Positive predictive value of prostate biopsy indicated by prostate-specific-antigen-based prostate cancer screening: trends over time in a European randomized trial*

In-Depth Glycoproteomic Assay of Urinary Prostatic Acid Phosphatase

Prostate-specific antigen (PSA) is a well-known clinical biomarker in prostate cancer (PCa) diagnosis, but a better test is still needed, as the serum-level-based PSA quantification exhibits limited specificity and comes with poor predictive value. Prior to PSA, prostatic acid phosphatase (PAP) was used, but it was replaced by PSA because PSA improved the early detection of PCa. Upon revisiting PAP and its glycosylation specifically, it appears to be a promising new biomarker candidate. Namely, previous studies have indicated that PAP glycoforms differ between PCa and non-PCa individuals. However, an in-depth characterization of PAP glycosylation is still lacking. In this study, we established an in-depth glycoproteomic assay for urinary PAP by characterizing both the micro- and macroheterogeneity of the PAP glycoprofile. For this purpose, PAP samples were analyzed by capillary electrophoresis coupled to mass spectrometry after affinity purification from urine and proteolytic digestion. The developed urinary PAP assay was applied on a pooled DRE (digital rectal examination) urine sample from nine individuals. Three glycosylation sites were characterized, namely N94, N220, and N333, via N-glycopeptide analysis. Taking sialic acid linkage isomers into account, a total of 63, 27, and 4 N-glycan structures were identified, respectively. The presented PAP glycoproteomic assay will enable the determination of potential glycomic biomarkers for the early detection and prognosis of PCa in cohort studies.

Relationship Between Baseline Prostate-specific Antigen on Cancer Detection and Prostate Cancer Death: Long-term Follow-up from the European Randomized Study of Screening for Prostate Cancer

BACKGROUND

The European Association of Urology guidelines recommend a risk-based strategy for prostate cancer screening based on the first prostate-specific antigen (PSA) level and age.

OBJECTIVE

To analyze the impact of the first PSA level on prostate cancer (PCa) detection and PCa-specific mortality (PCSM) in a population-based screening trial (repeat screening every 2-4 yr).

DESIGN, SETTING, AND PARTICIPANTS

We evaluated 25589 men aged 55-59 yr, 16898 men aged 60-64 yr, and 12936 men aged 65-69 yr who attended at least one screening visit in the European Randomized Study of Screening for Prostate Cancer (ERSPC) trial (screening arm: repeat PSA testing every 2-4 yr and biopsy in cases with elevated PSA; control arm: no active screening offered) during 16-yr follow-up (FU).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We assessed the actuarial probability for any PCa and for clinically significant (cs)PCa (Gleason ≥7). Cox proportional-hazards regression was performed to assess whether the association between baseline PSA and PCSM was comparable for all age groups. A Lorenz curve was computed to assess the association between baseline PSA and PCSM for men aged 60-61 yr.

RESULTS AND LIMITATIONS

The overall actuarial probability at 16 yr ranged from 12% to 16% for any PCa and from 3.7% to 5.7% for csPCa across the age groups. The actuarial probability of csPCa at 16 yr ranged from 1.2-1.5% for men with PSA <1.0 ng/ml to 13.3-13.8% for men with PSA ≥3.0 ng/ml. The association between baseline PSA and PCSM differed marginally among the three age groups. A Lorenz curve for men aged 60-61 yr showed that 92% of lethal PCa cases occurred among those with PSA above the median (1.21 ng/ml). In addition, for men initially screened at age 60-61 yr with baseline PSA <2 ng/ml, further continuation of screening is unlikely to be beneficial after the age of 68-70 yr if PSA is still <2 ng/ml. No case of PCSM emerged in the subsequent 8 yr (up to age 76-78 yr). A limitation is that these results may not be generalizable to an opportunistic screening setting or to contemporary clinical practice.

CONCLUSIONS

In all age groups, baseline PSA can guide decisions on the repeat screening interval. Baseline PSA of <1.0 ng/ml for men aged 55-69 yr is a strong indicator to delay or stop further screening.

PATIENT SUMMARY

In prostate cancer screening, the patient's baseline PSA (prostate-specific antigen) level can be used to guide decisions on when to repeat screening. The PSA test when used according to current knowledge is valuable in helping to reduce the burden of prostate cancer.

Prostate cancer risk, screening and management in patients with germline BRCA1/2 mutations

Mutations in the BRCA1 and BRCA2 tumour suppressor genes are associated with prostate cancer risk; however, optimal screening protocols for individuals with these mutations have been a subject of debate. Several prospective studies of prostate cancer incidence and screening among BRCA1/2 mutation carriers have indicated at least a twofold to fourfold increase in prostate cancer risk among carriers of BRCA2 mutations compared with the general population. Moreover, BRCA2 mutations are associated with more aggressive, high-grade disease characteristics at diagnosis, more aggressive clinical behaviour and greater prostate cancer-specific mortality. The risk for BRCA1 mutations seems to be attenuated compared with BRCA2. Prostate-specific antigen (PSA) measurement or prostate magnetic resonance imaging (MRI) alone is an imperfect indicator of clinically significant prostate cancer; therefore, BRCA1/2 mutation carriers might benefit from refined risk stratification strategies. However, the long-term impact of prostate cancer screening is unknown, and the optimal management of BRCA1/2 carriers with prostate cancer has not been defined. Whether timely localized therapy can improve overall survival in the screened population is uncertain. Long-term results of prospective studies are awaited to confirm the optimal screening strategies and benefits of prostate cancer screening among BRCA1/2 mutation carriers, and whether these approaches ultimately have a positive impact on survival and quality of life in these patients.

