Serum isoform [-2]proPSA derivatives significantly improve prediction of prostate cancer at initial biopsy in a total PSA range of 2-10 ng/ml: a multicentric European study

Established and emerging liquid biomarkers for prostate cancer detection: A review

Prostate cancer remains one of the most frequently diagnosed cancers among men in the world today. Since its introduction in 1987 and FDA approval in 1994, prostate specific antigen (PSA) has reduced prostate cancer specific mortality considerably. However, the positive and negative predictive value of PSA is less than ideal and can lead to the over-detection of clinically insignificant prostate cancer. In the search for better screening measures to identify this cohort, liquid biomarkers for prostate cancer have emerged. In this review we will explore the commonly used urine and blood based prostate cancer liquid biomarkers. We detail the mechanism of each test and the validation studies that underscore their efficacy. Additionally, we will examine each test's effect on shared decision making as well as their cost efficacy in clinical practice.

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

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.

2022 Update on Prostate Cancer Epidemiology and Risk Factors-A Systematic Review

CONTEXT

Prostate cancer (PCa) is one of the most common cancers worldwide. Understanding the epidemiology and risk factors of the disease is paramount to improve primary and secondary prevention strategies.

OBJECTIVE

To systematically review and summarize the current evidence on the descriptive epidemiology, large screening studies, diagnostic techniques, and risk factors of PCa.

EVIDENCE ACQUISITION

PCa incidence and mortality rates for 2020 were obtained from the GLOBOCAN database of the International Agency for Research on Cancer. A systematic search was performed in July 2022 using PubMed/MEDLINE and EMBASE biomedical databases. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and was registered in PROSPERO (CRD42022359728).

EVIDENCE SYNTHESIS

Globally, PCa is the second most common cancer, with the highest incidence in North and South America, Europe, Australia, and the Caribbean. Risk factors include age, family history, and genetic predisposition. Additional factors may include smoking, diet, physical activity, specific medications, and occupational factors. As PCa screening has become more accepted, newer approaches such as magnetic resonance imaging (MRI) and biomarkers have been implemented to identify patients who are likely to harbor significant tumors. Limitations of this review include the evidence being derived from meta-analyses of mostly retrospective studies.

CONCLUSIONS

PCa remains the second most common cancer among men worldwide. PCa screening is gaining acceptance and will likely reduce PCa mortality at the cost of overdiagnosis and overtreatment. Increasing use of MRI and biomarkers for the detection of PCa may mitigate some of the negative consequences of screening.

PATIENT SUMMARY

Prostate cancer (PCa) remains the second most common cancer among men, and screening for PCa is likely to increase in the future. Improved diagnostic techniques can help reduce the number of men who need to be diagnosed and treated to save one life. Avoidable risk factors for PCa may include factors such as smoking, diet, physical activity, specific medications, and certain occupations.

Relationship between Proclarix and the Aggressiveness of Prostate Cancer

INTRODUCTION

Proclarix is a CE-marked test that provides the risk of clinically significant prostate cancer (csPCa), ranging from 0% to 100%, based on the serum measurement of Thrombospondin-1, cathepsin D, prostate-specific antigen (PSA), and percentage of free PSA in addition to age. We hypothesize that Proclarix could be correlated with PCa aggressiveness. We analyzed the association of this new biomarker with four surrogates of aggressiveness: grade group (GG) in the biopsy, clinical stage, risk of biochemical recurrence after primary treatment of localized PCa, and pathology in the surgical specimen.

MATERIAL AND METHODS

This is a retrospective study from 606 men with suspicion of PCa [PSA of ≥ 3.0 ng/mL and/or abnormal digital rectal examination (DRE)], in whom Proclarix was assessed (0-100%). The GG was defined by the International Society of Urological Pathology categories. The TNM was used for clinical staging (cT based on DRE, whereas cN and cM were established with computed tomography and 99-technetium bone scintigraphy). The risk of biochemical recurrence of localized PCa after primary treatment was defined by combining PSA, GG, and cT. Finally, an unfavorable pathology in a surgical specimen was defined as GG > 2 or pT ≥ 3.

RESULTS

The median age of the cohort was 67 years old, with a median PSA of 7 ng/mL and a rate of abnormal DRE of 23.3%. CsPCa was detected in 254 men (41.9%), with a median Proclarix of 60.1% compared with 37.3% obtained in patients with insignificant PCa and 20.7% in men without PCa. Among patients with GG > 3, Proclarix was significantly higher (58.2%) than in those with GG of 3 or lower (33.1%, p < 0.001). Men with localized tumors exhibited a Proclarix median of 37.3% compared with those with advanced disease (60.1%, p < 0.001). Proclarix levels among 197 patients with low and intermediate risk of biochemical recurrence were 24.9% and 35.0%, respectively, significantly lower compared with patients with high-risk disease (58.7%, p < 0.001). Unfavorable pathology was observed in 35 patients out of the 79 who underwent radical prostatectomy, with a Proclarix median of 35.7% compared with 23.7% obtained in patients with favorable pathology (p = 0.013). Proclarix and magnetic resonance imaging were independent predictors of the four surrogates of aggressiveness analyzed.

CONCLUSION

There is a correlation between Proclarix and the aggressiveness of PCa.

Prostate cancer risk prediction based on clinical factors and prostate-specific antigen

INTRODUCTION

The incidence rate of prostate cancer (PCa) has continued to rise in Korea. This study aimed to construct and evaluate a 5-year PCa risk prediction model using a cohort with PSA < 10 ng/mL by incorporating PSA levels and individual factors.

METHODS

The PCa risk prediction model including PSA levels and individual risk factors was constructed using a cohort of 69,319 participants from the Kangbuk Samsung Health Study. 201 registered PCa incidences were observed. A Cox proportional hazards regression model was used to generate the 5-year risk of PCa. The performance of the model was assessed using standards of discrimination and calibration.

RESULTS

The risk prediction model included age, smoking status, alcohol consumption, family history of PCa, past medical history of dyslipidemia, cholesterol levels, and PSA level. Especially, an elevated PSA level was a significant risk factor of PCa (hazard ratio [HR]: 1.77, 95% confidence interval [CI]: [1.67-1.88]). This model performed well with sufficient discrimination ability and satisfactory calibration (C-statistic: 0.911, 0.874; Nam-D'Agostino test statistic:19.76, 4.21 in the development and validation cohort, respectively).

CONCLUSIONS

Our risk prediction model was effective in predicting PCa in a population according to PSA levels. When PSA levels are inconclusive, an assessment of both PSA and specific individual risk factors (e.g., age, total cholesterol, and family history of PCa) could provide further information in predicting PCa.

