Combined Use of Prostate-specific Antigen Density and Magnetic Resonance Imaging for Prostate Biopsy Decision Planning: A Retrospective Multi-institutional Study Using the Prostate Magnetic Resonance Imaging Outcome Database (PROMOD)

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems.

RESULTS

Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification.

SIGNIFICANCE

The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Risk-Adapted Strategy Combining Magnetic Resonance Imaging and Prostate-Specific Antigen Density to Individualize Biopsy Decision in Patients With PI-RADS 3 ``Gray Zone'' Lesions

INTRODUCTION

Recent guidelines suggest that biopsy may be omitted in some groups of patients with PI-RADS 3 lesions on mpMRI. In this study, we aimed to evaluate biopsy strategies involving prostate-specific antigen density (PSAd) to avoid unnecessary biopsy versus the risk of missing clinically significant prostate cancer (csPCa) in patients with PI-RADS 3 lesions.

MATERIAL AND METHODS

Data of 616 consecutive patients who underwent PSAd and mpMRI before prostate biopsy between January 2017 and January 2022 at a single center were retrospectively assessed. All of these patients underwent combined cognitive or fusion targeted biopsy of suspicious lesions and transrectal ultrasonography guided systematic biopsy. PI-RADS 3 based strategies with PSAd and mpMRI combination were created. For each strategy, avoided unnecessary biopsy, reduced ISUP Grade 1, and missed ISUP Grade ≥ 2 ratios were determined. Decision curve analysis (DCA) was used to statistically compare the net benefit of each strategy.

RESULTS

DCA revealed that patients who had PI-RADS 3 lesions with PSAd ≥ 0.2, and/or patients who had PI-RADS 4 and 5 lesions had the most benefit, under the threshold probability level between 10% and 50%, which avoided 48.2% unnecessary prostate biopsies and reduced 51% of ISUP grade 1 cases, while missed 17.5% of ISUP grade ≥ 2 cases. (22.1% for ISUP grade 2 and 8.8% for ISUP grade ≥ 3). Strategy 1 (PI-RADS 4-5 and/or PSAd ≥ 0.2), 3 (PI-RADS 4-5 and/or PI-RADS 3 if PSAd ≥ 0.15), and 7 (PI-RADS 4-5 and/or PI-RADS 3 if PSAd ≥ 0.15 and/or PI-RADS 2 if PSAd ≥ 0.2) were the next three best strategies.

CONCLUSION

mpMRI combined with PSAd strategies reduced biopsy attempts in PI-RADS 3 lesions. Using these strategies, the advantage of avoiding biopsy and the risk of missing the diagnosis of csPCa can be discussed with the patient, and the biopsy decision can be made afterwards.

Avoiding unnecessary biopsy: the combination of PRIMARY score with prostate-specific antigen density for prostate biopsy decision

BACKGROUND

Avoiding unnecessary biopsies for men with suspected prostate cancer remains a clinical priority. The recently proposed PRIMARY score improves diagnostic accuracy in detecting clinically significant prostate cancer (csPCa). The aim of this study was to determine the best strategy combining PRIMARY score or MRI reporting scores (Prostate Imaging Reporting and Data System [PI-RADS]) with prostate-specific antigen density (PSAD) for prostate biopsy decision making.

METHODS

A retrospective analysis of 343 patients who underwent both 68Ga-PSMA PET/CT and MRI before prostate biopsy was performed. PSA was restricted to <20 ng/ml. Different biopsy strategies were developed and compared based on PRIMARY score or PI-RADS with PSAD thresholds. Decision curve analysis (DCA) was plotted to define the optimal biopsy strategy.