Detection of rare prostate cancer cells in human urine offers prospect of non-invasive diagnosis

Two molecular cytology approaches, (i) time-gated immunoluminescence assay (TGiA) and (ii) Raman-active immunolabeling assay (RiA), have been developed to detect prostate cancer (PCa) cells in urine from five prostate cancer patients. For TGiA, PCa cells stained by a biocompatible europium chelate antibody-conjugated probe were quantitated by automated time-gated microscopy (OSAM). For RiA, PCa cells labeled by antibody-conjugated Raman probe were detected by Raman spectrometer. TGiA and RiA were first optimized by the detection of PCa cultured cells (DU145) spiked into control urine, with TGiA-OSAM showing single-cell PCa detection sensitivity, while RiA had a limit of detection of 4-10 cells/mL. Blinded analysis of each patient urine sample, using MIL-38 antibody specific for PCa cells, was performed using both assays in parallel with control urine. Both assays detected very low abundance PCa cells in patient urine (3-20 PCa cells per mL by TGiA, 4-13 cells/mL by RiA). The normalized mean of the detected PCa cells per 1 ml of urine was plotted against the clinical data including prostate specific antigen (PSA) level and Clinical Risk Assessment for each patient. Both cell detection assays showed correlation with PSA in the high risk patients but aligned with the Clinical Assessment rather than with PSA levels of the low/intermediate risk patients. Despite the limited available urine samples of PCa patients, the data presented in this proof-of-principle work is promising for the development of highly sensitive diagnostic urine tests for PCa.

Development and external validation of a novel nomogram to predict prostate cancer in biopsy-naïve patients with PSA <10 ng/ml and PI-RADS v2.1 = 3 lesions

OBJECTIVE

To develop and externally validate a novel nomogram in biopsy-naïve patients with prostate-specific antigen (PSA) <10 ng/ml and PI-RADS v2.1 = 3 lesions.

METHODS

We retrospectively collected 307 men that underwent initial biopsy from October 2015 to January 2022 in Cohort 1 (The First Affiliated Hospital of Soochow University). External cohort (Cohort 2, Kunshan Hospital) included 109 men that met our criteria from July 2016 to June 2021. By Slicer-3D Software, the volume of all lesions was divided into two subgroups (PI-RADS v2.1 = 3a and 3b). Logistic regression analysis was performed to screen for variables and construct nomogram by analyzing clinical data from Cohort 1. Receiver operating characteristics curve analysis, calibration plot and decision curve analysis (DCA) were plotted to validate the nomogram in external cohort.

RESULTS

A total of 70 (22.8%) patients was diagnosed with prostate cancer in Institution 1. Among them, 34 (11.1%) had clinically significant prostate cancer (csPCa). Age, prostate-specific antigen density, digital rectal examination, PI-RADS v2.1 = 3 subgroups (3a and 3b) and apparent diffusion coefficient (ADC, <750 mm² /s) were predictive factors for prostate cancer (PCa) and csPCa. High area under the curve of the nomogram was found in Cohort 1 and Cohort 2 for PCa (0.857 vs. 0.850) and for csPCa (0.896 vs. 0.893). Calibration curves showed excellent agreement between the predicted probability and actual risk for the models in internal and external validation. The DCA demonstrated net benefit of our nomogram.

CONCLUSION

Until now, this is the first nomogram that predicts PCa and csPCa in biopsy-naïve patients with PSA <10 ng/ml and PI-RADS v2.1 = 3 lesions. Furthermore, PI-RADS v2.1 = 3 subgroups were considered to be an independent risk factor in our model. Our nomogram may assist urologists in biopsy decision making for these so-called "double gray zone" patients.

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

Background

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

Methods

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

Results

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

Conclusions

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

Identification of Urine Biomarkers to Improve Eligibility for Prostate Biopsy and Detect High-Grade Prostate Cancer

Simple Summary

The screening of prostate cancer (PCa), based on the serum prostate specific antigen (PSA), is characterized by a high number of false positives, leading to overdiagnosis of healthy men and overtreatment of indolent PCa. This clinical problem severely affects the quality of life of patients, who would benefit from more specific risk stratification models. By performing a mass spectrometry (MS) screening on urine samples collected prior to prostate biopsy, we identified novel biomarkers and validated them by ELISA. Here, we show that an upfront urine test, based on quantitative biomarkers and patient age, has a higher performance compared to PSA (AUC = 0.6020) and is a feasible method to improve the eligibility criteria for prostate biopsy, to detect healthy men (AUC = 0.8196) and clinically significant PCa, thereby reducing the number of unnecessary prostate biopsies.

