaging (MRI)/ultrasound (US) fusion biopsy in a team composed of a single surgeon, a single radiologist, and a single pathologist. We prospectively enrolled 206 patients undergoing MRI/US fusion prostate biopsy and divided them into four cohorts by the year of biopsy. We analyzed temporal changes in clinically significant prostate cancer (csPC) detection rate, percentage of positive cores on biopsy, and Gleason upgrading rate after radical prostatectomy. The csPC detection rate by MRI/US fusion targeted biopsy (TB) increased significantly (from 35.3% to 60.0%, p = 0.01). With increased experience, the csPC detection rates for small (≤1 cm) and anterior target lesions gradually increased (from 41.2% to 51.6%, p = 0.5; from 54.5% to 88.2%, p = 0.8, respectively). The percentage of positive cores on TB increased significantly (from 18.4% to 44.2%, p = 0.001). The Gleason upgrading rate gradually decreased (from 22.2% to 11.1%, p = 0.4). In conclusion, with accumulated experience and teamwork, the csPC detection rate by TB significantly increased. Multidisciplinary team meetings and a free-hand biopsy technique were the key factors for overcoming the learning curve.

The function of Prostate Health Index in detecting clinically significant prostate cancer in the PI-RADS 3 population: a multicenter prospective study

PURPOSE

The purpose of this study is to identify patients in the prostate imaging reporting and data system (PI-RADS) 3 population who need biopsy by using prostate health index (PHI) and other clinical parameters in order to avoid unnecessary biopsies.

METHODS

A total of 302 patients from four hospital were enrolled, and 92 patients with PI-RADS 3 were included finally. All patients were biopsy-naïve and had suspicion of prostate cancer (PCa) with PSA level in 4-20 ng/ml and a normal digital rectal exam. Univariable and stepwise forward multivariable logistic regression analyses were used to evaluated the risk factors. The sensitivity, specificity, and positive and negative predictive values of different cut-off value of PHI were calculated for the diagnosis of clinically significant prostate cancer (CSPCa).

RESULTS

The overall patient's mean age was 65.65 ± 9.55 years, median PSA was 7.68 (5.28-12.07) ng/ml and median PHI was 43.80 (33.09-64.69). PCa was identified in 32.61% (30/92) of PI-RADS 3 and CSPCa was identified in 28.26% (26/92) of PI-RADS 3. The risk factors for detecting PCa and CSPCa in multivariable regression analysis were age and PHI. When the biopsy was restricted to those PHI ≥ 43.5, 42.39% unnecessary biopsied could avoid. The sensitivity, specificity, positive predictive value and negative predictive value for the detection of CSPCa in the PHI ≥ 43.5 were 92.31%, 63.64%, 50% and 95.45% respectively.

CONCLUSION

The inclusion of PHI in the diagnosis of the PI-RADS 3 population may avoid many unnecessary biopsies. The multivariable models could increase the detection of cancer.

Combining prostate health index and multiparametric magnetic resonance imaging may better predict extraprostatic extension after radical prostatectomy

BACKGROUND

In patients undergoing radical prostatectomy (RP) for prostate cancer (PCa), preoperative prediction of extraprostatic extension (EPE) can facilitate patient selection for nerve-sparing procedures. Since both multiparametric magnetic resonance imaging (mpMRI) and prostate health index (PHI) have shown promise for the diagnosis and prognostication of PCa, we investigated whether a combination of mpMRI and PHI evaluations can improve the prediction of EPE after RP.

METHODS

Patients diagnosed with PCa and treated with RP were prospectively enrolled between February 2017 and July 2019. Preoperative blood samples were analyzed for PHI (defined as [p2PSA/fPSA] × √tPSA), and mpMRI examinations were performed and interpreted by a single experienced uroradiologist retrospectively. The area under the receiver operating characteristic curve (ROC) was used to determine the performance of mpMRI, PHI, and their combination in predicting EPE after RP.

RESULTS

A total of 163 patients were included for analysis. The pathological T stage was T3a or more in 59.5%. Overall staging accuracy of mpMRI for EPE was 72.4% (sensitivity and specificity: 73.2% and 71.2%, respectively). The area under the ROC of the combination of mpMRI and PHI in predicting EPE (0.785) was higher than those of mpMRI alone (0.717; p = 0.0007) and PHI alone (0.722; p = 0.0236). mpMRI showed false-negative non-EPE results in 26 patients (16%), and a PHI threshold of >40 could avoid undiagnosed EPE before RP in 21 of these 26 patients.

CONCLUSION

The combination of PHI and mpMRI may better predict the EPE preoperatively, facilitating preoperative counseling and tailoring the need for nerve-sparing RP.