RESULTS

The prevalence of csPCa was 41.1% (141/343). According to DCA, the strategies of PRIMARY score +PSAD (strategy #1, strategy #2, strategy #6) had a higher net benefit than the strategies of PI-RADS + PSAD at the risk threshold of 8-20%. The best diagnostic strategy was strategy #1 (PRIMARY score 4-5 or PSAD ≥ 0.20), which avoided 38.2% biopsy procedures while missed 9.2% of csPCa cases. From a clinical perspective, strategies with a lower risk of missing csPCa were strategy #2 (PRIMARY score ≥4 or PSAD ≥ 0.15), which avoided 28.6% biopsies while missed 5.7% of csPCa cases, or strategy #6 (PRIMARY score≥3 or PSAD ≥ 0.15), which avoided 20.7% biopsies while missed only 3.5% of csPCa cases. The limitations of the study were the retrospective single-center nature.

CONCLUSIONS

The combination of PRIMARY score +PSAD allows individualized decisions to avoid unnecessary biopsy, outperforming the strategies of PI-RADS + PSAD. Further prospective trials are needed to validate these findings.

Prediction of false-positive PI-RADS 5 lesions on prostate multiparametric MRI: development and internal validation of a clinical-radiological characteristics based nomogram

BACKGROUND

To develop a risk model including clinical and radiological characteristics to predict false-positive The Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions.

METHODS

Data of 612 biopsy-naïve patients who had undergone multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy were collected. Clinical variables and radiological variables on mpMRI were adopted. Lesions were divided into the training and validation cohort randomly. Stepwise multivariate logistic regression analysis with backward elimination was performed to screen out variables with significant difference. A diagnostic nomogram was developed in the training cohort and further validated in the validation cohort. Calibration curve and receiver operating characteristic (ROC) analysis were also performed.

RESULTS

296 PI-RADS 5 lesions in 294 patients were randomly divided into the training and validation cohort (208 : 88). 132 and 56 lesions were confirmed to be clinically significant prostate cancer in the training and validation cohort respectively. The diagnostic nomogram was developed based on prostate specific antigen density, the maximum diameter of lesion, zonality of lesion, apparent diffusion coefficient minimum value and apparent diffusion coefficient minimum value ratio. The C-index of the model was 0.821 in the training cohort and 0.871 in the validation cohort. The calibration curve showed good agreement between the estimation and observation in the two cohorts. When the optimal cutoff values of ROC were 0.288 in the validation cohort, the sensitivity, specificity, PPV, and NPV were 90.6%, 67.9%, 61.7%, and 92.7% in the validation cohort, potentially avoiding 9.7% unnecessary prostate biopsies.

CONCLUSIONS

We developed and validated a diagnostic nomogram by including 5 factors. False positive PI-RADS 5 lesions could be distinguished from clinically significant ones, thus avoiding unnecessary prostate biopsy.

Diagnostic Performance of Prostate-specific Antigen Density for Detecting Clinically Significant Prostate Cancer in the Era of Magnetic Resonance Imaging: A Systematic Review and Meta-analysis

CONTEXT

There has been a dramatic increase in the use of prostate magnetic resonance imaging (MRI) in the diagnostic workup. With prostate volume calculated from MRI, prostate-specific antigen density (PSAD) now is a ready-to-use parameter for prostate cancer (PCa) risk stratification before prostate biopsy, especially among patients with negative MRI or equivocal lesions.

OBJECTIVE

In this review, we aimed to evaluate the diagnostic performance of PSAD for clinically significant prostate cancer (CSPCa) among patients who received MRI before prostate biopsy.

EVIDENCE ACQUISITION

Two investigators performed a systematic review according of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria. Studies (published between January 1, 2012, and December 31, 2021) reporting the diagnostic performance (outcomes) of PSAD (intervention) for CSPCa among men who received prebiopsy prostate MRI and subsequent prostate biopsy (patients), using biopsy pathology as the gold standard (comparison), were eligible for inclusion.

