A Multicentre Evaluation of the Role of the Prostate Health Index (PHI) in Regions with Differing Prevalence of Prostate Cancer: Adjustment of PHI Reference Ranges is Needed for European and Asian Settings

A 2-year prospective evaluation of the Prostate Health Index in guiding biopsy decisions in a large cohort

OBJECTIVES

To prospectively evaluate how the Prostate Health Index (PHI) impacts on clinical decision in a real-life setting for men with a prostate-specific antigen (PSA) level between 4 and 10 ng/mL and normal digital rectal examination.

PATIENTS AND METHODS

Since 2016, the PHI has been available at no cost to eligible men in all Hong Kong public hospitals. All eligible patients who received PHI testing in all public Urology units (n = 16) in Hong Kong between May 2016 and August 2017 were prospectively included and followed up. All included men had a PHI test, with its result and implications explained; the subsequent follow-up plan was then decided via shared decision-making with urologists. Patients were followed up for 2 years, with outcomes including prostate biopsy rates and biopsy findings analysed in relation to the initial PHI measurements.

RESULTS

A total of 2828 patients were followed up for 2 years. The majority (82%) had PHI results in the lower risk range (score <35). Knowing the PHI findings, 83% of the patients with elevated PSA decided not to undergo biopsy. In all, 11% and 45% opted for biopsy in the PHI score <35 and ≥35 groups, respectively. The initial detection rate of International Society of Urological Pathology (ISUP) Grade Group (GG) ≥2 cancer was higher in the PHI score ≥35 group (23%) than in the PHI score <35 group (7.9%). Amongst patients with no initial positive biopsy findings, the subsequent positive biopsy rate for ISUP GG ≥2 cancer was higher in the PHI score ≥35 group (34%) than the PHI score <35 group (13%) with a median follow-up of 2.4 years.

CONCLUSION

In a real-life setting, with the PHI incorporated into the routine clinical pathway, 83% of the patients with elevated PSA level decided not to undergo prostate biopsy. The PHI pathway also improved the high-grade prostate cancer detection rate when compared to PSA-driven strategies. Higher baseline PHI predicted subsequent biopsy outcome at 2 years. The PHI can serve as a tool to individualise biopsy decisions and frequency of follow-up visits.

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.

Evaluation of blood and urine based biomarkers for detection of clinically-significant prostate cancer

BACKGROUND

Recognizing the limitations of prostate-specific antigen (PSA) screening and the morbidity of prostate biopsies, several blood- and urine-based biomarkers have been proposed for pre-biopsy risk stratification. These assays aim to reduce the frequency of unnecessary biopsies (i.e., negative or Grade Group 1 [GG1]) while maintaining highly sensitive detection of clinically significant cancer (GG ≥ 2) prostate cancer.

METHODS

We reviewed the literature describing the use of currently available blood- and urine-based biomarkers for detection of GG ≥ 2 cancer, including the Prostate Health Index (PHI), 4Kscore, MyProstateScore (MPS), SelectMDx, ExoDx Prostate Intelliscore (EPI), and IsoPSA. To facilitate clinical application, we focused on the use of biomarkers as a post-PSA secondary test prior to biopsy, as proposed in clinical guidelines. Our outcomes included test performance measures-sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV)-as well as clinical outcomes resulting from biomarker use (i.e., unnecessary biopsies avoided, GG ≥ 2 cancers missed).

RESULTS

Contemporary validation data (2015-2023) reveal that currently available biomarkers provide ~15-50% specificity at a sensitivity of 90-95% for GG ≥ 2 PCa. Clinically, this indicates that secondary use of biomarker testing in men with elevated PSA could allow for avoidance of up to 15-50% of unnecessary prostate biopsies, while preserving detection of 90-95% of GG ≥ 2 cancers that would be detected under the traditional "biopsy all" approach.

CONCLUSIONS

The contemporary literature further supports the proposed role of post-PSA biomarker testing to reduce the use of invasive biopsy while maintaining highly sensitive detection of GG ≥ 2 cancer. Questions remain regarding the optimal application of biomarkers in combination or in sequence with mpMRI.

Liquid biomarkers in prostate cancer: recent advancements and future directions

PURPOSE OF REVIEW

Traditional diagnostic approaches of prostate cancer like PSA are limited by high false-positive rates and insufficient capture of tumour heterogeneity, necessitating the development of more precise tools. This review examines the latest advancements in liquid biomarkers for prostate cancer, focusing on their potential to refine diagnostic accuracy and monitor disease progression.

RECENT FINDINGS

Liquid biomarkers have gained prominence because of their minimally invasive nature and ability to reflect the molecular characteristics of prostate cancer. Circulating tumour cells provide insight into tumour cell dissemination and are indicative of aggressive disease phenotypes, with single-cell analyses revealing genomic instability and treatment resistance. Circulating tumour DNA offers real-time tumour genomic information, aiding in treatment decision-making in advanced prostate cancer, where it has been associated with clinical progression. MicroRNAs act as oncogenes or tumour suppressors and exhibit diagnostic and prognostic potential; however, their clinical utility is constrained by the lack of consistent validation. Extracellular vesicles contain tumour-derived biomolecules, with specific proteins demonstrating prognostic relevance. Applications of these markers to urinary testing have been demonstrated.

SUMMARY

Liquid biomarkers show potential in refining prostate cancer management. Future research should aim to integrate these biomarkers into a cohesive framework in line with precision medicine principles.

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.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

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.

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study

PURPOSE

To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed.

MATERIALS AND METHODS

A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed.

RESULTS

Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively.