Nomograms Combining PHI and PI-RADS in Detecting Prostate Cancer: A Multicenter Prospective Study

(1) Background: The study aimed to construct nomograms to improve the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in the Asian population. (2) Methods: This multicenter prospective study included a group of 293 patients from three hospitals. Univariable and multivariable logistic regression analysis was performed to identify potential risk factors and construct nomograms. Discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. The web-based dynamic nomograms were subsequently built based on multivariable logistic analysis. (3) Results: A total of 293 patients were included in our study with 201 negative and 92 positive results in PCa. Four independent predictive factors (age, prostate health index (PHI), prostate volume, and prostate imaging reporting and data system score (PI-RADS)) for PCa were included, and four factors (age, PHI, PI-RADS, and Log PSA Density) for CSPCa were included. The area under the ROC curve (AUC) for PCa was 0.902 in the training cohort and 0.869 in the validation cohort. The AUC for CSPCa was 0.896 in the training cohort and 0.890 in the validation cohort. (4) Conclusions: The combined diagnosis of PHI and PI-RADS can avoid more unnecessary biopsies and improve the detection rate of PCa and CSPCa. The nomogram with the combination of age, PHI, PV, and PI-RADS could improve the detection of PCa, and the nomogram with the combination of age, PHI, PI-RADS, and Log PSAD could improve the detection of CSPCa.

Molecular Biomarkers for the Detection of Clinically Significant Prostate Cancer: A Systematic Review and Meta-analysis

Context

Prostate cancer (PCa) is the second most common type of cancer in men. Individualized risk stratification is crucial to adjust decision-making. A variety of molecular biomarkers have been developed in order to identify patients at risk of clinically significant PCa (csPCa) defined by the most common PCa risk stratification systems.

Objective

The present study aims to examine the effectiveness (diagnostic accuracy) of blood or urine-based PCa biomarkers to identify patients at high risk of csPCa.

Evidence acquisition

A systematic review of the literature was conducted. Medline and EMBASE were searched from inception to March 2021. Randomized or nonrandomized clinical trials, and cohort and case-control studies were eligible for inclusion. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Pooled estimates of sensitivity, specificity, and area under the curve were obtained.

Evidence synthesis

Sixty-five studies (N = 34 287) were included. Not all studies included prostate-specific antigen-selected patients. The pooled data showed that the Prostate Health Index (PHI), with any cutoff point between 15 and 30, had sensitivity of 0.95-1.00 and specificity of 0.14-0.33 for csPCa detection. The pooled estimates for SelectMDx test sensitivity and specificity were 0.84 and 0.49, respectively.

Conclusions

The PHI test has a high diagnostic accuracy rate for csPCa detection, and its incorporation in the diagnostic process could reduce unnecessary biopsies. However, there is a lack of evidence on patient-important outcomes and thus more research is needed.

Patient summary

It has been possible to verify that the application of biomarkers could help detect prostate cancer (PCa) patients with a higher risk of poorer evolution. The Prostate Health Index shows an ability to identify 95-100 for every 100 patients suffering from clinically significant PCa who take the test, preventing unnecessary biopsies in 14-33% of men without PCa or insignificant PCa.

Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI

INTRODUCTION

On prostate biopsy, multiparametric magnetic resonance imaging (mpMRI) and the Prostate Health Index (PHI) have allowed prediction of clinically significant prostate cancer (csPCa).

METHODS

To predict the likelihood of csPCa, we created a nomogram based on a multivariate model that included PHI and mpMRI. We assessed 315 males who were scheduled for prostate biopsies.

RESULTS

We used the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2) to assess mpMRI and optimize PHI testing prior to biopsy. Univariate analysis showed that csPCa may be identified by PHI with a cut-off value of 77.77, PHID with 2.36, and PI-RADS with 3 as the best threshold. Multivariable logistic models for predicting csPCa were developed using PI-RADS, free PSA (fPSA), PHI, and prostate volume. A multivariate model that included PI-RADS, fPSA, PHI, and prostate volume had the best accuracy (AUC: 0.882). Decision curve analysis (DCA), which was carried out to verify the nomogram's clinical applicability, showed an ideal advantage (13.35% higher than the model that include PI-RADS only).

DISCUSSION

In conclusion, the nomogram based on PHI and mpMRI is a valuable tool for predicting csPCa while avoiding unnecessary biopsy as much as possible.

The Detection Performance of 18 F-Prostate-Specific Membrane Antigen-1007 PET/CT in Primary Prostate Cancer : A Systemic Review and Meta-analysis

BACKGROUND

Multiple tools are now available to determine the requirement for a biopsy to diagnose prostate cancer, and PET/CT with radiolabeled prostate-specific membrane antigen (PSMA)-targeting radiotracers has been recommended for detecting primary prostate cancer. Particularly, the radiotracer 18 F-PSMA-1007 was found to be more favorable for primary tumors compared with other PSMA-targeting radiotracers because of its low clearance via the urinary tract and better image resolution. Thus, we performed a systematic review and meta-analysis to more accurately evaluate the detection performance of 18 F-PSMA-1007 PET/CT in primary prostate cancer patients.

METHODS

An update on the databases of PubMed/MEDLINE, EMBASE, and Cochrane Library for comprehensive literature search was performed on September 30, 2021. The pooling detection rate was calculated on a per-patient basis. The pooling median of the SUV max was analyzed from the included studies. Furthermore, the positive predictive value of 18 F-PSMA-1007 PET/CT with pathologic lesions was analyzed using the criterion standard.