Urinary exosomal prostate-specific antigen is a noninvasive biomarker to detect prostate cancer: Not only old wine in new bottles

The study aimed at evaluating the performance of urinary exosomal prostate-specific antigen (UE-PSA) to predict the results of initial prostate biopsies and discriminate clinically significant prostate cancer (Gleason score ≥ 7, csPCa) from nonsignificant PCa (Gleason score < 7, nsPCa) plus benign patients. Two hundred seventy-two consecutive participants were admitted who underwent a prostate biopsy. The UE-PSA expression was detected by enzyme-linked immunosorbent assay (ELISA). The predictive power and clinical value of UE-PSA was assessed by receiver operating characteristic (ROC), decision curve analysis (DCA) and waterfall plots. UE-PSA was upregulated in PCa compared to benign patients (P < .001) and csPCa compared to nsPCa plus benign patients (P < .001). UE-PSA achieved an AUC of 0.953 (0.905-0.989) in distinguishing PCa from benign patients and an AUC of 0.879 (0.808-0.941) in predicting csPCa from nsPCa plus benign patients. These results were validated in an additional multicenter cohort. In addition, DCA showed that UE-PSA achieved the highest net benefit at almost any threshold probability compared to tPSA and %fPSA. As the waterfall plot showed, the UE-PSA assay could avoid 57.6% (155 cases) and 34.6% (93 cases) unnecessary biopsies while only missing 2.6% (7 cases) and 1.5% (4 cases) of the cases of csPCa at the cutoff value of 90% and 95% sensitivity, respectively. We validated that UE-PSA presented great diagnostic power and clinical utility to diagnose PCa and csPCa. UE-PSA could be a promising noninvasive biomarker to improve PCa detection.

Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

Prospective evaluation of the role of imaging techniques and TMPRSS2:ERG mutation for the diagnosis of clinically significant prostate cancer

Objectives

To test the hypothesis of a relationship between a specific genetic lesion (T2:ERG) and imaging scores, such as PI-RADS and PRI-MUS, and to test the effectiveness of these parameters for the diagnosis of prostate cancer (PCa) and clinically significant PCa (csPCa).

Materials and methods

This is a prospective study of men with suspected PCa enrolled between 2016 and 2019 at a high-volume tertiary hospital. Patients underwent systematic US-guided biopsy, plus targeted biopsy if they were presenting with >=1 suspicious lesion (PI-RADS>2) at mpMRI or PR-IMUS >2 at micro-ultrasound assessment. For each patient, one core from the highest PI-RADS or PRI-MUS lesion was collected for T2:ERG analysis. Multivariable logistic regression models (LRMs) were fitted for csPCa with a clinical model (age, total PSA, previous biopsy, family history for PCa), a clinical plus PI-RADS, clinical plus T2:ERG, clinical plus PI-RADS plus T2:ERG, and T2:ERG plus PI-RADS alone.

Results

The cohort consists of 158 patients: 83.5% and 66.2% had respectively a diagnosis of PCa and csPCa after biopsy. A T2:ERG fusion was found in 37 men and 97.3% of these patients harbored PCa, while 81.1% were diagnosed with csPCa. SE of T2:ERG assay for csPCa was 28.8%, SP 87.0%, NPV 38.8%, and PPV 81.1%. Of 105 patients who performed mpMRI 93.% had PIRADS ≥3. SE of mpMRI for csPCa was 98.5%, SP was 12.8%, NPV was 83.3%, and PPV was 65.7%. Among 67 patients who were subjected to micro-US, 90% had a PRI-MUS ≥3. SE of micro-US for csPCa was 89.1%, SP was 9.52%, NPV was 28.6%, and PPV was 68.3%. At univariable LRM T2:ERG was confirmed as independent of mpMRI and micro-US result (OR 1.49, p=0.133 and OR 1.82, p=0.592, respectively). At multivariable LRM the clinical model alone had an AUC for csPCa of 0.74 while the clinical model including PI-RADS and T2:ERG achieved an AUC of 0.83.

Conclusions

T2:ERG translocation and imaging results are independent of each other, but both are related csPCa. To evaluate the best diagnostic work-up for PCa and csPCa detection, all available tools (T2:ERG detection and imaging techniques) should be employed together as they appear to have a complementary role.

Utility of serum biomarkers for predicting cancer in patients with previous negative prostate biopsy

PURPOSE

To review the role of serum biomarkers: prostate-specific antigen (PSA), PSA density (PSAD), free:total PSA ratio, prostate health index (PHI) and PHI density (PHID), along with magnetic resonance imaging (MRI) for identification of clinically significant prostate cancer (PCa), comparing their utility in patients with persistently raised PSA levels after a prior negative prostate biopsy (PNB).

METHODS

In this single-centre prospective observational study conducted from September 2015 to October 2020, patients underwent a saturation biopsy via the transperineal route. If a Prostate Imaging Reporting and Data System version 2 (PIRADS) 3 and above lesion was seen on MRI, targeted biopsies were also obtained. Information on clinical history, lesion characteristics, PIRADS classification and follow-up was collected. The sensitivity, specificity and area under curve (AUC) for each of the biomarkers were calculated.

RESULTS

351 men underwent saturation biopsy with or without targeted biopsies. 103 patients had a PNB. Among this PNB cohort, 43 (41.7%) men had a benign outcome, while 60 (58.3%) men had histopathologically diagnosed PCa, of which 41 (39%) were clinically significant. All patients underwent multiparametric MRI scans prior to biopsy. Within this cohort, PHI and PHID had the best abilities to predict for clinically significant PCa with an AUC of 0.73 and 0.70 respectively, compared to 0.65 for PSAD, 0.34 for free:total PSA and 0.56 for PSA.

CONCLUSION

A significant proportion of patients are diagnosed with PCa after a PNB. This study shows that PHI and PHI densities may be suitable adjuncts predicting for clinically significant PCa in patients with PNB.

Diagnostic and prognostic factors in patients with prostate cancer: a systematic review

OBJECTIVES

As part of the PIONEER Consortium objectives, we have explored which diagnostic and prognostic factors (DPFs) are available in relation to our previously defined clinician and patient-reported outcomes for prostate cancer (PCa).

DESIGN

We performed a systematic review to identify validated and non-validated studies.

DATA SOURCES

MEDLINE, Embase and the Cochrane Library were searched on 21 January 2020.