CONCLUSIONS

Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

Prostate health index can stratify patients with Prostate Imaging Reporting and Data System score 3 lesions on magnetic resonance imaging to reduce prostate biopsies

We aim to evaluate prostate health index as an additional risk-stratification tool in patients with Prostate Imaging Reporting and Data System score 3 lesions on multiparametric magnetic resonance imaging. Men with biochemical or clinical suspicion of having prostate cancer who underwent multiparametric magnetic resonance imaging in two tertiary centers (Queen Mary Hospital and Princess Margaret Hospital, Hong Kong, China) between January 2017 and June 2022 were included. Ultrasound-magnetic resonance imaging fusion biopsies were performed after prostate health index testing. Those who only had Prostate Imaging Reporting and Data System score 3 lesions were further stratified into four prostate health index risk groups and the cancer detection rates were analyzed. Out of the 747 patients, 47.3% had Prostate Imaging Reporting and Data System score 3 lesions only. The detection rate of clinically significant prostate cancer in this group was 15.0%. The cancer detection rates of clinically significant prostate cancer had statistically significant differences 5.3% in prostate health index <25.0, 7.4% in prostate health index 25.0-34.9, 17.9% in prostate health index 35.0-54.9, and 52.6% in prostate health index ≥55.0 (P < 0.01). Among the patients, 26.9% could have avoided a biopsy with a prostate health index <25.0, at the expense of a 5.3% risk of missing clinically significant prostate cancer. Prostate health index could be used as an additional risk stratification tool for patients with Prostate Imaging Reporting and Data System score 3 lesions. Biopsies could be avoided in patients with low prostate health index, with a small risk of missing clinically significant prostate cancer.

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.

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.

Urine spermine and multiparametric magnetic resonance imaging for prediction of prostate cancer in Japanese men

Objectives

To investigate the role of urine spermine and spermine risk score in predicting prostate cancer (PCa) diagnoses in combination with multiparametric magnetic resonance imaging (mpMRI).

Methods

Three hundred forty seven consecutive men with elevated prostate-specific antigen (PSA) with mpMRI examination were prospectively enrolled in this study. In 265 patients with PSA levels between 4 and20 ng/ml, pre-biopsy urine samples were analyzed for spermine levels with ultra-high performance liquid chromatography (UPLC-MS/MS). Transperineal image-guided prostate biopsies with 16-18 cores were performed. Logistic regressions were used to form different models for the prediction of the PCa, and the performances were compared using the area under the curve (AUC).

Results

The median serum PSA level and prostate volume were 7.4 ng/mL and 33.9 mL, respectively. PCa and high-grade PCa (ISUP group ≥2, HGPCa) were diagnosed in 66.0% (175/265) and 132/265 (49.8%) cases, respectively. The urine spermine levels were significantly lower in men with PCa (0.87 vs. 2.20, P < 0.001). Multivariate analyses showed that age, PSA, PV, urine spermine level, and Prostate Imaging Reporting and Data System (PI-RADS) findings were independent predictors for PCa. The Spermine Risk Score is a multivariable model including PSA, age, prostate volume, and urine spermine. Adding the Spermine Risk Score to PI-RADS improved the AUC from 0.73 to 0.86 in PCa and from 0.72 to 0.83 in high grade PCa (HGPCa) prediction (both P < 0.001). At 90% sensitivity for HGPCa prediction using Spermine Risk Score, 31.1% of unnecessary biopsies could be avoided. In men with equivocal MRI PI-RADS score 3, the AUC for HGPCa prediction was 0.58, 0.79, and 0.87 for PSA, PSA density, and Spermine Risk Score, respectively.

Conclusion

Urine Spermine Risk Score, including mpMRI could accurately identify men at high risk of HGPCa and reduce unnecessary prostate biopsies. Spermine Risk Score could more accurately predict HGPCa than PSA density in men with MRI showing equivocal PI-RADS 3 lesions.

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.

Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer

We collected surface-enhanced Raman spectroscopy (SERS) data from the serum of 729 patients with prostate cancer or benign prostatic hyperplasia (BPH), corresponding to their pathological results, and built an artificial intelligence-assisted diagnosis model based on a convolutional neural network (CNN). We then evaluated its value in diagnosing prostate cancer and predicting the Gleason score (GS) using a simple cross-validation method. Our CNN model based on the spectral data for prostate cancer diagnosis revealed an accuracy of 85.14 ± 0.39%. After adjusting the model with patient age and prostate specific antigen (PSA), the accuracy of the multimodal CNN was up to 88.55 ± 0.66%. Our multimodal CNN for distinguishing low-GS/high-GS and GS = 3 + 3/GS = 3 + 4 revealed accuracies of 68 ± 0.58% and 77 ± 0.52%, respectively.

A prospective evaluation of the effect of finasteride on prostate health index (phi)

PURPOSE

5-alpha reductase inhibitor (5ARI) reduces prostate-specific antigen (PSA) by half but its effect on prostate health index (phi) is unknown. This study aims to investigate this effect and to enable accurate interpretation of phi in men with elevated PSA and on 5ARI.

METHODS

This is a prospective study evaluating the effect of finasteride on PSA, free PSA (fPSA), [ - 2]proPSA (p2PSA) and phi at 6 and 12 moths in men with PSA 4-20 ng/mL, no prior 5ARI use, and one negative prostate biopsy within 6 months before recruitment. The 5ARI Finasteride (5 mg/day) for 1 year was offered if International Prostatic Symptom Score (IPSS) was ≥ 8 at baseline. 5ARI group included patients taking finasteride, while control group included patients not on finasteride. The blood results were compared with t-test between baseline and different time points in each group and between groups at 1 year.

RESULTS

164 men fit the inclusion criteria and 150 were analyzed. In 5ARI group (n = 100) at 1 year, mean PSA reduced by 51.4% from 8.9(± SD 3.7) to 4.4(± SD 2.8)ng/mL (paired t-test, p < 0.001), fPSA reduced by 52.4% from 1.6(± 0.6) to 0.8(± 0.4)ng/mL (p < 0.001), p2PSA reduced by 55.3% from 18.4(± 8.8) to 8.3(± 5.6)pg/mL (p < 0.001), and phi reduced by 34.2% from 33.7(± 11.9) to 22.4(± 12.5) (p < 0.001). PSA and phi values in the control group remained static over 1 year and significantly higher than those in 5ARI group.