RESULTS

Twelve studies (540 patients total) were included in the meta-analysis. The overall pooling detection rate of 18 F-PSMA-1007 per patient was 94%, and the pooling median of SUV max located at the intraprostate tumor was 16 (range, 3.7-77.7). The positive predictive value of 18 F-PSMA-1007 per lesion with histopathological validation was 0.90, detecting regional lymph node metastasis was 0.94, and detecting localized prostatic tumors was 0.84.

CONCLUSIONS

In the current meta-analysis, we revealed the excellent performance of 18 F-PSMA-1007 to detect localized prostatic tumor lesions and regional lymph node metastasis. Moreover, the uptake of localized tumors in primary prostate cancer was nearly liver uptake and may be considered a suspicious malignancy if it was equal to or greater than the liver uptake.

Clinical Utility of Prostate Health Index for Diagnosis of Prostate Cancer in Patients with PI-RADS 3 Lesions

The risk of prostate cancer (PCa) in prostate imaging reporting and data system version 2 (PI-RADSv2) score-3 lesions is equivocal; it is regarded as an intermediate status of presented PCa. In this study, we evaluated the clinical utility of the prostate health index (PHI) for the diagnosis of PCa and clinically significant PCa (csPCa) in patients with PI-RADSv2 score-3 lesions. The study cohort included patients who underwent a transrectal ultrasound (TRUS)-guided, cognitive-targeted biopsy for PI-RADSv2 score-3 lesions between November 2018 and April 2021. Before prostate biopsy, the prostate-specific antigen (PSA) derivatives, such as total PSA (tPSA), [-2] proPSA (p2PSA) and free PSA (fPSA) were determined. The calculation equation of PHI is as follows: [(p2PSA/fPSA) × tPSA ½]. Using a receiver operating characteristic (ROC) curve analysis, the values of PSA derivatives measured by the area under the ROC curve (AUC) were compared. For this study, csPCa was defined as Gleason grade 2 or higher. Of the 392 patients with PI-RADSv2 score-3 lesions, PCa was confirmed in 121 (30.9%) patients, including 59 (15.1%) confirmed to have csPCa. Of all the PSA derivatives, PHI and PSA density (PSAD) showed better performance in predicting overall PCa and csPCa, compared with PSA (all p < 0.05). The AUC of the PHI for predicting overall PCa and csPCa were 0.807 (95% confidence interval (CI): 0.710−0.906, p = 0.001) and 0.819 (95% CI: 0.723−0.922, p < 0.001), respectively. By the threshold of 30, PHI was 91.7% sensitive and 46.1% specific for overall PCa, and was 100% sensitive for csPCa. Using 30 as a threshold for PHI, 34.4% of unnecessary biopsies could have been avoided, at the cost of 8.3% of overall PCa, but would include all csPCa.

Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer

Background

With the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.

Methods

We retrospectively collected the clinical indicators of patients with TPSA at 4–20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.

Results

Among the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4–20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4–20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4–20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.

Conclusions

The models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.

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

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.

The modified prostate health index (PHI) outperforms PHI density in the detection of clinical prostate cancer within the PSA grey zone

OBJECTIVES

To compare the accuracy of several volume and diameters modified prostate health index (mPHI) models with PHI density (PHID), PHI, and other prostate-specific antigen (PSA) derivatives in detecting PSA grey zone prostate cancer (PCa).

MATERIALS AND METHODS

Between August 2020 and September 2021, a consecutive cohort of 214 suspected PCa patients with elevated total PSA values ranged from 4.0 to 10.0 ng/mL were prospectively recruited and received PHI detections and transrectal ultrasonography (TRUS) measurements, followed by systematic prostate biopsies confirmation.

RESULTS

Among the 214 patients enrolled in the project, a total of 80 were diagnosed with PCa. In univariate analysis for the training cohort, the area under curve (AUC) of mPHI-2 [Formula: see text] was 0.8310, which outperformed PHID in identifying PSA grey zone PCa (P ≤ 0.0001) and showed the best net benefit in decision curve analysis (DCA). By a threshold of 0.2835, the sensitivity and specificity in the prediction of PCa were 78.9% and 90.3%, while the positive predictive value (PPV) and negative predictive value (NPV) were 78.3% and 78.6%, respectively.

CONCLUSIONS

According to our present single-center experience, the mPHI-2 risk predictor outperformed PHID or other classical parameters alone in the PCa detection with a grey zone PSA level in Asian males.

[Clinical use and evolution of circulating biomarkers in the era of personalized oncology: From protein markers to bioclinical scores]

In oncology, the identification of targets that correlate with a type of cancer has led to a profound change in the notion of "tumor markers". Technological advances, in particular the development of high-throughput sequencing, have led to the emergence of a new generation of molecular biomarkers for tumors. Despite their limited utility for screening and diagnosis, conventional tumor markers remain interesting for evaluation of prognoses, the choice and optimization of treatments, as well as for monitoring the effectiveness of those treatments. In this article, we revisit the conventional serum markers that are enjoying a 'come back' thanks to the development of high-performance scores based on biological, cytological, clinical, or radiological criteria.