EVIDENCE SYNTHESIS

A total of 1536 papers were identified in PubMed, Scopus, and Embase. Of these, 248 studies were reviewed in detail and 39 were qualified. The pooled sensitivity (SENS) and specificity (SPEC) for diagnosing CSPCa among patients with positive MRI were, respectively, 0.87 and 0.35 for PSAD of 0.1 ng/ml/ml, 0.74 and 0.61 for PSAD of 0.15 ng/ml/ml, and 0.51 and 0.81 for PSAD of 0.2 ng/ml/ml. The pooled SENS and SPEC for diagnosing CSPCa among patients with negative MRI were, respectively, 0.85 and 0.36 for PSAD of 0.1 ng/ml/ml, 0.60 and 0.66 for PSAD of 0.15 ng/ml/ml, and 0.33 and 0.84 for PSAD of 0.2 ng/ml/ml. The pooled SENS and SPEC among patients with Prostate Imaging Reporting and Data System (PI-RADS) 3 or Likert 3 lesions were, respectively, 0.87 and 0.39 for PSAD of 0.1 ng/ml/ml, 0.61 and 0.69 for PSAD of 0.15 ng/ml/ml, and 0.42 and 0.82 for PSAD of 0.2 ng/ml/ml. The post-test probability for CSPCa among patients with negative MRI was 6% if PSAD was <0.15 ng/ml/ml and dropped to 4% if PSAD was <0.10 ng/ml/ml.

CONCLUSIONS

In this systematic review, we quantitatively evaluated the diagnosis performance of PSAD for CSPCa in combination with prostate MRI. It demonstrated a complementary performance and predictive value, especially among patients with negative MRI and PI-RADS 3 or Likert 3 lesions. Integration of PSAD into decision-making for prostate biopsy may facilitate improved risk-adjusted care.

PATIENT SUMMARY

Prostate-specific antigen density is a ready-to-use parameter in the era of increased magnetic resonance imaging (MRI) use in clinically significant prostate cancer (CSPCa) diagnosis. Findings suggest that the chance of having CSPCa was very low (4% or 6% for those with negative prebiopsy MRI or Prostate Imaging Reporting and Data System (Likert) score 3 lesion, respectively, if the PSAD was <0.10 ng/ml/ml), which may lower the need for biopsy in these patients.

Variability of mpMRI diagnostic performance according to the upfront individual patient risk of having clinically significant prostate cancer

BACKGROUND

To assess the variation of multiparametric magnetic resonance imaging (mpMRI) positive predictive value (PPV) according to each patient's risk of clinically significant prostate cancer (csPCa) based exclusively on clinical factors.

METHODS

We evaluated 999 patients with positive mpMRI (PI-RADS ≥ 3) receiving targeted (TBx) plus systematic prostate biopsy. We built a multivariable logistic regression analysis (MVA) using clinical risk factors to calculate the individual patients' risk of harboring csPCa at TBx. A second MVA tested the association between individual patients' clinical risk and mpMRI PPV accounting for the PI-RADS score. Finally, we plotted the PPV of each PI-RADS score by the individual patient pretest probability of csPCa using a LOWESS approach.

RESULTS

Overall, TBx found csPCa in 21%, 51%, and 80% of patients with PI-RADS 3, 4, and 5 lesions, respectively. At MVA, age, PSA, digital rectal examination (DRE), and prostate volume were significantly associated with the risk of csPCa at biopsy. DRE yielded the highest odds ratio (OR: 2.88; p < 0.001). The individual patient's clinical risk was significantly associated with mpMRI PPV (OR: 2.49; p < 0.001) using MVA. Plotting the mpMRI PPV according to the predicted clinical risks, we observed that for patients with clinical risk close to 0 versus patients with risk higher than 90%, the mpMRI PPV of PI-RADS 3, 4, and 5 ranged from 0% to 75%, from 0% to 96%, and from 45% to 100%, respectively.

CONCLUSION

mpMRI PPV varies according to the individual pretest patient's risk based on clinical factors. These findings should be considered in the decision-making process for patients with suspect MRI findings referred for a prostate biopsy. Moreover, our data support the need for further studies to create an individualized risk prediction tool.

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies

OBJECTIVES

To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS).

METHODS

This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics.

RESULTS

A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001).

CONCLUSION

Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers.

CLINICAL RELEVANCE STATEMENT

Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers.