ELIGIBILITY CRITERIA

Only quantitative studies were included. Single studies with fewer than 50 participants, published before 2014 and looking at outcomes which are not prioritised in the PIONEER core outcome set were excluded.

DATA EXTRACTION AND SYNTHESIS

After initial screening, we extracted data following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of prognostic factor studies (CHARMS-PF) criteria and discussed the identified factors with a multidisciplinary expert group. The quality of the included papers was scored for applicability and risk of bias using validated tools such as PROBAST, Quality in Prognostic Studies and Quality Assessment of Diagnostic Accuracy Studies 2.

RESULTS

The search identified 6604 studies, from which 489 DPFs were included. Sixty-four of those were internally or externally validated. However, only three studies on diagnostic and seven studies on prognostic factors had a low risk of bias and a low risk concerning applicability.

CONCLUSION

Most of the DPFs identified require additional evaluation and validation in properly designed studies before they can be recommended for use in clinical practice. The PIONEER online search tool for DPFs for PCa will enable researchers to understand the quality of the current research and help them design future studies.

ETHICS AND DISSEMINATION

There are no ethical implications.

Reclassification prediction of first-year protocol biopsy on active surveillance of prostate cancer by p2PSA-related parameters: from PRIAS-JAPAN

BACKGROUND

There is no useful predictive marker for reclassification on active surveillance. Thus, we aimed to investigate thresholds of [-2] proPSA (p2PSA)-related parameters to predict reclassification of the first-year protocol biopsy (1-year PBx) and evaluate the influence of clinical decision-making.

METHODS

This was an observational, prospective cohort study conducted at 19 Japanese institutes. The inclusion criteria included clinical stage T1c/T2, prostate-specific antigen (PSA) levels ≤10 ng/mL, PSA density <0.2 ng/ml/cc, one or two positive biopsy cores, and Gleason score (GS) ≤6 (GS ≦7 for patients aged ≥70 years) at diagnostic biopsy. All participants were required to receive a blood-sampling test on a protocol visit at inclusion and at the 1-year PBx. PSA and PSA isoforms (free PSA, p2PSA) were measured, and parameters (%free PSA, %p2PSA, phi) were calculated. Multivariable logistic regression models were used to predict the reclassification risk. To assess the predictive power and thresholds for reclassification, we plotted Receiver Operating Characteristic (ROC) curves. Decision curve analysis (DCA) was used to evaluate the variables that yielded a net clinical benefit.

RESULTS

A total of 135 patients were included, and 36 patients were reclassified on the 1-year PBx. Multivariate analyses showed that %p2PSA and phi at inclusion and p2PSA, %p2PSA, and phi before the 1-year PBx were significant predictors of reclassification at the 1-year PBx. The ROC analysis showed an optimal cutoff point, sensitivity, and specificity of %p2PSA and phi before the 1-year PBx of 1.64, 86%, 49% and 35.92, 89%, 47%, respectively. The DCA showed that phi before the 1-year PBx had the highest net benefit. The study limitation was its single-arm observational design.

CONCLUSIONS

%p2PSA and phi before the 1-year PBx had a good prediction power. phi is the most useful indicator for clinical decision-making on active surveillance.

TRIAL REGISTRATION

This study is registered atthe Japan Trial Register with ID UMIN000009876 ( https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000011573 ).

[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.

Beyond Prostate Specific Antigen: New Prostate Cancer Screening Options

Prostate specific antigen (PSA) is one of the best-known biomarkers for screening, diagnosis and follow-up of patients for prostate cancer. Owing to several inherent limitations with PSA, various newer blood and urinary based biomarkers have been evaluated in pursuit of better detection and risk stratification of prostate cancer cases. A combination of these different markers, in adjunct with clinical risk factors, and recent advances in imaging promises to offer better diagnostic performance with clearer risk stratification guiding therapeutics. We carried out an extensive literature search for the different biomarkers available for screening and diagnosis of prostate cancer, compared their performance with serum PSA to allow clinicians to draw meaningful conclusions to offer their patients a more personalized medical care.

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.

Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

Purpose

The purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.

Methods

We retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.

Results

In the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.

Conclusion

The prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

Usefulness of the prostate health index in predicting the presence and aggressiveness of prostate cancer among Korean men: a prospective observational study

Background

We aimed to evaluate the usefulness of the Beckman Coulter prostate health index (PHI) and to compare it with total prostate-specific antigen (PSA) levels and related derivatives in predicting the presence and aggressiveness of prostate cancer (PCa) in the Korean population.

Methods

A total of 140 men who underwent their first prostate biopsy for suspected PCa were included in this prospective observational study. The diagnostic performance of total PSA, free PSA, %free PSA, [–2] proPSA (p2PSA), %p2PSA, and PHI in detecting and predicting the aggressiveness of PCa was estimated using the receiver operating characteristic curve (ROC) and logistic multivariate regression analyses.

Results

Of 140 patients, PCa was detected in 63 (45%) of participants, and 48 (76.2%) of them had significant cancer with a Gleason score (GS) ≥ 7. In the whole group, the area under the curve (AUC) for ROC analysis of tPSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI were 0.63, 0.57, 0.69, 0.69, 0.72, and 0.76, respectively, and the AUC was significantly greater in the PHI group than in the tPSA group (p = 0.005). For PCa with GS ≥ 7, the AUCs for tPSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI were 0.62, 0.58, 0.41, 0.79, 0.86, and 0.87, respectively, and the AUC was significantly greater in the PHI group than in the tPSA group (p < 0.001). In the subgroup with tPSA 4–10 ng/mL, both %p2PSA and PHI were strong independent predictors for PCa (p = 0.007, p = 0.006) and significantly improved the predictive accuracy of a base multivariable model, including age, tPSA, fPSA and %fPSA, using multivariate logistic regression analysis. (p = 0.054, p = 0.048). Additionally, at a cutoff PHI value > 33.4, 22.9% (32/140) of biopsies could be avoided without missing any cases of aggressive cancer.

Conclusions

This study shows that %p2PSA and PHI are superior to total PSA and %fPSA in predicting the presence and aggressiveness (GS ≥ 7) of PCa among Korean men. Using PHI, a significant proportion of unnecessary biopsies can be avoided.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-021-00897-2.

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.