CONCLUSION

This study demonstrated p2PSA and phi are reduced by about 55% and 34% in men on 5ARI. A conversion factor of division by 0.66 is needed for phi in men on finasteride to allow the interpretation and use of phi in men on 5ARI.

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.

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.

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.

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.

Serum multi-cytokines screening identifies TRAIL and IL-10 as probable new biomarkers for prostate health index diagnostic utility adjustment in grey zone aggressive prostate cancer detection: A single-center data in China

Objective

To identify less invasive and easily applicable serum cytokine-derived biomarkers which contribute to the diagnostic utility and risk assessment ability of the prostate health index (PHI) based multivariable model in grey zone aggressive prostate cancer (AG PCa) early detection.

Methods

Serum 45 cytokines screening was performed in a small training cohort consisting of 10 sera by Luminex liquid array-based multiplexed immunoassays and identified TRAIL and IL-10 as new biomarkers for PHI diagnostic utility adjustment for further validation with a multivariable predictive model in a cohort including 79 aggressive prostate cancer patients and 209 benign prostatic hyperplasia or indolent PCa patients within the PSA grey zone.

Results

TRAIL and IL-10 were identified as potential serum biomarkers for AG PCa detection by the result of multi-cytokines screening in the univariate analysis, while multivariable logistic regression confirmed the AUC of the full risk predictive model (0.915) including tPSA, fPSA, PHI, TRAIL, and IL-10 was higher than various diagnostic strategies. DCA suggested a superior net benefit and indicated a good discriminative ability of the full risk model consistently with the result of the nomogram.

Conclusion

We suggest a significant advantage for the PHI-based multivariate combinations of serum TRAIL and IL-10 comparing to PHI or other serum-derived biomarkers alone in the detection and risk stratification of grey zone AG PCa.

Application of Multiple Ultrasonic Techniques in the Diagnosis of Prostate Cancer

Methods for diagnosing prostate cancer (PCa) are developing in the direction of imaging. Advanced ultrasound examination modes include micro-Doppler, computerized-transrectal ultrasound, elastography, contrast-enhanced ultrasound and microultrasound. When two or more of these modes are used in PCa diagnosis, the combined technique is called multiparameter ultrasound (mp-US). Mp-US provides complementary information to multiparameter magnetic resonance imaging (mp-MRI) for diagnosing PCa. At present, no study has attempted to combine the characteristics of different ultrasound modes with advanced classification systems similar to the PIRADS system in mpMRI for the diagnosis of PCa. As an imaging method, mp-US has great potential in the diagnosis of PCa.

Initial Prostate Health Index (phi) and phi density predicts future risk of clinically significant prostate cancer in men with initial negative prostate biopsy: a 6-year follow-up study

BACKGROUND

Men with elevated prostate-specific antigen (PSA) and initial negative prostate biopsy may have risk of prostate cancer (PCa) in the future. The role of Prostate Health Index (phi) in determining future PCa risk has not been studied before. This study aims to investigate the role of initial phi and phi density in predicting future PCa risk in men with initial negative biopsy.

METHODS

Five hundred sixty nine men with PSA 4-10 ng/mL were recruited between 2008 and 2015 for prostate biopsy with prior phi. Electronic clinical record of men with initial negative biopsy was reviewed. Patients and follow-up doctors were blinded to phi. Kaplan-Meier curves were used to analyze the PCa-free survival in different baseline phi and phi density groups.

RESULTS

Four hundred sixty-one men with complete follow-up data were included. Median follow-up is 77 months. PCa and HGPCa was diagnosed in 8.2% (38/461) and 4.8% (22/461) of cohort respectively. A higher baseline phi value was associated with PCa (p = 0.003) and HGPCa (p < 0.001). HGPCa was diagnosed in 0.6% (1/163) of phi < 25, 4.6% (9/195) of phi 25-34.9, and 11.7% (12/103) of phi ≥ 35 (p < 0.001). HGPCa was diagnosed in 0% (0/109) and 21.0% (13/62) with phi density of <0.4 and ≥1.2, respectively, (p < 0.001). Kaplan-Meier curves showed phi and phi density predicted PCa and HGPCa diagnoses (log-rank test, all p ≤ 0.002).

CONCLUSIONS

Initial phi or phi density predicted 6-year risk of PCa in men with initial negative prostate biopsy. Men with higher phi (≥35) or phi density (≥1.2) need closer follow-up and repeated investigation, while men with lower phi (<25) or phi density (<0.4) could have less frequent follow-up.

Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study

BACKGROUND

To investigate the value of machine learning(ML) in enhancing prostate cancer(PCa) diagnosis.

METHODS

Consecutive systematic prostate biopsies performed from Jan 2003-June 2017 were used as the training cohort, and prospective biopsies performed from July 2017-November 2019 were used as validation cohort. Men were included if PSA was 0.4-50 ng/mL, and information of digital rectal examination (DRE), Transrectal ultrasound(TRUS) prostate volume, TRUS abnormality were known. Clinically significant PCa(csPCa) was defined as Gleason 3 + 4 or above cancers. Area-under-curve (AUC) of receiver-operating characteristics (ROC) was compared between PSA, PSA density, European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator (ERSPC-RC), and various ML techniques using PSA, DRE and TRUS information. ML techniques used included XGBoost, LightGBM, Catboost, Support vector machine (SVM), Logistic regression (LR), and Random Forest (RF), where cost sensitive learning was applied.