KEY POINTS

• Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

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

Importance

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

Objective

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

Data Sources

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

Study Selection

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

Data Extraction and Synthesis

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

Main Outcomes and Measures

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

Results

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

Conclusions and Relevance

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

Development and internal validation of a nomogram predicting significant prostate cancer: Is it clinically applicable in low prevalent prostate cancer countries? A multicenter study

BACKGROUND

Accurately identifying aggressive prostate tumors and studying them as a separate outcome are urgently needed. Nomogram is a predictive tool using an algorithm, it has been widely applied in clinical practice to predict prognosis. We aimed to develop and internally validate a nomogram predicting clinically significant prostate cancer (csPCa).

METHODS

Data were retrieved from the records of the two main hospitals in Riyadh, during the period 2019-2022. Significant variables associated with csPCa cases were used to develop and internally validate a novel nomogram, utilizing the C index, and calibration curves. Decision curve analysis (DCA) was used to assess its clinical utility.

RESULTS

Prostate imaging reporting and data system (PI-RADS), smaller prostate volume, and prostate-specific antigen (PSA) > 10 ng/mL were significantly associated with the risk csPCa, respectively. The model developed by the nomogram showed an excellent accuracy for csPCa discrimination, as indicated by area under the curve (0.83), and calibration curves. DCA showed that our model was superior and surpassed all other models with a larger net benefit for various threshold probabilities. Based on our model, at a probability threshold of 30%, biopsying patients is the equivalent of a strategy that led to an absolute 5% reduction in the number of biopsies without missing any csPCa.

CONCLUSION

The developed nomogram consisting of PI-RAD, total PSA, and prostate volume showed a robust predictive capacity for csPCa before prostate biopsy that may be valuable for clinical judgment to prevent needless biopsy. Yet, the small percentage (5%) of yielded unnecessary biopsies that could be saved by using such a model, cast an important question on its merit and clinical applicability.

Clinical and Radiological Factors for Predicting Clinically Significant Prostate Cancer in Biopsy-Naive Patients With PI-RADS 3 Lesions

OBJECTIVE

This study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions.

METHODS

This was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram.

RESULTS

Clinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ2 = 10.254, P = .248).

CONCLUSION

The combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.

[Early detection of prostate cancer: Towards a new paradigm?]

Prostate cancer (PCa) is a public health issue. The diagnostic strategy for PCa is well codified and assessed by digital rectal examination, PSA testing and multiparametric MRI, which may or may not lead to prostate biopsies. The formal benefit of organized PCa screening, studied more than 10 years ago at an international scale and for all incomers, is not demonstrated. However, diagnostic and therapeutic modalities have evolved since the pivotal studies. The contribution of MRI and targeted biopsies, the widespread use of active surveillance for unsignificant PCa, the improvement of surgical techniques and radiotherapy… have allowed a better selection of patients and strengthened the interest for an individualized approach, reducing the risk of overtreatment. Aiming to enhance coverage and access to screening for the population, the European Commission recently promoted the evaluation of an organized PCa screening strategy, including MRI. The lack of screening programs has become detrimental to the population and must shift towards an early detection policy adapted to the risk of each individual.

Development and validation of a novel nomogram predicting clinically significant prostate cancer in biopsy-naive men based on multi-institutional analysis

BACKGROUND

Prediction of clinically significant prostate cancer (csPCa) is essential to select biopsy-naive patients for prostate biopsy. This study was to develop and validate a nomogram based on clinicodemographic parameters and exclude csPCa using prostate-specific antigen density (PSAD) stratification.

METHODS

Independent predictors were determined via univariate and multivariate logistic analysis and adopted for developing a predictive nomogram, which was assessed in terms of discrimination, calibration, and net benefit. Different PSAD thresholds were used for deciding immediate biopsies in patients with Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions.