Clinical utility of a serum biomarker panel in distinguishing prostate cancer from benign prostate hyperplasia

Prostate-specific antigen (PSA) screening for prostate cancer (PCa) is limited by the lack of specificity but is further complicated in the benign prostatic hyperplasia (BPH) population which also exhibit elevated PSA, representing a clear unmet need to distinguish BPH from PCa. Herein, we evaluated the utility of FLNA IP-MRM, age, and prostate volume to stratify men with BPH from those with PCa. Diagnostic performance of the biomarker panel was better than PSA alone in discriminating patients with negative biopsy from those with PCa, as well as those who have had multiple prior biopsies (AUC 0.75 and 0.87 compared to AUC of PSA alone 0.55 and 0.57 for patients who have had single compared to multiple negative biopsies, respectively). Of interest, in patients with PCa, the panel demonstrated improved performance than PSA alone in those with Gleason scores of 5–7 (AUC 0.76 vs. 0.56) and Gleason scores of 8–10 (AUC 0.74 vs. 0.47). With Gleason scores (8–10), the negative predictive value of the panel is 0.97, indicating potential to limit false negatives in aggressive cancers. Together, these data demonstrate the ability of the biomarker panel to perform better than PSA alone in men with BPH, thus preventing unnecessary biopsies.

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.

The current value of histological findings in negative prostate biopsies to predict the future risk of clinically significant prostate cancer

BACKGROUND

Repeat prostate biopsy (PBx) is recommended under persistent suspicion of prostate cancer (PCa) or in the face of the following findings: atypical small acinar proliferation (ASAP); extense (≥3 biopsy sites) high-grade prostatic intraepithelial neoplasia (HGPIN); or HGPIN with atypical glands; suspicious for adenocarcinoma (PIN-ATYP). Nowadays; multiparametric magnetic resonance imaging (mpMRI) and mpMRI targeted PBx (MRI-TBx) are recommended in repeat PBx. Our objective was to analyze the current value of ASAP; mHGPIN; PIN-ATYP and other histological findings to predict clinically significant PCa (csPCa) risk.

METHODS

Retrospective analysis of 377 repeat PBxs. MRI-TBx was performed when Prostate Imaging-Reporting and Data System (PI-RADS) score >3 and 12-core transrectal ultrasound (TRUS) systematic PBx when ≤2. ASAP; HGPIN; mHGPIN; PIN-ATYP; and 8 other histological findings were prospectively reported in negative PBx. CsPCa was defined as ISUP group grade >2.

RESULTS

Incidence of ASAP; multifocal HGPIN (mHGPIN) and PINATYP was 4.2%; 39.7% and 3.7% respectively; and csPCa rate was statistically similar among men with these histological findings. However; the rate of csPCa was 22.2% when proliferative inflammatory atrophy (PIA) was present; and 36.1% when it was not. PIA was the only histological finding which predicted lower risk of csPCa; with an OR of 0.54 (95%CI: 0.308-0.945; P = .031). In addition; PIA was an independent predictor of a model combining clinical variables and mpMRI which reached area under de ROC curve of 0.86 (95%CI: 0.83-0.90).

CONCLUSION

PIA emerged as the only predictive histological finding of csPCa risk and can contribute to a predictive model. mHGPIN failed to predict csPCa risk. The low incidence of ASAP (4.2%) and PIN-ATYP (3.7%) prevented us from drawing conclusions.

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.

Diagnostic accuracy of [-2]proPSA versus Gleason score and Prostate Health Index versus Gleason score for the determination of aggressive prostate cancer: a systematic review

OBJECTIVE

The objective of this review was to determine the diagnostic accuracy of [-2]proPSA (p2PSA) and the Prostate Health Index compared to the Gleason score in determining the aggressiveness of prostate cancer.

INTRODUCTION

Prostate cancer is the most commonly diagnosed cancer in men. However, the utility of currently available biomarkers for determining the aggressive form of the disease remains unknown. This review sought to determine the diagnostic accuracy of two new biomarkers in determining the aggressive form of prostate cancer.

INCLUSION CRITERIA

Diagnostic accuracy studies that enrolled men of any age and any prostate specific antigen (PSA) level with histologically confirmed prostate cancer in which Prostate Health Index and p2PSA were assessed in comparison to Gleason score for the determination of aggressive prostate cancer were considered for inclusion. There was no time limitation on study inclusion.

METHODS

A three-step search strategy was utilized to identify both published and unpublished studies in the English language in the following sources: PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science, Google Scholar, MedNar, and SIGLE. Databases were searched from inception to January 2019. Study selection, critical appraisal, data extraction, and data synthesis were done according to the approach recommended by JBI.

RESULTS

A total of 12 studies (n = 8462) that recruited men with aggressive prostate cancer were considered in this review. The majority of included subjects had a total PSA level of 2 to 10ng/mL. The sensitivity of the Prostate Health Index ranged from 67% to 97% while specificity ranged from 6% to 64%. At a Prostate Health Index threshold of 25 and below (three studies, n = 3222), pooled sensitivity was 97% (95% confidence interval [CI], 95% to 98%) and specificity was 10% (95% CI, 6% to 16%). At a Prostate Health Index threshold of between 26 and 35 (six studies, n = 6030), pooled sensitivity was 87% (95% CI, 8% to 91%) and specificity was 45% (95% CI, 39% to 50%). At a Prostate Health Index threshold of 36 and above (five studies, n = 1476), pooled sensitivity was 72% (95% CI, 64% to 79%) and specificity was 74% (95% CI, 68% to 80%). Only one study assessed p2PSA. Sensitivity ranged from 80% to 95%, and specificity ranged from 9.9% to 27.9% with increasing threshold values from 7.9 to 10.9ng/mL.

CONCLUSIONS

Overall, both Prostate Health Index and p2PSA have acceptable accuracy for the determination of the likelihood of aggressive prostate cancer. However, the inverse relationship between sensitivity and specificity makes it difficult to determine an optimum cut-off value for positivity. Further research is warranted to determine their utility in the management of prostate cancer.

Can serum endoglin be used to improve the diagnostic performance in prostate cancer?

PURPOSE

New biomarkers may contribute to avoid unnecessary biopsies resulting from the suboptimal performance of prostate-specific antigen (PSA) testing. This study aimed to assess serum endoglin as a prostate cancer (PCa) diagnostic tool among biopsy candidates.

METHODS

A total of 262 consecutive patients referred for prostate biopsy based on abnormal digital rectal examination and/or elevated total PSA (tPSA) who had serum endoglin assessed by solid-phase enzyme-linked immunosorbent assay were selected. Receiver operating characteristic curves were used to compare the predictive accuracy of different combinations of biomarkers to distinguish between PCa and benign prostatic conditions, and to identify cut-offs that maximize the ability of endoglin to rule out patients for biopsy (highest sensitivities).