Biomarkers for prostate cancer detection and risk stratification

Although prostate cancer (PCa) is the most commonly diagnosed cancer in men, most patients do not die from the disease. Prostate specific antigen (PSA), the most widely used oncologic biomarker, has revolutionized screening and early detection, resulting in reduced proportion of patients presenting with advanced disease. However, given the inherent limitations of PSA, additional diagnostic and prognostic tools are needed to facilitate early detection and accurate risk stratification of disease. Serum, urine, and tissue-based biomarkers are increasingly being incorporated into the clinical care paradigm, but there is still a limited understanding of how to use them most effectively. In the current article, we review test characteristics and clinical performance data for both serum [4 K score, prostate health index (phi)] and urine [SelectMDx, ExoDx Prostate Intelliscore, MyProstateScore (MPS), and PCa antigen 3 (PCA3)] biomarkers to aid decisions regarding initial or repeat biopsies as well as tissue-based biomarkers (Confirm MDx, Decipher, Oncotype Dx, and Polaris) aimed at risk stratifying patients and identifying those patients most likely to benefit from treatment versus surveillance or monotherapy versus multi-modal therapy.

Prostate Health Index Density Outperforms Prostate Health Index in Clinically Significant Prostate Cancer Detection

Background

Prostate-specific antigen (PSA) is considered neither sensitive nor specific for prostate cancer (PCa). We aimed to compare total PSA (tPSA), percentage of free PSA (%fPSA), the PSA density (PSAD), Prostate Health Index (PHI), and the PHI density (PHID) to see which one could best predict clinically significant prostate cancer (csPCa): a potentially lethal disease.

Methods

A total of 412 men with PSA of 2-20 ng/mL were prospectively included. Serum biomarkers for PCa was collected before transrectal ultrasound guided prostate biopsy. PHI was calculated by the formula: (p2PSA/fPSA) x √tPSA. PHID was calculated as PHI divided by prostate volume measured by transrectal ultrasound.

Results

Of the 412 men, 134 (32.5%) and 94(22.8%) were diagnosed with PCa and csPCa, respectively. We used the area under the receiver operating characteristic curve (AUC) and decision curve analyses (DCA) to compare the performance of PSA related parameters, PHI and PHID in diagnosing csPCa. AUC for tPSA, %fPSA, %p2PSA, PSAD, PHI and PHID were 0.56、0.63、0.76、0.74、0.77 and 0.82 respectively for csPCa detection. In the univariate analysis, the prostate volume, tPSA, %fPSA, %p2PSA, PHI, PSAD, and PHID were all significantly associated with csPCa, and PHID was the most important predictor (OR 1.41, 95% CI 1.15-1.72). Besides, The AUC of PHID was significantly larger than PHI in csPCa diagnosis (p=0.004). At 90% sensitivity, PHID had the highest specificity (54.1%) for csPCa and could reduce the most unnecessary biopsies (43.7%) and miss the fewest csPCa (8.5%) when PHID ≥ 0.67. In addition to AUC, DCA re-confirmed the clinical benefit of PHID over all PSA-related parameters and PHI in csPCa diagnosis. The PHID cut-off value was positively correlated with the csPCa ratio in the PHID risk table, which is useful for evaluating csPCa risk in a clinical setting.

Conclusion

The PHID is an excellent predictor of csPCa. The PHID risk table may be used in standard clinical practice to pre-select men at the highest risk of harboring csPCa.

The Role and Significance of Bioumoral Markers in Prostate Cancer

The prostate is one of the most clinically accessible internal organs of the genitourinary tract in men. For decades, the only method of screening for prostate cancer (PCa) has been digital rectal examination of 1990s significantly increased the incidence and prevalence of PCa and consequently the morbidity and mortality associated with this disease. In addition, the different types of oncology treatment methods have been linked to specific complications and side effects, which would affect the patient's quality of life. In the first two decades of the 21st century, over-detection and over-treatment of PCa patients has generated enormous costs for health systems, especially in Europe and the United States. The Prostate Specific Antigen (PSA) is still the most common and accessible screening blood test for PCa, but with low sensibility and specificity at lower values (<10 ng/mL). Therefore, in order to avoid unnecessary biopsies, several screening tests (blood, urine, or genetic) have been developed. This review analyzes the most used bioumoral markers for PCa screening and also those that could predict the evolution of metastases of patients diagnosed with PCa.

Combining prostate health index and multiparametric magnetic resonance imaging in estimating the histological diameter of prostate cancer

BACKGROUND

Although multiparametric magnetic resonance imaging (mpMRI) is widely used to assess the volume of prostate cancer, it often underestimates the histological tumor boundary. The aim of this study was to evaluate the feasibility of combining prostate health index (PHI) and mpMRI to estimate the histological tumor diameter and determine the safety margin during treatment of prostate cancer.