RESULTS

A total of 932 consecutive patients who underwent ultrasound-guided transperineal cognitive biopsy were enrolled in our study. In the development cohort, age (odds ratio [OR], 1.075; 95% confidence interval [CI], 1.036-1.114), PSAD (OR, 6.003; 95% CI, 2.826-12.751), and PI-RADS (OR, 3.419; 95% CI, 2.453-4.766) were significant predictors for csPCa. On internal and external validation, this nomogram showed high areas under the curve of 0.943, 0.922, and 0.897, and low Brier scores of 0.092, 0.102, and 0.133 and insignificant unreliability tests of 0.713, 0.490, and 0.859, respectively. Decision curve analysis revealed this model could markedly improve clinical net benefit. The probability of excluding csPCa was 98.51% in patients with PI-RADS 3 lesions and PSAD <0.2 ng/ml2 .

CONCLUSION

This novel nomogram including age, PSAD, and PI-RADS could be applied to accurately predict csPCa, and 44.08% of patients with equivocal imaging findings plus PSAD <0.2 ng/ml2 could safely forgo biopsy.

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors

PURPOSE

The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade ≥ 2).

METHODS

This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients.

RESULTS

705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naïve (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG ≥ 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS ≥ 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively.

CONCLUSIONS

Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions.

TRIAL REGISTRATION

The present study was registered at ClinicalTrials.gov number: NCT05078359.

Added Value of Prostate-specific Antigen Density in Selecting Prostate Biopsy Candidates Among Men with Elevated Prostate-specific Antigen and PI-RADS ≥3 Lesions on Multiparametric Magnetic Resonance Imaging of the Prostate: A Systematic Assessment by PI-RADS Score

BACKGROUND

A significant proportion of patients with positive multiparametric magnetic resonance imaging (mpMRI; Prostate Imaging-Reporting and Data System [PI-RADS] scores of 3-5) have negative biopsy results.

OBJECTIVE

To systematically assess all prostate-specific antigen density (PSAD) values and identify an appropriate cutoff for identification of patients with positive mpMRI who could potentially avoid biopsy on the basis of their PI-RADS score.

DESIGN, SETTING, AND PARTICIPANTS

The study included a cohort of 1341 patients with positive mpMRI who underwent combined targeted and systematic biopsies.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Multivariable logistic regression analysis (MVA) was used to assess the association between PSAD and the risk of clinically significant prostate cancer (csPCa, grade group ≥2) after adjusting for confounders. We used locally weighted scatterplot smoothing to explore csPCa risk according to PSAD and PI-RADS scores. PSAD utility was observed only for patients with PI-RADS 3 lesions, so we plotted the effect of each PSAD value as a cutoff for this subgroup in terms of biopsies saved, csPCa cases missed, and clinically insignificant PCa (ciPCa, grade group 1) cases not detected.

RESULTS AND LIMITATIONS

Overall, 667 (50%) csPCa cases were identified. On MVA, PSAD independently predicted csPCa (odds ratio 1.57; p < 0.001). For PI-RADS ≥4 lesions, the csPCa risk was ≥40% regardless of PSAD. Conversely, among patients with PI-RADS 3 lesions, csPCa risk ranged from 0% to 60% according to PSAD values, and a PSAD cutoff of 0.10 ng/ml/cm3 corresponded to a threshold probability of 10% for csPCa. Using this PSAD cutoff for patients with PI-RADS 3 lesions would have saved 32% of biopsies, missed 7% of csPCa cases, and avoided detection of 34% of ciPCa cases. Limitations include selection bias and the high experience of the radiologists and urologists involved.

CONCLUSIONS

Patients with PI-RADS ≥4 lesions should undergo prostate biopsy regardless of their PSAD, while PSAD should be used to stratify patients with PI-RADS 3 lesions. Using a threshold probability of 10% for csPCa, our data suggest that the appropriate strategy is to avoid biopsy in patients with PI-RADS 3 lesions and PSAD <0.10 ng/ml/cm3. Our results also provide information to help in tailoring an appropriate strategy for every patient with positive mpMRI findings.