RESULTS

Serum endoglin levels were higher in patients with PCa (median: 7.86 vs. 5.88 pg/mL, P < 0.001). Among patients with baseline tPSA ≤ 10 ng/mL the area under the curve was 0.69 for endoglin. Approximately one-quarter of the patients had serum endoglin < 4.92 ng/mL (sensitivity: 90.3%; specificity: 32.8%), and the probability of PCa varied from 37.7% before testing to 15.2% among those with low endoglin levels [negative predictive value (NPV) = 84.8%]. When restricting the analyses to patients with free/total PSA ratio > 0.25, the probability of cancer was less than 5% among those with serum endoglin < 6.04 ng/mL (sensitivity: 93.8%; specificity: 56.1%), corresponding to a NPV of 95.8%; this could allow sparing approximately 40% of patients from biopsy.

CONCLUSIONS

Serum endoglin may be useful in clinical practice to distinguish between PCa and non-cancer patients among prostatic biopsy candidates.

The current value of histological findings in negative prostate biopsies to predict the future risk of clinically significant prostate cancer

INTRODUCTION

Repeat prostate biopsy (PBx) is recommended under persistent suspicion of prostate cancer (PCa) or in the face of the following findings: atypical small acinar proliferation (ASAP), extense (≥3 biopsy sites) high-grade prostatic intraepithelial neoplasia (HGPIN), or HGPIN with atypical glands, suspicious for adenocarcinoma (PIN-ATYP). Nowadays, multiparametric magnetic resonance imaging (mpMRI) and mpMRI targeted PBx (MRI-TBx) are recommended in repeat PBx. Our objective was to analyze the current value of ASAP, mHGPIN, PIN-ATYP and other histological findings to predict clinically significant PCa (csPCa) risk.

METHODS

Retrospective analysis of 377 repeat PBxs. MRI-TBx was performed when Prostate Imaging-Reporting and Data System (PI-RADS) score>3 and 12-core transrectal ultrasound (TRUS) systematic PBx when≤2. ASAP, HGPIN, mHGPIN, PIN-ATYP, and 8 other histological findings were prospectively reported in negative PBx. CsPCa was defined as ISUP group grade>2.

RESULTS

Incidence of ASAP, multifocal HGPIN (mHGPIN) and PINATYP was 4.2%, 39.7% and 3.7% respectively, and csPCa rate was statistically similar among men with these histological findings. However, the rate of csPCa was 22.2% when proliferative inflammatory atrophy (PIA) was present, and 36.1% when it was not. PIA was the only histological finding which predicted lower risk of csPCa, with an OR of .54 (95% CI: .308-.945, P=.031). In addition, PIA was an independent predictor of a model combining clinical variables and mpMRI which reached area under de ROC curve of .86 (95% CI: .83-.90).

CONCLUSIONS

PIA emerged as the only predictive histological finding of csPCa risk and can contribute to a predictive model. mHGPIN failed to predict csPCa risk. The low incidence of ASAP (4.2%) and PIN-ATYP (3.7%) prevented us from drawing conclusions.

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.

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.

Diagnostic performance of prostate cancer antigen 3 and the Prostate Health Index in detecting overall and clinically significant prostate cancer in men at first biopsy: A meta-analysis

OBJECTIVE

To evaluate the diagnostic value of prostate cancer antigen 3 and the Prostate Health Index for the detection of overall and clinically significant prostate cancer at initial biopsy.

METHODS

A search was conducted in the online databases PubMed, Embase and the Cochrane database, and relevant articles published up to 23 February 2020 were extracted.

RESULTS

Twenty studies including 10 376 patients were included in the meta-analysis. The pooled sensitivity and specificity were 0.55 (95% confidence interval 0.53-0.57) and 0.74 (95% confidence interval 0.72-0.75) for prostate cancer antigen 3 and 0.88 (95% confidence interval 0.86-0.90) and 0.36 (95% confidence interval 0.34-0.38) for the Prostate Health Index. The area under the curve was 0.72 for prostate cancer antigen 3 and 0.76 for the Prostate Health Index. The combination of prostate cancer antigen 3 and the Prostate Health Index had a higher area under the curve (0.79) and diagnostic odds ratio (5.83) than the use of Prostate Health Index (area under the curve 0.75, diagnostic odds ratio 4.69) or prostate cancer antigen 3 (area under the curve 0.77, diagnostic odds ratio 4.84) alone. For clinically significant prostate cancer detection, the pooled sensitivity and specificity were 0.80 (95% confidence interval 0.76-0.84) and 0.53 (95% confidence interval 0.50-0.55), respectively, for prostate cancer antigen 3, and 0.77 (95% confidence interval 0.71-0.82) and 0.64 (95% confidence interval 0.61-0.67), respectively, for the Prostate Health Index. The area under the curve was 0.71 for prostate cancer antigen 3 and 0.77 for the Prostate Health Index.

CONCLUSION

Both the Prostate Health Index and prostate cancer antigen 3 showed acceptable and similar results for the detection of overall and clinically significant prostate cancer at first biopsy. A combination of these two diagnostic tests may be more helpful than the use of either test alone in prostate cancer management.

Commercialized Blood-, Urinary- and Tissue-Based Biomarker Tests for Prostate Cancer Diagnosis and Prognosis

In the diagnosis and prognosis of prostate cancer (PCa), the serum prostate-specific antigen test is widely used but is associated with low specificity. Therefore, blood-, urinary- and tissue-based biomarker tests have been developed, intended to be used in the diagnostic and prognostic setting of PCa. This review provides an overview of commercially available biomarker tests developed to be used in several clinical stages of PCa management. In the diagnostic setting, the following tests can help selecting the right patients for initial and/or repeat biopsy: PHI, 4K, MiPS, SelectMDx, ExoDx, Proclarix, ConfirmMDx, PCA3 and PCMT. In the prognostic setting, the Prolaris, OncotypeDx and Decipher test can help in risk-stratification of patients regarding treatment decisions. Following, an overview is provided of the studies available comparing the performance of biomarker tests. However, only a small number of recently published head-to-head comparison studies are available. In contrast, recent research has focused on the use of biomarker tests in relation to the (complementary) use of multiparametric magnetic resonance imaging in PCa diagnosis.

Cost-Effectiveness Analysis of Prostate Health Index in Decision Making for Initial Prostate Biopsy

Background

Clinical studies have suggested that prostate health index (phi) outperforms prostate-specific antigen (PSA) tests in prostate cancer detection. The cost-effectiveness of phi with different cutoffs is poorly understood in the context of decision making for prostate biopsy.