RESULTS

Training and validation cohorts included 3881 and 778 consecutive men, respectively. RF model performed better than other ML techniques and PSA, PSA density and ERSPC-RC for prediction of PCa or csPCa in the validation cohort. In csPCa prediction, AUC of PSA, PSA density, ERSPC-RC and RF was 0.71, 0.80, 0.83 and 0.88 respectively. At 90-95% sensitivity for csPCa, RF model achieved a negative predictive value (NPV) of 97.5-98.0% and avoided 38.3-52.2% unnecessary biopsies. Decision curve analyses (DCA) showed RF model provided net clinical benefit over PSA, PSA density and ERSPC-RC.

CONCLUSION

By using the same clinical parameters, ML techniques performed better than ERSPC-RC or PSA density in csPCa predictions, and could avoid up to 50% unnecessary biopsies.

Liquid biomarkers for early detection of prostate cancer and summary of available data for their use in African-American men

BACKGROUND

Several liquid biomarker tests have been developed to account for the limitations of prostate specific antigen (PSA) screening prior to prostate biopsy. African ancestry is an established risk factor for prostate cancer (PCa) and must be particularly considered when evaluating patients with liquid biomarkers. While multiple tests have been developed over decades of exploration, recent advances can help patients and physicians incorporate data into a broader clinical context.

METHODS

We sought to review currently available liquid biomarker tests in a practical, clinically directed fashion with particular focus on performance in men with African ancestry. We reviewed discovery and validation studies and highlight important considerations for each test.

RESULTS

We discuss the advantages and limitations of percent free PSA, Prostate Health Index, Progensa® PCA3, ExoDx® Prostate Test, SelectMDx®, 4Kscore® Test, and Mi-Prostate Score and summarize salient studies on their use. A literature review of evidence specifically for men with African ancestry was conducted and available studies were summarized.

CONCLUSIONS

Liquid biomarkers can be useful tools for aiding in risk stratification prior to prostate biopsy. Use of such tests should be individualized based on a thorough knowledge of supporting evidence and the goals of the patient and physician. Further study should prioritize evaluation of such biomarkers in men with African ancestry.

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.

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.

The value of magnetic resonance imaging and ultrasonography (MRI/US)-fusion biopsy in clinically significant prostate cancer detection in patients with biopsy-naïve men according to PSA levels: A propensity score matching analysis

Objectives

Materials and methods

Results

Conclusions

The Role of Prostate Combination Biopsy Consisting of Targeted and Additional Systematic Biopsy

BACKGROUND

To identify the role of combination biopsy, which consists of both targeted and additional systematic cores, in the diagnosis of clinically significant prostate cancer (csPCa).

METHODS

We retrospectively reviewed patients with PSA levels 2.5-15 ng/mL who have a suspicious prostate lesion (with the Prostate Imaging Reporting and Data System (PI-RADS) ≥ 3) on multiparametric MRI (mpMRI) between January 2016 and December 2018. We analyzed biopsy results by PI-RADS score and biopsy methods (systematic, targeted, and combination biopsy).

RESULTS

Of the 711 total patients, an average of 4.0 ± 1.8 targeted and 8.6 ± 3.1 additional systematic biopsies were performed. The additional systematic biopsies were sampled outside the targeted biopsy area. The combination biopsies detected more csPCa (201 patients, 28.3%) than did the targeted (175 patients, 24.6%) or systematic (124 patients, 17.4%) biopsies alone (p < 0.001). In the initial biopsy samples, there was a 7% increase in the detection of csPCa than in targeted biopsy (62% to 69%). It increased by 11% in repeat biopsy (46% to 57%). There was no statistical significance in both groups (p = 0.3174).

CONCLUSIONS

Combination biopsy has the benefit of detecting csPCa in both initial and repeat biopsy when there is a suspicious lesion on mpMRI.

Biomarkers for detection of clinically significant prostate cancer: contemporary clinical data and future directions

Use of serum prostate-specific antigen (PSA) testing for early detection of prostate cancer appears to reduce cancer-specific mortality. Due to the limited specificity of PSA for clinically significant [Grade Group (GG) ≥2] cancer, however, screening carries substantial risks, including frequent unnecessary prostate biopsies and overdetection of non-aggressive cancers. To that end, serum and urine biomarkers with improved specificity for GG ≥2 cancer have been proposed for clinical use following PSA. In the current article, we present clinical validation data for five such biomarkers: PHI, 4Kscore, SelectMDx, ExoDx, and MPS. For all studies, we specify the study population (overall biopsy referral vs. pre-specified PSA ranges), previous biopsy status (biopsy-naïve vs. previous negative biopsy), and the proportion of subjects diagnosed with GG ≥2 cancer. Outcomes include test performance characteristics: sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Published data were used to compute the number of unnecessary biopsies avoided and number of GG ≥2 cancers missed if the biomarker had been used clinically to select for prostate biopsy. The evidence review is preceded by a primer on these and other clinically-relevant summary statistics.

Comparison of PHI and PHI Density for Prostate Cancer Detection in a Large Retrospective Caucasian Cohort

BACKGROUND

Beyond prostate-specific antigen (PSA), other biomarkers for prostate cancer (PCa) detection are available and need to be evaluated for clinical routine.

OBJECTIVE

The aim of the study was to evaluate the Prostate Health Index (PHI) density (PHID) in comparison with PHI in a large Caucasian group >1,000 men.

METHODS

PHID values were used from available patient data with PSA, free PSA, and [-2]pro-PSA and prostate volume from 3 former surveys from 2002 to 2014. Those 1,446 patients from a single-center cohort included 701 men with PCa and 745 with no PCa. All patients received initial or repeat biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing area under the ROC curves (AUCs), precision-recall approach, and decision curve analysis (DCA).