Abstract

PCa screening is based on the measurements of the serum prostate specific antigen (PSA) to select men with higher risks for tumors and, thus, eligible for prostate biopsy. However, PSA testing has a low specificity, leading to unnecessary biopsies in 50–75% of cases. Therefore, more specific screening opportunities are needed to reduce the number of biopsies performed on healthy men and patients with indolent tumors. Urine samples from 45 patients with elevated PSA were collected prior to prostate biopsy, a mass spectrometry (MS) screening was performed to identify novel biomarkers and the best candidates were validated by ELISA. The urine quantification of PEDF, HPX, CD99, CANX, FCER2, HRNR, and KRT13 showed superior performance compared to PSA. Additionally, the combination of two biomarkers and patient age resulted in an AUC of 0.8196 (PSA = 0.6020) and 0.7801 (PSA = 0.5690) in detecting healthy men and high-grade PCa, respectively. In this study, we identified and validated novel urine biomarkers for the screening of PCa, showing that an upfront urine test, based on quantitative biomarkers and patient age, is a feasible method to reduce the number of unnecessary prostate biopsies and detect both healthy men and clinically significant PCa.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Simple Summary

Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology.

Abstract

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Definition of Outcome-Based Prostate-Specific Antigen (PSA) Thresholds for Advanced Prostate Cancer Risk Prediction

Simple Summary

In this study, we used a well calibrated risk prediction model to define prostate-specific antigen (PSA) thresholds for identifying or excluding advanced prostate cancer (PCa) as an aid to personalize management of the diagnostic workup. PSA concentrations ≤ 4.1 (<65 years old) and ≤3.7 μg/L (≥65 years old) excluded an advanced PCa in patients without glandular inflammation, while PSA > 5.7 (<65) and >6.1 μg/L (≥65) suggested a biopsy referral. In the presence of glandular inflammation, PSA does not provide a valid estimate for risk of advanced cancer since the marker variability is higher and the pre-test probability of PCa is low in this group. The proposed PSA thresholds may allow an individualized approach to the diagnostic workup, assisting patients in making an informed decision. However, patients with asymptomatic prostatitis cannot benefit from the use of this model since they cannot be pre-biopsy identified.

Abstract

We defined prostate-specific antigen (PSA) thresholds from a well calibrated risk prediction model for identifying and excluding advanced prostate cancer (PCa). We retrieved 902 biopsied patients with a pre-biopsy PSA determination (Roche assay). A logistic regression model predictive for PCa including the main effects [i.e., PSA, age, histological evidence of glandular inflammation (GI)] was built after testing the accuracy by calibration plots and Hosmer-Lemeshow test for goodness of fit. PSA thresholds were derived by assuming a diagnostic sensitivity of 95% (rule-out) and 80% (rule-in) for overall and advanced/poorly differentiated PCa. In patients without GI, serum PSA concentrations ≤ 4.1 (<65 years old) and ≤3.7 μg/L (≥65 years old) excluded an advanced PCa (defined as Gleason score ≥ 7 at biopsy), with a negative predictive value of 95.1% [95% confidence interval (CI): 83.0–98.7] and 88.8% (CI: 80.2–93.9), respectively, while PSA > 5.7 (<65) and >6.1 μg/L (≥65) should address biopsy referral. In presence of GI, PSA did not provide a valid estimate for risk of advanced cancer because of its higher variability and the low pre-test probability of PCa. The proposed PSA thresholds may support biopsy decision except for patients with asymptomatic prostatitis who cannot be pre-biopsy identified.

Prospective Validation of Pentraxin-3 as a Novel Serum Biomarker to Predict the Risk of Prostate Cancer in Patients Scheduled for Prostate Biopsy

PURPOSE

To test and internally validate serum Pentraxin-3 (PTX3) levels as a potential PCa biomarker to predict prostate biopsy (PBx) results.

MATERIALS AND METHODS

Serum PSA and serum PTX3 were prospectively assessed in patients scheduled for PBx at our Institution due to increased serum PSA levels or abnormal digital rectal examination. Uni- and multivariable logistic regression analysis, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA), were used to test the accuracy of serum PTX3 in predicting anyPCa and clinically significant PCa (csPCa) defined as Gleason Grade (GG) ≥ 2.

RESULTS

Among the 455 eligible patients, PCa was detected in 49% and csPCa in 25%. During univariate analysis, PTX3 outperformed other variables in predicting both anyPCa and csPCa. The addition of PTX3 to multivariable models based on standard clinical variables, significantly increased each model's predictive accuracy for anyPCa (AUC from 0.73 to 0.82; p < 0.001) and csPCa (AUC from 0.79 to 0.83; p < 0.001). At DCA, PTX3, and PTX3, density showed higher net benefit than PSA and PSA density and increased the net benefit of multivariable models in deciding when to perform PBx.