Methods

In a multicenter cohort, 3,348 men with elevated total PSA (tPSA) underwent initial prostate biopsy from August 2013 to May 2019. We constructed a decision model to evaluate the incremental cost-effectiveness ratios of different phi cutoffs. Total costs and reimbursement payments were based on the fee schedule of Shanghai Basic Medical Insurance and converted into United States dollars ($). Two willingness-to-pay thresholds were estimated as one or three times the average gross domestic product per capita of China ($7,760 or $23,279, respectively).

Results

The total costs of prostate biopsy and PSA tests were estimated at $315 and $19, respectively. The cost of phi test varied between $72 to $130 in different medical centers. Under different phi cutoffs (from 23 to 35), phi test predicted reductions of 420 (21.7%) to 972 (50.2%) in unnecessary biopsies, with a total gain of 23.77-57.58 quality adjusted life-years compared to PSA tests. All the cutoffs would be cost-effective for patients with tPSA levels of 2-10 ng/ml. Applying 27 as the cutoff was cost-effective for each tPSA range, with missing positive cases ranging from 11 (3.4%) to 33 (11.5%).

Conclusions

Using phi test was cost-effective in the decision-making process for initial prostate biopsy, especially for patients with tPSA values between 2-10 ng/ml. The phi cutoff of 27 was cost-effective regardless of tPSA ranges and should be recommended from a health-economic perspective.

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.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Detection and Prognosis of Prostate Cancer Using Blood-Based Biomarkers

Prostate cancer (PCa) is second only to lung cancer as a cause of death. Clinical assessment of patients and treatment efficiency therefore depend on the disease being diagnosed as early as possible. However, due to issues regarding the use of prostate-specific antigen (PSA) for screening purposes, PCa management is among the most contentious of healthcare matters. PSA screening is problematic primarily because of diagnosis difficulties and the high rate of false-positive biopsies. Novel PCa biomarkers, such as the Prostate Health Index (PHI) and the 4Kscore, have been proposed in recent times to improve PSA prediction accuracy and have shown higher performance by preventing redundant biopsies. The 4Kscore also shows high precision in determining the risk of developing high-grade PCa, whereas elevated PHI levels suggest that the tumor is aggressive. Some evidence also supports the effectiveness of miRNAs as biomarkers for distinguishing PCa from benign prostatic hyperplasia and for assessing the aggressiveness of the disease. A number of miRNAs that possibly act as tumor inhibitors or oncogenes are impaired in PCa. These new biomarkers are comprehensively reviewed in the present study in terms of their potential use in diagnosing and treating PCa.

Explication of the roles of prostate health index (PHI) and urokinase plasminogen activator (uPA) as diagnostic and predictor tools for prostate cancer in equivocal PSA range of 4-10 ng/mL

Background

Prostate cancer (PCa) is one of the most commonly encountered cancers and the leading cause of death worldwide. Currently used biomarkers accounts difficulties in discriminating benign from malignant cases or predicting outcome, so investigating new biomarkers performance is needed.

Objectives

Assessment of diagnostic and predictor roles of prostate health index (PHI) and urokinase plasminogen activator (uPA) in PCa.

Methods

194 males with initial tPSA of 4-10 ng/mL were categorized into three groups: PCa, benign prostatic hyperplasia (BPH) and healthy control. Serum levels of tPSA, fPSA, p2PSA, and uPA were performed by ELISA with calculation of PHI as (p2PSA/fPSA) × √PSA.

Results

PHI and uPA were significantly higher in PCa patients relevant to BPH and healthy control (p ≤ 0.001). Both markers outperformed all assessed biomarkers and showed the highest area under the curve (AUC) in ROC curve analysis. Both were significantly higher in PCa patients with {Gleason score ≥ 7, late stages (cT2b,c; T3), LN extension and distant metastasis}relative to their counterparts. Additionally, PHI and uPA and were independent predictors of distant metastasis and Gleason score ≥ 7, while PHI was predictor of LN invasion (β = 0.25, p = 0.004).

Conclusion

PHI and uPA would be of potential value in discriminating between PCa, BPH and healthy men in addition, both are promising as independent predictors of adverse pathological features.

Establishing a novel prediction model for improving the positive rate of prostate biopsy

Background

At present, prostate-specific antigen (PSA) is the primary evaluation index for judging the necessity of prostate cancer (PCa) biopsy. However, there is a high false-positive rate and a low predictive value due to many interference factors. In this study, we tried to find a novel prediction model that could improve the positive rate of prostate biopsy and reduce unnecessary biopsy.

Methods

We retrospectively studied 237 patients, including their age, body mass index (BMI), PSA, prostate volume (PV), prostate imaging-reporting and data system (PI-RADS) v2 score, neutrophil-lymphocyte ratio (NLR), biopsy Gleason score (BGS), and other information. The univariate and multivariate logistic analyses were used to screen out indicators related to PCa. After establishing a prediction formula model, we used receiver operating characteristic (ROC) curves to assess its prediction performance.

Results

Our study found that age, PSA, PI-RADS v2 score, and diabetes significantly correlated with PCa. Based on multivariate logistic regression analysis results, we created the following prediction formula: Y = 2.599 × PI-RADS v2 score + 1.766 × diabetes + 0.052 × age + 1.005 × PSAD - 9.119. ROC curves showed the formula's threshold was 0.3543. The composite formula had an excellent capacity to detect PCa with the area under the curve (AUC) of 0.91. In addition, the composite formula also achieved significantly better sensitivity, specificity, and diagnostic accuracy than PSA, PSA density (PSAD), and PI-RADS v2 score alone.

Conclusions

Our predictive formula predicted performance better than PSA, PSAD, and PI-RADS v2 score. It can thus contribute to the diagnosis of PCa and be used as an indicator for prostate biopsy, thereby reducing unnecessary biopsy.