METHODS

We retrospectively enrolled 72 prostate cancer patients who underwent radical prostatectomy and had received PHI tests and mpMRI before surgery. We compared the discrepancy between histological and radiological tumor diameter stratified by Prostate Imaging-Reporting and Data System (PI-RADS) score, and then assessed the influence of PHI on the discrepancy between low PI-RADS (2 or 3) and high PI-RADS (4 or 5) groups.

RESULTS

The mean radiological and histological tumor diameters were 1.60 cm and 2.13 cm, respectively. The median discrepancy between radiological and histological tumor diameter of PI-RADS 4 or 5 lesions was significantly greater than that of PI-RADS 2 or 3 lesions (0.50 cm, IQR (0.00-0.90) vs. 0.00 cm, IQR (-0.10-0.20), p = 0.02). In the low PI-RADS group, the upper limit of the discrepancy was 0.2 cm; so the safety margin could be set at 0.1 cm. In the high PI-RADS group, the upper limits of the discrepancy were 1.2, 1.6, and 2.2 cm in men with PHI < 30, 30-60, and > 60; so the safety margin could be set at 0.6, 0.8, and 1.1 cm, respectively.

CONCLUSIONS

Radiological tumor diameter on mpMRI often underestimated the histological tumor diameter, especially for PI-RADS 4 or 5 lesions. Combining mpMRI and PHI may help to better estimate the histological tumor diameter.

Blood and urine biomarkers in prostate cancer: Are we ready for reflex testing in men with an elevated prostate-specific antigen?

Objective

There is no consensus on the role of biomarkers in determining the utility of prostate biopsy in men with elevated prostate-specific antigen (PSA). There are numerous biomarkers such as prostate health index, 4Kscore, prostate cancer antigen 3, ExoDX, SelectMDx, and Mi-Prostate Score that may be useful in this decision-making process. However, it is unclear whether any of these tests are accurate and cost-effective enough to warrant being a widespread reflex test following an elevated PSA. Our goal was to report on the clinical utility of these blood and urine biomarkers in prostate cancer screening.

Methods

We performed a systematic review of studies published between January 2000 and October 2020 to report the available parameters and cost-effectiveness of the aforementioned diagnostic tests. We focus on the negative predictive value, the area under the curve, and the decision curve analysis in comparing reflexive tests due to their relevance in evaluating diagnostic screening tests.

Results

Overall, the biomarkers are roughly equivalent in predictive accuracy. Each test has additional clinical utility to the current diagnostic standard of care, but the added benefit is not substantial to justify using the test reflexively after an elevated PSA.

Conclusions

Our findings suggest these biomarkers should not be used in binary fashion and should be understood in the context of pre-existing risk predictors, patient's ethnicity, cost of the test, patient life-expectancy, and patient goals. There are more recent diagnostic tools such as multi-parametric magnetic resonance imaging, polygenic single-nucleotide panels, IsoPSA, and miR Sentinel tests that are promising in the realm of prostate cancer screening and need to be investigated further to be considered a consensus reflexive test in the setting of prostate cancer screening.

Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer

Simple Summary

In the last decades, the widespread use of PSA as the standard tool for prostate cancer diagnosis led to a high rate of overdiagnosis and overtreatment. More recently, multiparametric magnetic resonance imaging (mpMRI) became part of the diagnostic pathway, and several next-generation PSA-based tests (PHI, PHI density, 4Kscore, STHLM3) have been proposed. The multivariable approach promises to help with a better stratification of PCa patients at initial diagnosis. In this study, we evaluated the performance of the prostate health index (PHI) and mpMRI for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). Our findings suggested that PHI had a better ability than mpMRI to predict positive biopsy, whereas a comparable performance in the identification of pathological aggressive PCa was pointed out. Notably, PHI and PHI density might represent useful biomarkers to recognize high-grade PCa in patients with low or uncertain PI-RADS scores on mpMRI.

Abstract

Widespread use of PSA as the standard tool for prostate cancer (PCa) diagnosis led to a high rate of overdiagnosis and overtreatment. In this study, we evaluated the performance of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). To this end, we prospectively enrolled 196 biopsy-naïve patients who underwent mpMRI. A subgroup of 116 subjects with biopsy-proven PCa underwent surgery. We found that PHI significantly outperformed both PI-RADS score (difference in AUC: 0.14; p < 0.001) and PHI density (difference in AUC: 0.08; p = 0.002) in the ability to predict positive biopsy with a cut-off value of 42.7 as the best threshold. Conversely, comparing the performance in the identification of clinically significant prostate cancer (csPCa) at RP, we found that PHI ≥ 61.68 and PI-RADS score ≥ 4 were able to identify csPCa (Gleason score ≥ 7 (3 + 4)) both alone and added to a base model including age, PSA, fPSA-to-tPSA ratio and prostate volume. In conclusion, PHI had a better ability than PI-RADS score to predict positive biopsy, whereas it had a comparable performance in the identification of pathological csPCa.