CONCLUSIONS

Serum PTX3 levels might be of clinical utility in predicting prostate biopsy results. Should our findings be confirmed, this novel reflex test could be used to reduce the number and burden of unnecessary prostate biopsies.

Role of MRI for the detection of prostate cancer

The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.

Use of the Prostate Health Index and Density in 3 Outpatient Centers to Avoid Unnecessary Prostate Biopsies

OBJECTIVES

We investigated the diagnostic efficacy of the prostate health index (PHI) and PHI density (PHID) to avoid unnecessary prostate biopsies in 3 urological practices.

METHODS

In 122 patients, total prostate-specific antigen (PSA), free PSA (f-PSA), the quotient from total PSA and f-PSA (f-PSA%), and [-2]pro-PSA were measured in the serum; PHI, PHID, and PSA density (PSAD) were calculated prior to prostate biopsy. Tissue sampling via transrectal biopsy was indicated in case of suspicious PSA (progression and/or elevation of PSA) and/or suspicious digital rectal examination. PSAD, PHI, and PHID were not used for biopsy indication. The diagnostic efficacy was determined with receiver-operating characteristic (ROC)and decision curve analyses.

RESULTS

Based on prostate biopsies, 38% (n = 46) of the cases had no prostate carcinoma (PCa), 21% (n = 26) no clinically significant (insignificant) PCa, and 41% (n = 50) had clinically significant PCa. ROC analyses of the PSA parameters showed higher diagnostic efficacy for PHI and PHID (AUC 0.722 and 0.739) than for f-PSA%, PSA, and PSAD (AUC 0.612, 0.595, and 0.698, respectively) regarding carcinoma diagnosis. With a combined use of PHI and PHID (cutoff >40 and >0.9, respectively), only 1 clinically significant PCa would have been missed (sensitivity 98%); in 24 (20%) patients, biopsy could have been avoided.

CONCLUSION

The integration of PHI and PHID could improve the diagnostic efficacy of risk calculators to avoid unnecessary prostate biopsies. However, as a prerequisite, validation of cutoff values in prospective studies is urgently required.

External validation of Cormio nomogram for predicting all prostate cancers and clinically significant prostate cancers

PURPOSE

Recently, the Cormio et al. nomogram has been developed to predict prostate cancer (PCa) and clinically significant PCa using benign prostatic obstruction parameters. The aim of the present study was to externally validate the nomogram in a multicentric cohort.

METHODS

Between 2013 and 2019, patients scheduled for ultrasound-guided prostate biopsy were prospectively enrolled at 11 Italian institutions. Demographic, clinical and histological data were collected and analysed. Discrimination and calibration of Cormio nomogram were assessed with the receiver operator characteristics (ROC) curve and calibration plots. The clinical net benefit of the nomogram was assessed with decision curve analysis. Clinically significant PCa was defined as ISUP grade group > 1.

RESULTS

After accounting for inclusion criteria, 1377 patients were analysed. 816/1377 (59%) had cancer at final pathology (574/816, 70%, clinically significant PCa). Multivariable analysis showed age, prostate volume, DRE and post-voided residual volume as independent predictors of any PCa. Discrimination of the nomogram for cancer was 0.70 on ROC analysis. Calibration of the nomogram was excellent (p = 0.94) and the nomogram presented a net benefit in the 40-80% range of probabilities. Multivariable analysis for predictors of clinically significant PCa found age, PSA, prostate volume and DRE as independent variables. Discrimination of the nomogram was 0.73. Calibration was poor (p = 0.001) and the nomogram presented a net benefit in the 25-75% range of probabilities.

CONCLUSION

We confirmed that the Cormio nomogram can be used to predict the risk of PCa in patients at increased risk. Implementation of the nomogram in clinical practice will better define its role in the patient's counselling before prostate biopsy.

A novel nomogram combined PIRADS v2 and neutrophil-to-lymphocyte ratio to predict the risk of clinically significant prostate cancer in men with PSA < 10 ng/ml at first biopsy

OBJECTIVE

To determine whether Prostate Imaging-Reporting and Data System version 2 (PIRADS v2) and neutrophil-to-lymphocyte ratio(NLR) improve the detection of clinically significant prostate cancer(csCaP) in men with prostate-specific antigen (PSA) <10 ng/ml at first biopsy.

METHODS

Univariable and multivariable binary logistic regression analysis were used to screen for independent risk factors of csCaP. The multivariable model based on the risk factors was to build the nomogram predicting csCaP and assessed by receiver operator characteristic curve analysis, calibration plot, and decision curve analysis.

RESULTS

This retrospective study included 335 men with PSA < 10 ng/ml who underwent initial biopsy. A total of 78 (23.3%) men had csCaP. The nomogram was built based on the multivariable model including age, digital rectal examination, free prostate-specific antigen, PIRADS v2, and NLR. It had high area under the curve of 0.876 and was well calibrated in internal validation. Decision curve analysis also demonstrated that it would improve the prediction of csCaP.