Long Noncoding RNA SNHG1 Contributes to the Promotion of Prostate Cancer Cells Through Regulating miR-377-3p/AKT2 Axis

Background: Long noncoding RNAs could serve as a candidate target for prostate cancer (PCa) diagnosis and treatment. The current study aimed to investigate the role and functions of SNHG1 in PCa cells. Materials and Methods: Abnormal expression of SNHG1, survival analysis, and target gene were determined or predicted by bioinformatics techniques. Gene expressions at transcriptional and translational levels were determined by Quantitative Real-time PCR and Western blotting, respectively. Cell viability, growth, and apoptosis rate were detected by Cell Counting Kit-8, colony formation assay and flow cytometry. Results: The results showed that SNHG1 was highly expressed in PCa tissues, which was accompanied by decreased miR-377-3p expression and poor overall survival rate, and that miR-377-3p was predicted as the target of SNHG1 in PCa cells. Moreover, SNHG1 counteracted the effects of miR-377-3p on inhibiting cell growth and promoting apoptosis of PCa cells. Furthermore, miR-377-3p counteracted the effects of AKT2 on promoting cell viability, growth, and suppressing apoptosis of PCa cells. In addition, AKT2 expression was proved to be regulated by miR-377-3p. Conclusions: The SNHG1/miR-377-3p/AKT2 regulatory axis in PCa cells was disclosed. The upregulated AKT2 might be a result of dysregulated interaction balance between the expressions of miR-377-3p and SNHG1. Based on such discoveries, the intervention of SNHG1/miR-377-3p/AKT2 axis could be further explored in the treatment of PCa.

Preoperative %p2PSA and Prostate Health Index Predict Pathological Outcomes in Patients with Prostate Cancer Undergoing Radical Prostatectomy

To evaluate the predictive accuracy of the %p2PSA and prostate health index (PHI) in predicting aggressive pathological outcomes in patients with prostate cancer (PCa) undergoing radical prostatectomy (RP), we enrolled 91 patients with organ-confined PCa who were treated with robot-assisted RP. p2PSA levels and the PHI were investigated for their ability to predict pathological results. The %p2PSA and PHI were both significantly higher in patients with ≥pT3 disease, high-risk disease, positive surgical margin, or seminal vesical invasion (SVI). In univariable analysis, p2PSA derivatives were significant predictors of the presence of ≥pT3 disease, high-risk disease, positive surgical margin, and SVI. To predict adverse pathological outcomes at a sensitivity of 90%, p2PSA derivatives had higher specificity than standard PSA derivatives. In multivariable analysis, additional increases in the area under the receiver operating characteristic curve (AUC) were observed with the %p2PSA and PHI for ≥pT3 disease, high-risk disease, and positive surgical margin (8.2% and 2.7%, 6.2% and 4.1%, and 8.6% and 5.4%, respectively). A PHI ≥61.26 enhanced the predictive accuracy of the model for SVI by increasing the AUC from 0.624 to 0.819 (p = 0.009). The preoperative %p2PSA and PHI accurately predict adverse pathological results and are useful for decision-making.

Optimal threshold of the prostate health index in predicting aggressive prostate cancer using predefined cost-benefit ratios and prevalence

PURPOSES

The aim of the study was to determine optimal threshold of the Prostate Health Index (Phi) for predicting aggressive prostate cancer (PCa), taking into account misclassification costs, prevalence, and plausible risk factors.

METHODS

This prospective cohort study analyzed patients undergoing prostate biopsy and Phi testing. The primary endpoint was aggressive PCa, defined as biopsy Gleason score ≥ 7. The data about age, total prostate-specific antigen (PSA), percentage of free PSA (%fPSA), and digital rectal examination (DRE) were extracted from the patient files. We divided the patients to the low- and high-risk group. The clinical usefulness of the Phi was assessed by the decision curve analysis. The predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), per-class metrics, and the potential reduction in unnecessary biopsies. The uncertain interval of Phi values was also determined.

RESULTS

There were 200 men included in the study, 35 (17.5%) of them having aggressive PCa. Important predictors of aggressive PCa were %fPSA, DRE, Phi, and belonging to the high-risk group. With optimal threshold of 30.7, about 32% unnecessary biopsies would be avoided. The optimal threshold of Phi was lower in the high-risk group than in the low-risk group. The AUC for detection of aggressive PCa was 0.791. Per-class metrics showed that the Phi has insufficient diagnostic accuracy. The lower and upper limits of the uncertain interval were 41.8 and 51.4, respectively.

CONCLUSION

Different thresholds of the Phi could be optimal, depending on prevalence, patient characteristics, and misclassification costs. Further studies with a larger patient sample are necessary to confirm our conclusions.

Preanalytical stability of [-2]proPSA in whole blood stored at room temperature before separation of serum and plasma: implications to Phi determination

Background [-2]proPSA seems to outperform free/total prostate-specific antigen (PSA) ratio in prostate cancer diagnosis. However, [-2]proPSA stability remains an underestimated issue. We examined [-2]proPSA stability over time in whole blood before separation of serum and plasma and its implications for prostate health index (Phi) determination. Total PSA (tPSA) and free PSA (fPSA) stabilities were also assessed. Methods Blood was drawn from 26 patients and separated in two tubes for plasma (K2EDTA and K2EDTA plus protease inhibitors - P100) and one for serum (clot activator plus gel separator). Tubes were stored at room temperature before centrifugation 1, 3 and 5 h for serum and EDTA plasma or 1 and 5 h for P100 plasma. To investigate the influence of gel separator on markers' stability, blood was collected from 10 patients in three types of tubes to obtain serum: tubes with clot activator plus gel separator, with silica particles or glass tubes. Biomarkers were assayed with chemiluminescent immunoassays. Results [-2]proPSA and Phi levels significantly and progressively increased over time in serum (+4.81% and +8.2% at 3 h; +12.03% and +14.91% at 5 h, respectively, vs. 1 h; p<0.001). Conversely, [-2]proPSA levels did not change in plasma (EDTA or P100). tPSA levels did not change over time in serum or plasma, whereas fPSA decreased in serum. All markers were higher in plasma than in serum at any time point. This difference did not seem to be attributable to the use of gel for serum preparation. Conclusions EDTA prevented spurious in vitro modifications in PSA-related isoforms, confirming that a stabilized blood sample is a prerequisite for [-2]proPSA measurement and Phi determination.

Prostate health index density predicts aggressive pathological outcomes after radical prostatectomy in Taiwanese patients

BACKGROUND

There are models to predict pathological outcomes based on established clinical and prostate-specific antigen (PSA)-derived parameters; however, they are not satisfactory. p2PSA and its derived biomarkers have shown promise for the diagnosis and prognosis of prostate cancer (PCa). The aim of this study was to investigate whether p2PSA-derived biomarkers can assist in the prediction of aggressive pathological outcomes after radical prostatectomy (RP).

METHODS

We prospectively enrolled patients who were diagnosed with PCa and treated with RP between February 2017 and December 2018. Preoperative blood samples were analyzed for tPSA, free PSA (fPSA), percentage of fPSA (%fPSA), [-2]proPSA (p2PSA), and percentage of p2PSA (%p2PSA). Prostate health index (PHI) was calculated as (p2PSA/fPSA) × √tPSA. Prostate volume was determined by transrectal ultrasound using the ellipsoid formula, and PHI density was calculated as PHI/prostate volume. The areas under the receiver operating characteristic curve were estimated for various PSA/p2PSA derivatives. Aggressive pathological outcomes measured after RP were defined as pathological T3 or a Gleason score (GS) >6 as determined in RP specimens.