RESULTS

PHID medians differed almost 2-fold between PCa (1.12) and no PCa (0.62) in comparison to PHI (48.6 vs. 33; p always <0.0001). However, PHID and PHI were equal regarding the AUC (0.737 vs. 0.749; p = 0.226), and the curves of the precision-recall analysis also overlapped in the sensitivity range between 70 and 100%. DCA had a maximum net benefit of only ∼5% for PHID versus PHI between 45 and 55% threshold probability. Contrary, in the 689 men with a prostate volume ≤40 cm3, PHI (AUC 0.732) showed a significant larger AUC than PHID (AUC 0.69, p = 0.014).

CONCLUSIONS

Based on DCA, PHID had only a small advantage in comparison with PHI alone, while ROC analysis and precision-recall analysis showed similar results. In smaller prostates, PHI even outperformed PHID. The increment for PHID in this large Caucasian cohort is too small to justify a routine clinical use.

Digital rectal examination in prostate cancer screening at PSA level 3.0-3.9 ng/ml: long-term results from a randomized trial

OBJECTIVE

To evaluate digital rectal examination (DRE) as a predictor of prostate cancer (PC) at serum PSA level 3.0-3.9 ng/ml. We compared the PC incidence rates of men with different screening test results in this PSA range and analyzed DRE in comparison with free/total PSA ratio as an additional screening test.

MATERIALS AND METHODS

Using data from the FinRSPC trial, PC incidence rate ratios (IRR) for groups defined by the secondary screening test results (DRE vs. free/total PSA) were calculated for 17-year follow-up, using adjustment for age, family history of PC and place of residence. Screening test performance was evaluated by calculating sensitivity, specificity, positive and negative predictive value, and likelihood ratio.

RESULTS

The IRR for men with a positive DRE compared to those with a negative result was 1.40 (95% confidence interval (CI) 1.00-1.96), while the IRR for men with a positive free/total PSA result compared to those with a negative one was 1.62 (95% CI 1.08-2.43). The estimated sensitivity was 0.15 (95% CI 0.11-0.20, 40/270) for DRE and 0.32 (95% CI 0.23-0.41, 36/113) for free/total PSA, and the specificity 0.91 (95% CI 0.88-0.93, 419/461) for DRE and 0.85 (95% CI 0.78-0.90, 134/158) for free/total PSA.

CONCLUSIONS

Our results do not support utility of DRE as a screening test for PC at serum PSA level 3.0-3.9 ng/ml, while the results regarding free/total PSA determination were more encouraging and reconfirm the decision to switch from DRE to free/total PSA.

Tissue- and Liquid-Based Biomarkers in Prostate Cancer Precision Medicine

Worldwide, prostate cancer (PC) is the second-most-frequently diagnosed male cancer and the fifth-most-common cause of all cancer-related deaths. Suspicion of PC in a patient is largely based upon clinical signs and the use of prostate-specific antigen (PSA) levels. Although PSA levels have been criticised for a lack of specificity, leading to PC over-diagnosis, it is still the most commonly used biomarker in PC management. Unfortunately, PC is extremely heterogeneous, and it can be difficult to stratify patients whose tumours are unlikely to progress from those that are aggressive and require treatment intensification. Although PC-specific biomarker research has previously focused on disease diagnosis, there is an unmet clinical need for novel prognostic, predictive and treatment response biomarkers that can be used to provide a precision medicine approach to PC management. In particular, the identification of biomarkers at the time of screening/diagnosis that can provide an indication of disease aggressiveness is perhaps the greatest current unmet clinical need in PC management. Largely through advances in genomic and proteomic techniques, exciting pre-clinical and clinical research is continuing to identify potential tissue, blood and urine-based PC-specific biomarkers that may in the future supplement or replace current standard practices. In this review, we describe how PC-specific biomarker research is progressing, including the evolution of PSA-based tests and those novel assays that have gained clinical approval. We also describe alternative diagnostic biomarkers to PSA, in addition to biomarkers that can predict PC aggressiveness and biomarkers that can predict response to certain therapies. We believe that novel biomarker research has the potential to make significant improvements to the clinical management of this disease in the near future.

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.

Correlation between Gleason score distribution and Prostate Health Index in patients with prostate-specific antigen values of 2.5-10 ng/mL

Purpose

To determine the clinical significance and correlation between the Prostate Health Index (PHI) and Gleason score in patients with a prostate-specific antigen (PSA) value of 2.5–10 ng/mL.

Materials and Methods

This retrospective analysis included 114 patients who underwent biopsy after completion of the PHI from November 2018 to July 2019. Various parameters such as PSA, PHI, PSA density, free PSA, p2PSA, and %free PSA were collected, and correlations with biopsy Gleason score and cancer detection rates were investigated.

Results

Baseline characteristics were comparable between PHI groups (0–26.9 [n=11], 27.0–35.9 [n=17], 36.0–54.9 [n=50], and ≥55.0 [n=36]). A total of 37 patients (32.5%) were diagnosed with prostate cancer, and 28 (24.6%) were diagnosed with clinically significant prostate cancer (CSPC, Gleason score ≥7) after prostate biopsy. The cancer detection rate gradually increased with a corresponding increase in the PHI (18%, 24%, 30%, and 44%, respectively). The same pattern was observed with detecting CSPC (0%, 18%, 26%, and 33%, respectively). There was no CSPC in the groups with PHI <27.0, and Gleason score 7 began to appear in groups with PHI ≥27.0. In particular, patients with Gleason score 8 and 9 were distributed only in the groups with PHI ≥36.0.

Conclusions

The diagnostic accuracy of detection of CSPC could be increased when prostate biopsy is performed in patients with a PHI ≥36.0. In this study, there was a clear Gleason score difference when the PHI cutoff value was set to 27.0 or 36.0.