CONCLUSION

PIRADS v2 and NLR improve the detection of csCaP in men with PSA < 10 ng/ml at first biopsy. Due to lack of external validation, relatively small cohort and homogenous population, the study has several limitations. Despite of this, the nomogram based on our study is a promising tool for patients to understand their risk of csCaP and for urologists to make clinical decisions.

Three-dimensional greyscale transrectal ultrasound-guidance and biopsy core preembedding for detection of prostate cancer: Dutch clinical cohort study

Background

To overcome the limitations regarding two dimensional (2D) greyscale (GS) transrectal ultrasound (TRUS)-guided biopsy in prostate cancer (PCa) detection and tissue packaging in biopsy processing, there is an ongoing focus on new imaging and pathology techniques. A three-dimensional (3D) model of the prostate with biopsy needle guidance can be generate by the Navigo™ workstation (UC-care, Israel). The SmartBX™ system (UC-care, Israel) provides a prostate biopsy core preembedding method. The aim of this study was to compare cancer detection rates between the 3D TRUS-guidance and preembedding method with conventional 2D GS TRUS-guidance among patients undergoing prostate biopsies.

Methods

We retrospectively analyzed the records of all patients who underwent prostate biopsies for PCa detection at our institution from 2007 to 2016. The cohort was divided into a 2D GS TRUS-guidance cohort (from 2007 to 2013, n = 1149) and a 3D GS TRUS-guidance with preembedding cohort (from 2013 to 2016, n = 469). Effect of 3D GS TRUS-guidance with preembedding on detection rate of PCa and clinically significant PCa (Gleason score ≥ 7 or > 2 biopsy cores with a Gleason score 6) was compared to 2D GS TRUS-guidance using regression models.

Results

Detection rate of PCa and clinically significant PCa was 39.0 and 24.9% in the 3D GS TRUS cohort compared to 33.5 and 19.0% in the 2D GS TRUS cohort, respectively. On multivariate regression analysis the use of 3D GS TRUS-guidance with preembedding was associated with a significant increase in detection rate of PCa (aOR = 1.33; 95% CI: 1.03–1.72) and clinically significant PCa (aOR = 1.47; 95% CI: 1.09–1.98).

Conclusion

Our results suggest that 3D GS TRUS-guidance with biopsy core preembedding improves PCa and clinically significant PCa detection compared to 2D GS TRUS-guidance. Additional studies are needed to justify the application of these systems in clinical practice.

Electronic supplementary material

The online version of this article (10.1186/s12894-019-0455-7) contains supplementary material, which is available to authorized users.

Label-free diagnostics and cancer surgery Raman spectra guidance for the human colon at different excitation wavelengths

Raman spectroscopy and imaging are highly structure-sensitive methods that allow the characterization of biological samples with minimal impact. In this paper, Raman spectra and imaging of noncancerous and cancerous human colon tissue samples were measured at different excitation wavelengths: 355, 532, and 785 nm. Intra-patient variability in the analyzed spectra showed colon sample heterogeneity for both noncancerous and cancerous human sample types. The lowest inter-patient variability of Raman spectra was observed for the fingerprint region of noncancerous samples for the 532 nm excitation laser line. The bands of principal biochemical constituents (proteins, lipids, nucleic acids) predominate in VIS and NIR-Raman spectra (excitation: 532, 785 nm), with the special role of the bands of intrinsic tissue chromophores—carotenoids for VIS excitation due to resonance enhancement. At 355 nm excitation, high autofluorescence of colon tissues were observed. Our studies proved high potential of Raman spectroscopy and Raman imaging in differentiation of noncancerous and cancerous human colon tissues and that the wavelengths 532 and 785 nm offer wide possibilities for the detection of human colon tissue pathology for ex vivo and in vivo measurements and prevail over 355 nm excitation.

Raman spectroscopy and imaging are highly structure-sensitive methods that allow the characterization of biological samples with minimal impact.

Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics

Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.

Development and Internal Validation of Novel Nomograms Based on Benign Prostatic Obstruction-Related Parameters to Predict the Risk of Prostate Cancer at First Prostate Biopsy

The present study aimed to determine the ability of novel nomograms based onto readily-available clinical parameters, like those related to benign prostatic obstruction (BPO), in predicting the outcome of first prostate biopsy (PBx). To do so, we analyzed our Internal Review Board-approved prospectively-maintained PBx database. Patients with PSA>20 ng/ml were excluded because of their high risk of harboring prostate cancer (PCa). A total of 2577 were found to be eligible for study analyses. The ability of age, PSA, digital rectal examination (DRE), prostate volume (PVol), post-void residual urinary volume (PVR), and peak flow rate (PFR) in predicting PCa and clinically-significant PCa (CSPCa)was tested by univariable and multivariable logistic regression analysis. The predictive accuracy of the multivariate models was assessed using receiver operator characteristic curves analysis, calibration plot, and decision-curve analyses (DCA). Nomograms predicting PCa and CSPCa were built using the coefficients of the logit function. Multivariable logistic regression analysis showed that all variables but PFR significantly predicted PCA and CSPCa. The addition of the BPO-related variables PVol and PVR to a model based on age, PSA and DRE findings increased the model predictive accuracy from 0.664 to 0.768 for PCa and from 0.7365 to 0.8002 for CSPCa. Calibration plot demonstrated excellent models' concordance. DCA demonstrated that the model predicting PCa is of value between ~15 and ~80% threshold probabilities, whereas the one predicting CSPCa is of value between ~10 and ~60% threshold probabilities. In conclusion, our novel nomograms including PVR and PVol significantly increased the accuracy of the model based on age, PSA and DRE in predicting PCa and CSPCa at first PBx. Being based onto parameters commonly assessed in the initial evaluation of men “prostate health,” these novel nomograms could represent a valuable and easy-to-use tool for physicians to help patients to understand their risk of harboring PCa and CSPCa.