RESULTS

One hundred and forty-four patients were included for analysis. Postoperative GS was >6 in 86.1% of the patients, and pT stage was T3a or more in 54.2%. Among all PSA- and p2PSA-derived biomarkers, PHI density was the best biomarker to predict aggressive pathological outcomes after RP. The odds ratio of having an aggressive pathological outcome of RP was 8.796 (p = 0.001). In multivariate analysis, adding %fPSA to base model did not improve the accuracy (area under curve), but adding PHI and PHI density to base model improved the accuracy by 2% and 16%, respectively, in predicting pT3 stage or GS ≥ 7. The risk of pT3 stage or GS ≥ 7 was 20.8% for PHI density <1.125, and 64.6% for PHI density >1.125 (sensitivity: 74.6% and specificity: 88.9%).

CONCLUSION

PHI density may further aid in predicting aggressive pathological outcomes after RP. This biomarker may be useful in preoperative counseling and may have potential in decision making when choosing between definitive treatment and active surveillance of newly diagnosed PCa.

A Rich Array of Prostate Cancer Molecular Biomarkers: Opportunities and Challenges

Prostate cancer is the most prevalent non-skin cancer in men and is the leading cause of cancer-related death. Early detection of prostate cancer is largely determined by a widely used prostate specific antigen (PSA) blood test and biopsy is performed for definitive diagnosis. Prostate cancer is asymptomatic in the early stage of the disease, comprises of diverse clinico-pathologic and progression features, and is characterized by a large subset of the indolent cancer type. Therefore, it is critical to develop an individualized approach for early detection, disease stratification (indolent vs. aggressive), and prediction of treatment response for prostate cancer. There has been remarkable progress in prostate cancer biomarker discovery, largely through advancements in genomic technologies. A rich array of prostate cancer diagnostic and prognostic tests has emerged for serum (4K, phi), urine (Progensa, T2-ERG, ExoDx, SelectMDx), and tumor tissue (ConfirmMDx, Prolaris, Oncoytype DX, Decipher). The development of these assays has created new opportunities for improving prostate cancer diagnosis, prognosis, and treatment decisions. While opening exciting opportunities, these developments also pose unique challenges in terms of selecting and incorporating these assays into the continuum of prostate cancer patient care.

[Active surveillance of prostate cancer : An update]

Prostate cancer is a heterogeneous disease. In cases of low-risk prostate cancer, active surveillance represents an attractive alternative treatment. Significant complications of a definitive treatment can therefore be delayed or completely avoided. Despite strict inclusion criteria for active surveillance, the diagnosis of low-risk prostate cancer can be inaccurate and there is therefore a risk of missing the optimal point in time for definitive treatment. Multimodal diagnostics and continuous aftercare are therefore crucial.

Can expressed prostatic secretions effect prostate biopsy decision of urologist?

Objectives:

To evaluate the frequency of NIH category IV prostatitis, and the use of expressed prostatic secretions tests in an effort to improve the reliability of prostate specific antigen as an indicator, to avoid unnecessary prostate biopsy.

Materials and Methods:

178 expressed prostatic secretion positive patients with serum prostate specific antigen levels of ≥ 2.5 ng / mL were included in present prospective study. The diagnostic evaluation included detailed history and physical examination, digital rectal examination, urine analysis, urine culture, and expressed prostatic secretions tests. Transrectal ultrasonography was used both to measure prostate volume and conduct 12 core prostate biopsy.

Results:

The prevalence of NIH category IV prostatitis was 36.9% (178 / 482) in our population of men. In our study patients (n: 178) prostate biopsy results were classified as; 66 prostatitis, 81 BPH, and 31 Pca. In asymptomatic prostatitis group, expressed prostatic secretion mean leucocyte ratio was higher compared to other two groups (p < 0.0001). The relation between number of expressed prostatic secretion leucocytes and prostatitis, benign prostate hyperplasia, and prostate cancer is analyzed. If 16 is taken as the cut of number for leucocyte presence, its sensitivity is 0.92 (AUC = 0.78 p = 0.01).

Conclusions:

The number of leucocytes in expressed prostatic secretion is higher in the chronic prostatitis group. If the leukocyte presence of 16 and above is taken as the cut off point, the sensitivity becomes 0.92 (AUC = 0.78). We firmly believe that our new cut off value may be used as to aid prostate specific antigen and derivates while giving biopsy decision.

Thrombospondin 1 and cathepsin D improve prostate cancer diagnosis by avoiding potentially unnecessary prostate biopsies

Objectives

To investigate and further validate if two novel cancer‐related glycoproteins, discovered by a genetic‐guided proteomics approach, can distinguish benign disease from prostate cancer (PCa) in men with enlarged prostates.

Patients and Methods

A retrospective study was performed that included men with a total prostate‐specific antigen (PSA) concentration of 2.0–10 ng/mL, negative digital rectal examination and enlarged prostate (volume ≥35 mL). Serum samples were collected between 2011 and 2016 at a single centre from 474 men before they underwent prostate biopsy. Serum concentrations of thrombospondin 1 (THBS1) and cathepsin D (CTSD) glycoproteins were combined with the percentage of free PSA to total PSA ratio (%fPSA) to predict any or significant cancer at biopsy.

Results

The multivariable logistic regression model including THBS1, CTSD and %fPSA discriminated among biopsy‐positive and biopsy‐negative patients in the validation set with an area under the curve (AUC) of 0.86 (P < 0.001, 95% confidence interval (CI) 0.82–0.91), while %fPSA alone showed an AUC of 0.64 (P < 0.001, 95% CI 0.57–0.71). At 90% sensitivity for PCa, the specificity of the model was 62%, while %fPSA had a specificity of 23%. For high grade (Gleason score ≥ 7 in prostatectomy specimen) PCa, the specificity was 48% at 90% sensitivity, with an AUC of 0.83, (P < 0.001, 95% CI 0.77 to 0.88). Limitations of the study include the retrospective set‐up and single‐centre cohort.

Conclusions

A model combining two cancer‐related glycoproteins (THBS1 and CTSD) and %fPSA can improve PCa diagnosis and may reduce the number of unnecessary prostate biopsies because of its improved specificity for PCa when compared to %fPSA alone.