Prostate cancer screening using a combination of risk-prediction, MRI, and targeted prostate biopsies (STHLM3-MRI): a prospective, population-based, randomised, open-label, non-inferiority trial

BACKGROUND

Screening for prostate cancer using prostate-specific antigen (PSA) reduces prostate cancer mortality but can lead to adverse outcomes. We aimed to compare a traditional screening approach with a diagnostic strategy of blood-based risk prediction combined with MRI-targeted biopsies.

METHODS

We did a prospective, population-based, randomised, open-label, non-inferiority trial (STHLM3-MRI) in Stockholm county, Sweden. Men aged 50-74 years were randomly selected by Statistics Sweden and invited by mail to participate in screening; those with an elevated risk of prostate cancer, defined as either a PSA of 3 ng/mL or higher or a Stockholm3 score of 0·11 or higher were eligible for randomisation. Men with a previous prostate cancer diagnosis, who had undergone a prostate biopsy within 60 days before the invitation to participate, with a contraindication for MRI, or with severe illness were excluded. Eligible participants were randomly assigned (2:3) using computer-generated blocks of five, stratified by clinically significant prostate cancer risk, to receive either systematic prostate biopsies (standard group) or biparametric MRI followed by MRI-targeted and systematic biopsy in MRI-positive participants (experimental group). The primary outcome was the detection of clinically significant prostate cancer at prostate biopsy, defined as a Gleason score of 3 + 4 or higher. We used a margin of 0·78 to assess non-inferiority for the primary outcome. Key secondary outcome measures included the proportion of men with clinically insignificant prostate cancer (defined as a Gleason score of 3 + 3), and the number of any prostate MRI and biopsy procedures done. We did two comparisons: Stockholm3 (using scores of 0·11 and 0·15 as cutoffs) versus PSA in the experimental group (paired analyses) and PSA plus standard biopsy versus Stockholm3 plus MRI-targeted and systematic biopsy (unpaired, randomised analyses). All analyses were intention to treat. This study is registered with ClinicalTrials.gov, NCT03377881.

FINDINGS

Between Feb 5, 2018, and March 4, 2020, 49 118 men were invited to participate, of whom 12 750 were enrolled and provided blood specimens, and 2293 with elevated risk were randomly assigned to the experimental group (n=1372) or the standard group (n=921). The area under the receiver-operating characteristic curve for detection of clinically significant prostate cancer was 0·76 (95% CI 0·72-0·80) for Stockholm3 and 0·60 (0·54-0·65) for PSA. In the experimental group, a Stockholm3 of 0·11 or higher was non-inferior to a PSA of 3 ng/mL or higher for detection of clinically significant prostate cancer (227 vs 192; relative proportion [RP] 1·18 [95% CI 1·09-1·28], p<0·0001 for non-inferiority), and also detected a similar number of low-grade prostate cancers (50 vs 41; 1·22 [0·96-1·55], p=0·053 for superiority) and was associated with more MRIs and biopsies. Compared with PSA of 3 ng/mL or higher, a Stockholm3 of 0·15 or higher provided identical sensitivity to detect clinically significant cancer, and led to fewer MRI procedures (545 vs 846; 0·64 [0·55-0·82]) and fewer biopsy procedures (311 vs 338; 0·92 (0·86-1·03). Compared with screening using PSA and systematic biopsies, a Stockholm3 of 0·11 or higher combined with MRI-targeted and systematic biopsies was associated with higher detection of clinically significant cancers (227 [3·0%] men tested vs 106 [2·1%] men tested; RP 1·44 [95% CI 1·15-1·81]), lower detection of low-grade cancers (50 [0·7%] vs 73 [1·4%]; 0·46 [0·32-0·66]), and led to fewer biopsy procedures. Patients randomly assigned to the experimental group had a lower incidence of prescription of antibiotics for infection (25 [1·8%] of 1372 vs 41 [4·4%] of 921; p=0·0002) and a lower incidence of admission to hospital (16 [1·2%] vs 31 [3·4%]; p=0·0003) than those in the standard group.

INTERPRETATION

The Stockholm3 test can inform risk stratification before MRI and targeted biopsies in prostate cancer screening. Combining the Stockholm3 test with an MRI-targeted biopsy approach for prostate cancer screening decreases overdetection while maintaining the ability to detect clinically significant cancer.

FUNDING

The Swedish Cancer Society, the Swedish Research Council, and Stockholm City Council.

Prostate Cancer Liquid Biopsy Biomarkers' Clinical Utility in Diagnosis and Prognosis

Prostate cancer (PCa) is the most common cancer in men worldwide. The current gold standard for diagnosing PCa relies on a transrectal ultrasound-guided systematic core needle biopsy indicated after detection changes in a digital rectal examination (DRE) and elevated prostate-specific antigen (PSA) level in the blood serum. PSA is a marker produced by prostate cells, not just cancer cells. Therefore, an elevated PSA level may be associated with other symptoms such as benign prostatic hyperplasia or inflammation of the prostate gland. Due to this marker's low specificity, a common problem is overdiagnosis, which leads to unnecessary biopsies and overtreatment. This is associated with various treatment complications (such as bleeding or infection) and generates unnecessary costs. Therefore, there is no doubt that the improvement of the current procedure by applying effective, sensitive and specific markers is an urgent need. Several non-invasive, cost-effective, high-accuracy liquid biopsy diagnostic biomarkers such as Progensa PCA3, MyProstateScore ExoDx, SelectMDx, PHI, 4K, Stockholm3 and ConfirmMDx have been developed in recent years. This article compares current knowledge about them and their potential application in clinical practice.