Post-Biopsy Cell-Free DNA From Blood: An Open Window on Primary Prostate Cancer Genetics and Biology

Circulating cell-free DNA (ccfDNA), released from normal and cancerous cells, is a promising biomarker for cancer detection as in neoplastic patients it is enriched in tumor-derived DNA (ctDNA). ctDNA contains cancer-specific mutations and epigenetic modifications, which can have diagnostic/prognostic value. However, in primary tumors, and in particular in localized prostate cancer (PCa), the fraction of ctDNA is very low and conventional strategies to study ccfDNA are unsuccessful. Here we demonstrate that prostate biopsy, by causing multiple injuries to the organ, leads to a significant increase in plasma concentration of ccfDNA (P<0.0024) in primary PCa patients. By calculating the minor allele fraction at patient-specific somatic mutations pre- and post-biopsy, we show that ctDNA is significantly enriched (from 3.9 to 164 fold) after biopsy, representing a transient “molecular window” to access and analyze ctDNA. Moreover, we show that newly released ccfDNA contains a larger fraction of di-, tri- and multi-nucleosome associated DNA fragments. This feature could be exploited to further enrich prostate-derived ccfDNA and to analyze epigenetic markers. Our data represent a proof-of-concept that liquid tumor profiling from peripheral blood performed just after the biopsy procedure can open a “valuable molecular metastatic window” giving access to the tumor genetic asset, thus providing an opportunity for early cancer detection and individual genomic profiling in the view of PCa precision medicine.

Comparison of contrast-enhanced ultrasound targeted biopsies versus standard systematic biopsies for prostate cancer correction in different PSA value groups in rural China

OBJECTIVE

To investigate prostate cancer detection rate of different biopsy protocols in different PSA value groups in rural China.

METHODS

A total of 186 patients underwent contrast-enhanced ultrasound (CEUS) in order to determine the puncture target prior to biopsy were enrolled in this retrospective study. All patients underwent 12-core SB combined with CEUS-TB. The biopsy results of different biopsy protocols were compared in patients with stratification by PSA value.

RESULTS

Among the 186 patients underwent prostate biopsy, the histopathologic results revealed prostate cancer (PCa) in 117 cases (62.9%) and benign lesions in 69 cases (37.1%). The PCa detection rate between 8-core SB and 12-core SB showed no significant difference in PSA 4-10 ng/ml group, while the 12-core SB was significantly higher than CEUS-TB (44.9% versus 32.7%, P = 0.01). In PSA 10-20 ng/ml group, the significant difference was not seen between SB and CEUS-TB (50.0% versus 45.7%, P = 0.15). As for PSA greater than 20 ng/ml group, the PCa detection rate by SB was higher than CEUS-TB, but showed no statistically significance (79.1% versus 76.9%, P = 0.15). In the overall patients, the biopsy core positive rate of CEUS-TB was significantly higher than SB (97% versus 55.5% and 28.5%, P = 0.0001).

CONCLUSION

The flexible use of SB combined with CEUS-TB can reduce the number of biopsy cores in higher PSA groups. It has clinical importance in the detection of PCa in different PSA value groups in rural China.

Epidemiology and genomics of prostate cancer in Asian men

Prostate cancer is a global health problem, but incidence varies considerably across different continents. Asia is traditionally considered a low-incidence area, but the incidence and mortality of prostate cancer have rapidly increased across the continent. Substantial differences in epidemiological features have been observed among different Asian regions, and incidence, as well as mortality-to-incidence ratio, is associated with the human development index. Prostate cancer mortality decreased in Japan and Israel from 2007 to 2016, but mortality has increased in Thailand, Kyrgyzstan and Uzbekistan over the same period. Genomic analyses have shown a low prevalence of ERG oncoprotein in the East Asian population, alongside a low rate of PTEN loss, high CHD1 enrichments and high FOXA1 alterations. Contributions from single-nucleotide polymorphisms to prostate cancer risk vary with ethnicity, but germline mutation rates of DNA damage repair genes in metastatic prostate cancer are comparable in Chinese and white patients from the USA and UK. Pharmacogenomic features of testosterone metabolism might contribute to disparities seen in the response to androgen deprivation between East Asian men and white American and European men. Overall, considerable diversity in epidemiology and genomics of prostate cancer across Asia defines disease characteristics in these populations, but studies in this area are under-represented in the literature. Taking into account this intracontinental and intercontinental heterogeneity, translational studies are required in order to develop ethnicity-specific treatment strategies.

Identification of a novel non-invasive biological marker to overcome the shortcomings of PSA in diagnosis and risk stratification for prostate cancer: Initial prospective study of developmental endothelial locus-1 protein

Objective

This prospective study sought to clarify the developmental endothelial locus-1 (Del-1) protein as values of diagnosis and risk stratification of prostate cancer (PCa).

Design

From February 2017 to December 2019, a total 458 patients who underwent transrectal ultrasound guided prostate biopsy or surgery of benign prostatic hyperplasia agreed to research of Del-1 protein. We prospectively compared and analyzed the Del-1 protein and prostate specific antigen (PSA) in relation to the patients’ demographic and clinicopathological characteristics.

Results

Mean age was 68.86±8.55 years. Mean PSA and Del-1 protein was 21.72±89.37, 0.099±0.145, respectively. Two hundred seventy-six (60.3%) patients were diagnosed as PCa. Among them, 181 patients underwent radical prostatectomy (RP). There were significant differences in Del-1 protein between benign and PCa group (0.066±0.131 vs 0.121±0.149, respectively, p<0.001). When we set the cut-off value of del-1 protein as 0.120, in patients with 3≤PSA≤8, positive predictive value and specificity of Del-1 protein (≥0.120) for predicting PCa were 88.9% (56/63) and 93.5% (101/108), respectively. Among 181 patients who underwent RP, there were significant differences in Del-1 protein according to stage (pT2 vs pT3a vs ≥pT3b) (0.113±0.078, 0.171±0.121, 0.227±0.161, respectively, p<0.001) and to Gleason score (6 (3+3) or 7 (3+4) vs 7 (4+3) or 8 (4+4) vs 9 or 10) (0.134±0.103, 0.150±0.109, 0.212±0.178, respectively, P = 0.044). Multivariate analysis showed that PSA, Del-1 protein and high Gleason score (≥9) were the independent prognostic factors for predicting higher pT stage (≥3b). Furthermore, age, PSA and Del-1 protein were independent prognostic factors for predicting significant PCa.