Detection of glypican-1 (GPC-1) expression in urine cell sediments in prostate cancer

While measurement of serum prostate specific antigen (PSA) is an important screening tool for prostate cancer, new biomarkers are necessary for better discrimination between presence and absence of disease. The MIL-38 monoclonal antibody is specific for the membrane glycoprotein glypican 1 (GPC-1) and binds to prostate cancer tissue. Urine is known to be a source of cellular material. Thus, we hypothesized that detection of GPC-1 in urine cellular material may identify individuals with prostate cancer. Urine samples from patients with prostate cancer, benign prostatic hyperplasia (BPH), or normal controls were collected and cell sediments prepared. GPC-1-positive cells were detected using a MIL-38 immunofluorescence assay (IFA) and samples were classed positive or negative for GPC-1 expressing cells. Assay sensitivity and specificity, stratified by PSA, was reported. A total of 125 patient samples were analyzed (N = 41 prostate cancer; N = 37 BPH; N = 47 normal controls). The use of MIL-38 to detect GPC-1 by IFA discriminated between prostate cancer and BPH urine specimens with a sensitivity and specificity of 71% and 76%, respectively. Assay specificity increased with increasing PSA, with the highest specificity (89%) for patients with PSA ≥4 ng/ml. At lower PSA (<2 ng/ml) specificity decreased, as evidenced by a greater number of false positives in this concentration range. The odds ratio (OR) and 95% confidence intervals (CIs) for GPC-1-positive cells in patients with prostate cancer, adjusted for PSA, was greatest at the lowest serum PSA (<2 ng/ml; OR = 13.4; 95% CI: 4.0-44.7) compared with no adjustment for PSA (OR = 6.4; 95% CI: 2.8-14.9). The use of MIL-38 for detection of GPC-1 may be a useful tool for detection of prostate cancer.

An In-Depth Glycosylation Assay for Urinary Prostate-Specific Antigen

The concentration of prostate-specific antigen (PSA) in serum is used as an early detection method of prostate cancer (PCa); however, it shows low sensitivity, specificity, and a poor predictive value. Initial studies suggested the glycosylation of PSA to be a promising marker for a more specific yet noninvasive PCa diagnosis. Recent studies on the molecular features of PSA glycosylation (such as antenna modification and core fucosylation) were not successful in demonstrating its potential for an improved PCa diagnosis, probably due to the lack of analytical sensitivity and specificity of the applied assays. In this study, we established for the first time a high-performance PSA Glycomics Assay (PGA), allowing differentiation of α2,6- and α2,3-sialylated isomers, the latter one being suggested to be a hallmark of aggressive types of cancer. After affinity purification from urine and tryptic digestion, PSA samples were analyzed by CE-ESI-MS (capillary electrophoresis-electrospray ionization coupled to mass spectrometry). Based on positive controls, an average interday relative standard deviation of 14% for 41 N-glycopeptides was found. The assay was further verified by analyzing PSA captured from patients' urine samples. A total of 67 N-glycopeptides were identified from the PSA pooled from the patients. In summary, the first PGA successfully established in this study allows an in-depth relative quantitation of PSA glycoforms from urine. The PGA is a promising tool for the determination of potential glycomic biomarkers for the differentiation between aggressive PCa, indolent PCa, and benign prostate hyperplasia in larger cohort studies.

What is an acceptable false negative rate in the detection of prostate cancer?

Background

In prostate cancer (PCa) screening men and their physicians aim to rule out the presence of potentially life threatening PCa. To date, prostate specific antigen (PSA) testing and systematic prostate biopsy (Bx)—in case of an elevated PSA—are still the main modes of PCa detection. Often uncertainty remains when a PSA-test is <3.0 ng/mL or a Bx shows a benign result, leading to the continuous repeating of procedures. Here we assess the potential consequences of false negatives by studying follow-up data of a purely PSA-based approach with applying sextant Bx, an approach considered to have a high risk of missing PCa diagnosis.