Biomarkers for prostate cancer: prostate-specific antigen and beyond

In recent years, several new biomarkers supplementing the role of prostate-specific antigen (PSA) have become available for men with prostate cancer. Although widely used in an ad hoc manner, the role of PSA in screening asymptomatic men for prostate cancer is controversial. Several expert panels, however, have recently recommended limited PSA screening following informed consent in average-risk men, aged 55-69 years. As a screening test for prostate cancer however, PSA has limited specificity and leads to overdiagnosis which in turn results in overtreatment. To increase specificity and reduce the number of unnecessary biopsies, biomarkers such as percent free PSA, prostate health index (PHI) or the 4K score may be used, while Progensa PCA3 may be measured to reduce the number of repeat biopsies in men with a previously negative biopsy. In addition to its role in screening, PSA is also widely used in the management of patients with diagnosed prostate cancer such as in surveillance following diagnosis, monitoring response to therapy and in combination with both clinical and histological criteria in risk stratification for recurrence. For determining aggressiveness and predicting outcome, especially in low- or intermediate-risk men, tissue-based multigene tests such as Decipher, Oncotype DX (Prostate), Prolaris and ProMark, may be used. Emerging therapy predictive biomarkers include AR-V7 for predicting lack of response to specific anti-androgens (enzalutamide, abiraterone), BRAC1/2 mutations for predicting benefit from PARP inhibitor and PORTOS for predicting benefit from radiotherapy. With the increased availability of multiple biomarkers, personalised treatment for men with prostate cancer is finally on the horizon.

The discriminative ability of Prostate Health Index to detect prostate cancer is enhanced in combination with miR-222-3p

BACKGROUND

There is an urgent need for better prostate cancer (PCa) biomarkers due to the low specificity of prostate specific antigen (PSA).

OBJECTIVE

Prostate Health Index (PHI) is an advanced PSA-based test for early detection of PCa. The present study aim was to investigate the potential improvement of diagnostic accuracy of PHI by its combination with suitable discriminative microRNAs (miRNAs).

METHODS

A two-phase study was performed. In a discovery phase, a panel of 177 miRNAs was measured in ten men with biopsy proven PCa and ten men with histologically no evidence of malignancy (NEM). These results were validated in a second phase including 25 patients in each group. The patients of all groups were matched regarding their PSA values and PHI were measured.

RESULTS

Based on data in the discovery phase, four elevated miRNAs were selected as potential miRNA candidates for further validation. A combination of miR-222-3p as the best discriminative miRNA with PHI extended the diagnostic accuracy of PHI from an AUC value of 0.690 to 0.787 and resulted in a sensitivity of 72.0% and a specificity of 84.0%.

CONCLUSION

Circulating microRNAs show useful diagnostic potential in combination with common used biomarkers to enhance their diagnostic power.

The Prostate Health Index aids multi-parametric MRI in diagnosing significant prostate cancer

To evaluate the performance of the Prostate Health Index (PHI) in magnetic resonance imaging-transrectal ultrasound (MRI-TRUS) fusion prostate biopsy for the detection of clinically significant prostate cancer (csPCa). We prospectively enrolled 164 patients with at least one Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) ≥ 3 lesions who underwent MRI-TRUS fusion prostate biopsy. Of the PSA-derived biomarkers, the PHI had the best performance in predicting csPCa (AUC 0.792, CI 0.707–0.877) in patients with PI-RADS 4/5 lesions. Furthermore, the predictive power of PHI was even higher in the patients with PI-RADS 3 lesions (AUC 0.884, CI 0.792–0.976). To minimize missing csPCa, we used a PHI cutoff of 27 and 7.4% of patients with PI-RADS 4/5 lesions could have avoided a biopsy. At this level, 2.0% of cases with csPCa would have been missed, with sensitivity and NPV rates of 98.0% and 87.5%, respectively. However, the subgroup of PI-RADS 3 was too small to define the optimal PHI cutoff. PHI was the best PSA-derived biomarker to predict csPCa in MRI-TRUS fusion prostate biopsies in men with PI-RADS ≥ 3 lesions, especially for the patients with PI-RADS 3 lesions who gained the most value.

PHI density prospectively improves prostate cancer detection

Purpose

To evaluate the Prostate Health Index (PHI) density (PHID) in direct comparison with PHI in a prospective large cohort.