PATIENT SUMMARY

We investigated whether a cutoff value for PSAD (prostate-specific antigen density) could identify patients with suspicious prostate lesions on MRI (magnetic resonance imaging) who could avoid biopsy according to the PI-RADS score for their scan. We found that patients with PI-RADS ≥4 should undergo prostate biopsy regardless of their PSAD. A PSAD cutoff of 0.10 should be used to stratify patients with PI-RADS 3.

Optimizing detection of clinically significant prostate cancer through nomograms incorporating mri, clinical features, and advanced serum biomarkers in biopsy naïve men

PURPOSE

To develop nomograms that predict the detection of clinically significant prostate cancer (csPCa, defined as ≥GG2 [Grade Group 2]) at diagnostic biopsy based on multiparametric prostate MRI (mpMRI), serum biomarkers, and patient clinicodemographic features.

MATERIALS AND METHODS

Nomograms were developed from a cohort of biopsy-naïve men presenting to our 11-hospital system with prostate specific antigen (PSA) of 2-20 ng/mL who underwent pre-biopsy mpMRI from March 2018-June 2021 (n = 1494). The outcomes were the presence of csPCa and high-grade prostate cancer (defined as ≥GG3 prostate cancer). Using significant variables on multivariable logistic regression, individual nomograms were developed for men with total PSA, % free PSA, or prostate health index (PHI) when available. The nomograms were both internally validated and evaluated in an independent cohort of 366 men presenting to our hospital system from July 2021-February 2022.

RESULTS

1031 of 1494 men (69%) underwent biopsy after initial evaluation with mpMRI, 493 (47.8%) of whom were found to have ≥GG2 PCa, and 271 (26.3%) were found to have ≥GG3 PCa. Age, race, highest PIRADS score, prostate health index when available, % free PSA when available, and PSA density were significant predictors of ≥GG2 and ≥GG3 PCa on multivariable analysis and were used for nomogram generation. Accuracy of nomograms in both the training cohort and independent cohort were high, with areas under the curves (AUC) of ≥0.885 in the training cohort and ≥0.896 in the independent validation cohort. In our independent validation cohort, our model for ≥GG2 prostate cancer with PHI saved 39.1% of biopsies (143/366) while only missing 0.8% of csPCa (1/124) with a biopsy threshold of 20% probability of csPCa.

CONCLUSIONS

Here we developed nomograms combining serum testing and mpMRI to help clinicians risk stratify patients with elevated PSA of 2-20 ng/mL who are being considered for biopsy. Our nomograms are available at https://rossnm1.shinyapps.io/MynMRIskCalculator/ to aid with biopsy decisions.

Optimal PSA density threshold and predictive factors for the detection of clinically significant prostate cancer in patient with a PI-RADS 3 lesion on MRI

INTRODUCTION

While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions usually justify prostate biopsy (PBx), the management of a PI-RADS 3 lesion can be discussed. The aim of our study was to determine the optimal prostate-specific antigen density (PSAD) threshold and predictive factors of clinically significant prostate cancer (csPCa) in patients with a PI-RADS 3 lesion on MRI.

PATIENTS AND METHODS

Using our prospectively maintained database, we conducted a monocentric retrospective study, including all patients with a clinical suspicious of prostate cancer (PCa), all of them had a PI-RADS 3 lesion on the mpMRI prior to PBx. Patients under active surveillance or displaying suspicious digital rectal examination were excluded. Clinically significant (csPCa) was defined as PCa with any ISUP grade group ≥ 2 (Gleason ≥ 3 + 4).

RESULTS

We included 158 patients. The detection rate of csPCa was 22.2%. In case of PSAD ≤ 0.15 ng/ml/cm³, PBx would be omitted in 71.5% (113/158) of men at the cost of missing 15.0% (17/113) of csPCa. With a threshold of 0.15 ng/ml/cm³, the sensitivity and the specificity were 0.51 and 0.78 respectively. The positive predictive value was 0.40 and the negative predictive value was 0.85. According to multivariate analysis, age (OR = 1.10, CI95% 1.03-1.19, P = 0.007), and PSAD ≥ 0.15 ng/ml/cm³ (OR = 3.59, CI95% 1.41-9.47, P = 0.008) were independent predictive factors of csPCa. Previous negative PBx was negatively associated with csPCa (OR = 0.24, CI 95% 0.07-0.66, P = 0.01).