Conclusion

Patients with PCa showed higher expression of Del-1 protein than benign patients. Del-1 protein increased with the stage and Gleason score of PCa. Collaboration with PSA, Del-1 protein can be a non-invasive useful marker for diagnosis and risk stratification of PCa.

Performance of Prostate Health Index in Biopsy Naïve Black Men

PURPOSE

The Prostate Health Index is validated for prostate cancer detection but has not been well validated for Gleason grade group 2-5 prostate cancer detection in Black men. We hypothesize that the Prostate Health Index has greater accuracy than prostate specific antigen for detection of Gleason grade group 2-5 prostate cancer. We estimated probability of overall and Gleason grade group 2-5 prostate cancer across previously established Prostate Health Index ranges and identified Prostate Health Index cutoffs that maximize specificity for Gleason grade group 2-5 prostate cancer with sensitivity >90%.

MATERIALS AND METHODS

We recruited a "cancer-free" Black control cohort (135 patients) and a cohort of biopsy naïve Black men (158) biopsied for elevated prostate specific antigen. Descriptive statistics compared the prostate cancer cases and controls and the frequency of Gleason grade group 2-5 prostate cancer across Prostate Health Index scores. Receiver operating characteristics compared the discrimination of prostate specific antigen, Prostate Health Index and other prostate specific antigen related biomarkers. Sensitivity and specificity for Gleason grade group 2-5 prostate cancer detection were assessed at prostate specific antigen and Prostate Health Index thresholds alone and in series.

RESULTS

Of biopsied subjects 32.9% had Gleason grade group 2-5 prostate cancer. In Blacks with prostate specific antigen from 4.0-10.0 ng/ml, Prostate Health Index and prostate specific antigen had similar discrimination for Gleason grade group 2-5 prostate cancer (0.63 vs 0.57, p=0.27). In Blacks with prostate specific antigen ≤10.0, a threshold of prostate specific antigen ≥4.0 had 90.4% sensitivity for Gleason grade group 2-5 prostate cancer; a threshold of prostate specific antigen ≥4.0 with Prostate Health Index ≥35.0 in series avoided unnecessary biopsy in 33.0% of men but missed 17.3% of Gleason grade group 2-5 prostate cancer. Prostate specific antigen ≥4.0 with Prostate Health Index ≥28.0 in series spared biopsy in 17.9%, while maintaining 90.4% sensitivity of Gleason grade group 2-5 prostate cancer.

CONCLUSIONS

The Prostate Health Index has moderate accuracy in detecting Gleason grade group 2-5 prostate cancer in Blacks, but Prostate Health Index ≥28.0 can be safely used to avoid some unnecessary biopsies in Blacks.

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.

Urine spermine and multivariable Spermine Risk Score predict high-grade prostate cancer

BACKGROUND

To investigate the role of urine spermine and Spermine Risk Score in prediction of high-grade prostate cancer (HGPCa, ISUP grade group ≥2).

METHODS

Nine hundred and five consecutive men with elevated PSA were prospectively recruited from two hospitals. Core analyses focused on consecutive men with PSA 4-20 ng/mL (n = 600). Pre-biopsy urine without prior prostatic massage was analyzed for spermine level with ultra-high performance liquid chromatography with triple quadrupole mass spectrometer (UPLC-MS/MS). The proportions of PCa and HGPCa were compared across different spermine ranges. Logistic regressions were used to form different models, and their performances were compared using area under curve (AUC) and decision curve analysis (DCA).

RESULTS

PCa and HGPCa were diagnosed in 30.8% (185/600) and 17.2% (103/600) men, respectively, and were significantly associated with lower urine spermine levels. Between the lowest and highest quartiles of spermine results, a threefold increase in PCa risk (49.3% vs. 16.7%) and 3.5-fold increase in ISUP grade group ≥2 PCa risk (31.3% vs. 8.7%) were observed. Multivariate analysis showed PSA, prostate volume (PV), digital rectal examination (DRE), and spermine, which were independent predictors for PCa and HGPCa, and a Spermine Risk Score with these factors achieved the highest AUC of 0.78 for PCa and 0.82 for HGPCa. At 90% sensitivity for HGPCa, 36.7% biopsies and 24.4% ISUP grade group 1 diagnoses could have been avoided, with a negative predictive value of 95.4%. DCA revealed net clinical benefit of the Spermine Risk Score. Internal validation with bootstrapping showed good discrimination and calibration.

CONCLUSION

Urine spermine and Spermine Risk Score identified men at higher risk of HGPCa and reduced unnecessary biopsies.

Discovery of extracellular vesicles derived miR-181a-5p in patient's serum as an indicator for bone-metastatic prostate cancer

Purpose: To identify extracellular vesicle (EV)-delivered microRNAs in the patient's serum as indicators for bone-metastatic prostate cancer.

Methods: First, the profiling change of serum EV-delivered miRNAs in patients with either benign prostatic hyperplasia (BPH), non-bone metastatic prostate cancer or bone-metastatic prostate cancer was detected by microRNA deep sequencing assay and microRNA-chip array assay, respectively. Second, the candidates were further confirmed using TaqMan microRNA assay in two independent validation cohorts of total 176 patients with either BPH, non-bone metastatic prostate cancer or bone metastatic prostate cancer to seek the most valuable microRNA(s).