Methods

PHID values were calculated from prostate-specific antigen (PSA), free PSA and [− 2]proPSA and prostate volume. The 1057 patients included 552 men with prostate cancer (PCa) and 505 with no evidence of malignancy (NEM). In detail, 562 patients were biopsied at the Charité Hospital Berlin and 495 patients at the Sana Hospital Offenbach. All patients received systematic or magnetic resonance imaging (MRI)/ultrasound fusion-guided biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing areas under the ROC-curves (AUC). The decision curve analysis (DCA) was performed with the MATLAB Neural Network Toolbox.

Results

PHID provided a significant larger AUC than PHI (0.835 vs. 0.801; p = 0.0013) in our prospective cohort of 1057 men from 2 centers. The DCA had a maximum net benefit of ~ 5% for PHID vs. PHI between 35 and 65% threshold probability. In those 698 men within the WHO-calibrated PSA grey-zone up to 8 ng/ml, PHID was also significantly better than PHI (AUC 0.819 vs. 0.789; p = 0.0219). But PHID was not different from PHI in the detection of significant PCa.

Conclusions

Based on ROC analysis and DCA, PHID had an advantage in comparison with PHI alone to detect any PCa but PHI and PHID performed equal in detecting significant PCa.

Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis

OBJECTIVE

To evaluate Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions' impact on the diagnostic test accuracy (DTA) of MRI for prostate cancer (PC) and to derive the prevalence of PC within each PI-RADS category.

METHODS

MEDLINE and Embase were searched until April 10, 2020 for studies reporting on the DTA of MRI by PI-RADS category. Accuracy metrics were calculated using a bivariate random-effects meta-analysis with PI-RADS three lesions treated as a positive test, negative test, and excluded from the analysis. Differences in DTA were assessed utilizing meta-regression. PC prevalence within each PI-RADS category was estimated with a proportional meta-analysis.

RESULTS

In total, 26 studies reporting on 12,913 patients (4,853 with PC) were included. Sensitivities for PC in the positive, negative, and excluded test groups were 96% (95% confidence interval [CI] 92-98), 82% (CI 75-87), and 95% (CI 91-97), respectively. Specificities for the positive, negative, and excluded test groups were 33% (CI 23-44), 71% (CI 62-79), and 52% (CI 37-66), respectively. Meta-regression demonstrated higher sensitivity (p < 0.001) and lower specificity (p < 0.001) in the positive test group compared to the negative group. Clinically significant PC prevalences were 5.9% (CI 0-17.1), 11.4% (CI 6.5-17.3), 24.9% (CI 18.4-32.0), 55.7% (CI 47.8-63.5), and 81.4% (CI 75.9-86.4) for PI-RADS categories 1, 2, 3, 4 and 5, respectively.

CONCLUSION

PI-RADS category 3 lesions can significantly impact the DTA of MRI for PC detection. A low prevalence of clinically significant PC is noted in PI-RADS category 1 and 2 cases.

ADVANCES IN KNOWLEDGE

Inclusion or exclusion of PI-RADS category 3 lesions impacts the DTA of MRI for PC detection.

Role of pre-biopsy multiparametric MRI in prostate cancer diagnosis: Evidence from the literature

As the conventional workup for diagnosing prostate cancer, transrectal systematic biopsy carries the risk of sepsis and overdiagnosis of insignificant prostate cancer. In recent years, multiparametric MRI (mpMRI) has revolutionized the diagnostic approach to prostate cancer. With widespread use of the prostate imaging and data system (PIRADS), MRI reporting has been more standardized. Several landmark papers have indicated that mpMRI in combination with targeted or combined biopsy can confidently diagnose more clinically significant prostate cancer while reducing diagnoses of insignificant disease. In this review, we aim to discuss the advantages of pre-biopsy MRI based on the current literature and to address its reliability in ruling out prostate cancer, reproducibility, and cost-effectiveness.

Personalized strategies in population screening for prostate cancer

This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment.

Beyond PSA: The Role of Prostate Health Index (phi)

Background: Widespread use of prostate specific antigen (PSA) in screening procedures allowed early identification of an increasing number of prostate cancers (PCas), mainly including indolent cancer. Availability of different therapeutic strategies which have a very different impact on the patient’s quality of life suggested a strong need for tools able to identify clinically significant cancer at diagnosis. Multi-parametric magnetic resonance showed very good performance in pre-biopsy diagnosis. However, it is an expensive tool and requires an experienced radiologist. In this context, a simple blood-based test is worth investigating. In this context, researchers focused their attention on the development of a laboratory test able to minimize overdiagnosis without losing the identification of aggressive tumors. Results: Recent literature data on PCa biomarkers revealed a clear tendency towards the use of panels of biomarkers or a combination of biomarkers and clinical variables. Phi, the 4Kscore, and Stockholm3 as circulating biomarkers and the Mi-prostate score, Exo DX Prostate, and Select MD-X as urinary biomarker-based tests have been developed. In this scenario, phi is worthy of attention as a noninvasive test significantly associated with aggressive PCa. Conclusions: Literature data showed that phi had good diagnostic performance to identify clinically significant (cs) PCa, suggesting that it could be a useful tool for personalized treatment decision-making. In this review, phi potentialities, limitations, and comparisons with other blood- and urinary-based tests were explored.