Methods

Our study population consisted of 19,970 men from the ERSPC project section Rotterdam, initially screened in 1993–1999. We assessed clinically significant Gleason ≥3+4 PCa (csPCa) diagnosis within the 4-year screening interval and subsequent screening round 4 years later in men having a PSA <3.0 ng/mL at initial screening (no Bx) and men with Bx (PSA >3.0 ng/mL), but no PCa detected at that time. In addition, we addressed PCa mortality and PCa diagnosis for men with a negative PSA test and negative Bx, who were retested every 4 years covering a 15-year follow-up.

Results

A total of 14,935 men had PSA <3.0 ng/mL in the initial screening round, of whom 75 (0.5%) were diagnosed with csPCa at a subsequent screening examination and 2 (<0.1%) in the 4-year screening interval. For 2,260 men with a previously negative Bx at first screening, the figures were 17 (0.8%) and 2 (0.1%) respectively. Indolent PCa (Gleason ≤3+3) was diagnosed in 312 (2%) men with PSA <3.0 ng/mL initially and 115 (5%) men with initial negative Bx. After a 15-year follow-up, 45 (0.3%) PCa deaths occurred in men with initially low PSA, and 29 men (0.2%) had metastasis. For men with negative Bx, 11 (0.5%) PCa deaths occurred and 4 (0.2%) experienced metastasis.

Conclusions

The false negative rates for men with PSA <3.0 ng/mL and negative sextant Bx are extremely low but not negligible. Proper risk stratification before deciding to biopsy is expected to hardly miss any clinical significant PCa diagnosis. This is especially relevant with the increased use of the relatively expensive multi-parametric magnetic resonance imaging (mpMRI) guided targeted Bx procedures.

Characteristics of Prostate Cancer Found at Fifth Screening in the European Randomized Study of Screening for Prostate Cancer Rotterdam: Can We Selectively Detect High-grade Prostate Cancer with Upfront Multivariable Risk Stratification and Magnetic Resonance Imaging?

BACKGROUND

The harm of screening (unnecessary biopsies and overdiagnosis) generally outweighs the benefit of reducing prostate cancer (PCa) mortality in men aged ≥70 yr. Patient selection for biopsy using risk stratification and magnetic resonance imaging (MRI) may improve this benefit-to-harm ratio.

OBJECTIVE

To assess the potential of a risk-based strategy including MRI to selectively identify men aged ≥70 yr with high-grade PCa.

DESIGN, SETTING, AND PARTICIPANTS

Three hundred and thirty-seven men with prostate-specific antigen ≥3.0 ng/ml at a fifth screening (71-75 yr) in the European Randomized study of Screening for Prostate Cancer Rotterdam were biopsied. One hundred and seventy-nine men received six-core transrectal ultrasound biopsy (TRUS-Bx), while 158 men received MRI, 12-core TRUS-Bx, and fusion TBx in case of Prostate Imaging Reporting and Data System ≥3 lesions.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was the overall, low-grade (Gleason Score 3+3) and high-grade (Gleason Score ≥ 3+4) PCa rate. Secondary outcome was the low- and high-grade PCa rate detected by six-core TRUS-Bx, 12-core TRUS-Bx, and MRI ± TBx. Tertiary outcome was the reduction of biopsies and low-grade PCa detection by upfront risk stratification with the Rotterdam Prostate Cancer Risk Calculator 4.

RESULTS AND LIMITATIONS

Fifty-five percent of men were previously biopsied. The overall, low-grade, and high-grade PCa rates in biopsy naïve men were 48%, 27%, and 22%, respectively. In previously biopsied men these PCa rates were 25%, 20%, and 5%. Sextant TRUS-Bx, 12-core TRUS-Bx, and MRI ± TBx had a similar high-grade PCa rate (11%, 12%, and 11%) but a significantly different low-grade PCa rate (17%, 28%, and 7%). Rotterdam Prostate Cancer Risk Calculator 4-based stratification combined with 12-core TRUS-Bx ± MRI-TBx would have avoided 65% of biopsies and 68% of low-grade PCa while detecting an equal percentage of high-grade PCa (83%) compared with a TRUS-Bx all men approach (79%).

CONCLUSIONS

After four repeated screens and ≥1 previous biopsies in half of men, a significant proportion of men aged ≥70 yr still harbor high-grade PCa. Upfront risk stratification and the combination of MRI and TRUS-Bx would have avoided two-thirds of biopsies and low-grade PCa diagnoses in our cohort, while maintaining the high-grade PCa detection of a TRUS-Bx all men approach. Further studies are needed to verify these results.

PATIENT SUMMARY

Prostate cancer screening reduces mortality but is accompanied by unnecessary biopsies and overdiagnosis of nonaggressive tumors, especially in repeatedly screened elderly men. To tackle these drawbacks screening should consist of an upfront risk-assessment followed by magnetic resonance imaging and transrectal ultrasound-guided biopsy.

Risk-based Patient Selection for Magnetic Resonance Imaging-targeted Prostate Biopsy after Negative Transrectal Ultrasound-guided Random Biopsy Avoids Unnecessary Magnetic Resonance Imaging Scans

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) is increasingly used in men with suspicion of prostate cancer (PCa) after negative transrectal ultrasound (TRUS)-guided random biopsy. Risk-based patient selection for mpMRI could help to avoid unnecessary mpMRIs.