Results: Through microRNA deep sequencing and microRNA-chip array, we found 4 prospective EV-delivered miRNAs including miR-181a-5p with significantly upregulated expression in bone metastatic groups than in non-bone metastatic prostate cancer groups (p < 0.05). In the validation cohorts, logistic regression analysis was performed to evaluate the diagnostic association of candidates with bone metastasis, which indicated that miR-181a-5p was significantly associated with bone metastatic prostate cancer. Furthermore, accuracy estimate of each candidate for the diagnosis of bone metastatic prostate cancer was quantified using the area under the receiver-operating characteristic curve (AUC), which identified miR-181a-5p as the best biomarker with the AUCs of 85.6% for diagnosis of prostate cancer and 73.8% for diagnosis of bone metastatic prostate cancer.

Conclusion: EV-delivered miR-181a-5p from patient's serum is a promising diagnostic biomarker for bone metastatic prostate cancer.

Biomarker discrimination and calibration with MRI-targeted biopsies: an analysis with the Stockholm3 test

BACKGROUND

The validated Stockholm3 test is used to improve PC detection. Stockholm3, however, was developed using systematic biopsies. We aimed to assess Stockholm3 operating performance when using MRI-targeted biopsies for PC detection.

METHODS

A prospective cohort of 532 men was considered for prostate biopsy during 2016-2017. All men underwent Stockholm3 testing and MRI before biopsy. All PIRADs ≥3 lesion underwent targeted biopsy; all men underwent systematic biopsy. The primary outcome was ISUP Grade Group ≥2 (GG ≥ 2) PC. Detection strategies included: (1) systematic biopsies alone, (2) targeted biopsies alone, (3) targeted with associated systematic biopsies for MRI+, and (4) all biopsies in all men. For each strategy, the Stockholm3 operating characteristics were assessed with discrimination, calibration, and decision curve analysis (DCA).

RESULTS

Median age was 65 years, median PSA was 6.2 ng/mL, median Stockholm3 score was 16.5%, and overall detection of GG ≥ 2 PC was 36% (193/532). Stockholm3 showed accurate discrimination for separating GG ≥ 2 cancer from benign and GG1, with an area under the curve of 0.84-0.86 depending on the biopsy strategy. Calibration analysis showed that Stockholm3 underestimated risks for GG ≥ 2 PC risk using MRI-targeted biopsies: there was a net benefit over biopsies in all men for Stockholm3 at risk thresholds varying from >3% in systematic biopsies to >15% in targeted with systematic biopsies in MRI+ men. When using a Stockholm3 score of >10% cutoff, a range of 32-38% of biopsies could be avoided while missing 5-11% of GG ≥ 2 PC and 0-3% of GG ≥ 3 PC.

CONCLUSIONS

Stockholm3 shows high discriminatory performance in an MRI-targeted biopsy setting, however risks are underpredicted due to MRI-targeted biopsies being more sensitive than the systematic biopsies for which Stockholm3 was developed. Stockholm3, along with any risk prediction model developed for systematic prostate biopsy decisions, will need recalibration for optimal use in an MRI-driven biopsy setting.

Advances in the diagnostic options for prostate cancer

Over the past decade, despite the controversies surrounding prostate cancer screening, significant refinements have improved its application. PSA screening, although it has been questioned, appears to confer a mortality benefit and remains the most effective way to identify the possible presence of prostate cancer. Methods to improve the specificity of PSA screening and limit overdiagnosis of indolent cancers, including risk-stratified screening regimens, are currently being utilized. Certain imaging modalities, such as multiparametric MRI, have proven to be excellent adjuncts providing improved risk stratification and the ability for targeted biopsies; however, concerns over variability in interpretation and generalizability persist. A number of novel biomarkers have become available with nearly all demonstrating the ability to improve upon the specificity of PSA screening; however, optimal timing, direct comparisons, and usefulness in conjunction with imaging modalities remain to be elucidated. With the improvement in testing options and recognition of the risk/benefit ratio for men undergoing screening for prostate cancer, the increasing role of shared decision making in the process is emphasized.

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.

The Movember Prostate Cancer Landscape Analysis: an assessment of unmet research needs

Prostate cancer is a heterogeneous cancer with widely varying levels of morbidity and mortality. Approaches to prostate cancer screening, diagnosis, surveillance, treatment and management differ around the world. To identify the highest priority research needs across the prostate cancer biomedical research domain, Movember conducted a landscape analysis with the aim of maximizing the effect of future research investment through global collaborative efforts and partnerships. A global Landscape Analysis Committee (LAC) was established to act as an independent group of experts across urology, medical oncology, radiation oncology, radiology, pathology, translational research, health economics and patient advocacy. Men with prostate cancer and thought leaders from a variety of disciplines provided a range of key insights through a range of interviews. Insights were prioritized against predetermined criteria to understand the areas of greatest unmet need. From these efforts, 17 research needs in prostate cancer were agreed on and prioritized, and 3 received the maximum prioritization score by the LAC: first, to establish more sensitive and specific tests to improve disease screening and diagnosis; second, to develop indicators to better stratify low-risk prostate cancer for determining which men should go on active surveillance; and third, to integrate companion diagnostics into randomized clinical trials to enable prediction of treatment response. On the basis of the findings from the landscape analysis, Movember will now have an increased focus on addressing the specific research needs that have been identified, with particular investment in research efforts that reduce disease progression and lead to improved therapies for advanced prostate cancer.

The Movember global Landscape Analysis Committee (LAC) was established to act as an independent group of experts across urology, medical oncology, radiation oncology, radiology, pathology, translational research, health economics and patient advocacy to identify the highest priority research needs across the prostate cancer biomedical research domain. Findings from the landscape analysis illustrate the research priorities in prostate cancer and will enable Movember to focus on specific needs, with particular investment in research to reduce disease progression and improve therapies for advanced prostate cancer.

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.