OBJECTIVE

To study the rate of potentially avoided mpMRIs after negative TRUS-guided random biopsy by risk-based patient selection using the Rotterdam Prostate Cancer Risk Calculator (RPCRC).

DESIGN, SETTING, AND PARTICIPANTS

One hundred and twenty two consecutive men received a mpMRI scan and subsequent MRI-TRUS fusion targeted biopsy in case of suspicious lesion(s) (Prostate Imaging Reporting and Data System ≥ 3) after negative TRUS-guided random biopsy. Men were retrospectively stratified according to the RPCRC biopsy advice to compare targeted biopsy outcomes after risk-based patient selection with standard (prostate specific antigen and/or digital rectal examination-driven) patient selection.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The rate of potentially avoided mpMRIs by RPCRC-based patient selection in relation to the rate of missed high-grade (Gleason ≥ 3+4) PCa. Receiver operating characteristic curve analysis was performed to determine the area under the curve of the RPCRC for (high-grade) PCa.

RESULTS AND LIMITATIONS

Of the 60 men with a positive biopsy advice, six (10%) had low-grade PCa and 28 (47%) had high-grade PCa in targeted biopsy. Of the 62 men with a negative advice, two (3%) had low-grade PCa and three (5%) had high-grade PCa. Upfront RPCRC-based patient selection would have avoided 62 (51%) of 122 mpMRIs and two (25%) of eight low-grade PCa diagnoses, missing three (10%) of 31 high-grade PCa. The area under the curve of the RPCRC for PCa and high-grade PCa was respectively 0.76 (95% confidence interval 0.67-0.85) and 0.84 (95% confidence interval 0.76-0.93).

CONCLUSIONS

Risk-based patient selection with the RPCRC can avoid half of mpMRIs after a negative prostate specific antigen and/or digital rectal examination-driven TRUS-guided random biopsy. Further improvement in risk-based patient selection for mpMRI could be made by adjusting the RPCRC for MRI-targeted biopsy outcome prediction.

PATIENT SUMMARY

The suspicion of prostate cancer remains in many men after a negative ultrasound-guided prostate biopsy. These men increasingly receive an often unnecessary magnetic resonance imaging (MRI) scan. We found that patient selection for MRI based on the Rotterdam Prostate Cancer Risk Calculator biopsy advice could avoid half of the MRIs.

EN2 in Prostate Cancer

Despite extensive efforts to identify a clinically useful diagnostic biomarker in prostate cancer, no new test has been approved by regulatory authorities. As a result, this unmet need has shifted to biomarkers that additionally indicate presence or absence of "significant" disease. EN2 is a homeodomain-containing transcription factor secreted by prostate cancer into the urine and can be detected by enzyme-linked immunoassay. EN2 may be an ideal biomarker because normal prostate tissue and benign prostatic hypertrophic cells do not secrete EN2. This review discusses the enormous potential of EN2 to address this unmet need and provide the urologist with a simple, inexpensive, and reliable prostate cancer biomarker.

A panel of DNA methylation markers reveals extensive methylation in histologically benign prostate biopsy cores from cancer patients

BACKGROUND

Men with a negative first prostate biopsy will undergo one or more additional biopsies if they remain at high suspicion of prostate cancer. To date, there are no diagnostic tests capable of identifying patients at risk for a positive diagnosis with the predictive power needed to eliminate unnecessary repeat biopsies. Efforts to develop clinical tests using the epigenetic signature of cores recovered from first biopsies have been limited to a few markers and lack the sensitivity and specificity needed for widespread clinical adoption.

METHODS

We developed methylation-specific quantitative polymerase chain reaction assays for a panel of 24 markers that are preferentially methylated in prostate cancer. We modified the bisulfite conversion conditions to allow the integration of the methylation information from multiple markers. We determined the methylation status of the 24 markers in 213 prostate biopsy cores from 104 patients, 37 prostate cancer patients and 67 controls. We performed logistic regression on combinations of markers as well as the entire panel of 24 markers to identify the best candidates for a diagnostic test.

RESULTS

The marker panel differentiated between cancer cores and benign cores from non-cancer patients with 100% sensitivity and 97% specificity. Furthermore, the panel detected significant methylation in benign cores from prostate cancer patients that was not present in controls. Using methylation of 5 out of 24 to define a cancer case, the analysis of a single benign biopsy core identified 62% of prostate cancer patients undergoing repeat biopsies. ROC curve analysis showed that markers commonly methylated in benign cores from cancer patients are the best candidates for a diagnostic test. The results suggest that 5 to 10 markers will be needed to achieve optimal predictive power.

CONCLUSIONS

This study shows that epigenetic field effects differ significantly between cancer patients and controls. Their detection in benign biopsy cores can form the basis of diagnostic tests to identify patients in need of repeat biopsies, reducing the cost of continued PCA screening by up to 40%. They could also be used to identify prostate cancer patients with low grade disease who are likely candidates for active surveillance or focal therapy.