CONCLUSION

Our result suggests that the optimal PSAD threshold was 0.15 ng/ml/cm³. However, in this case omitting PBx in 71.5% of cases would be at the cost of missing 15.0% of csPCa. PSAD should not be used alone, other predictive factors as age and PBx history should also be considered in the discussion with the patient, to avoid PBx while missing few csPCa.

Developing a predictive model for clinically significant prostate cancer by combining age, PSA density, and mpMRI

PURPOSE

The study aimed to construct a predictive model for clinically significant prostate cancer (csPCa) and investigate its clinical efficacy to reduce unnecessary prostate biopsies.

METHODS

A total of 847 patients from institute 1 were included in cohort 1 for model development. Cohort 2 included a total of 208 patients from institute 2 for external validation of the model. The data obtained were used for retrospective analysis. The results of magnetic resonance imaging were obtained using Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). Univariate and multivariate analyses were performed to determine significant predictors of csPCa. The diagnostic performances were compared using the receiver operating characteristic (ROC) curve and decision curve analyses.

RESULTS

Age, prostate-specific antigen density (PSAD), and PI-RADS v2.1 scores were used as predictors of the model. In the development cohort, the areas under the ROC curve (AUC) for csPCa about age, PSAD, PI-RADS v2.1 scores, and the model were 0.675, 0.823, 0.875, and 0.938, respectively. In the external validation cohort, the AUC values predicted by the four were 0.619, 0.811, 0.863, and 0.914, respectively. Decision curve analysis revealed that the clear net benefit of the model was higher than PI-RADS v2.1 scores and PSAD. The model significantly reduced unnecessary prostate biopsies within the risk threshold of > 10%.

CONCLUSIONS

In both internal and external validation, the model constructed by combining age, PSAD, and PI-RADS v2.1 scores exhibited excellent clinical efficacy and can be utilized to reduce unnecessary prostate biopsies.

Prostate-specific Antigen Density Cutoff of 0.15 ng/ml/cc to Propose Prostate Biopsies to Patients with Negative Magnetic Resonance Imaging: Efficient Threshold or Legacy of the Past?

BACKGROUND

A prostate-specific antigen density (PSAd) cutoff of 0.15 ng/ml/cc is a commonly recommended threshold to identify patients with negative prostate magnetic resonance imaging (MRI) who should proceed to a prostate biopsy. We were unable to find any study that explicitly examined the properties of this threshold compared with others.

OBJECTIVE

To investigate whether the 0.15 cutoff is justified for selecting patients at risk of harboring high-grade cancer (Gleason score ≥3 + 4) despite negative MRI.

DESIGN, SETTING, AND PARTICIPANTS

A cohort of 8974 prostate biopsies provided by the Prostate Biopsy Collaborative Group (PBCG) was included in the study.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Locally weighted scatterplot smoothing was used to investigate whether there was a change in the risk of high-grade cancer around this value. We examined whether the use of this cutoff in patients with negative MRI corresponds to a reasonable threshold probability for a biopsy (defined as a 10% risk of high-grade disease). To do so, we applied the negative likelihood ratio of MRI, calculated from eight studies on prostate MRI, to the risk curve derived from the PBCG.

RESULTS AND LIMITATIONS

There was no discontinuity in the risk of high-grade prostate cancer at a PSAd cutoff of 0.15. This cutoff corresponded to a probability of high-grade disease ranging from 2.6% to 10%, depending on MRI accuracy. Using 10% as threshold probability, the corresponding PSAd cutoff varied between 0.15 and 0.38, with the threshold increasing for greater MRI accuracy. Possible limitations include difference between studies on MRI and the use of ultrasound to measure prostate volume.

CONCLUSIONS

The 0.15 cutoff to recommend prostate biopsies in patients with negative MRI is justified only under an extreme scenario of poor MRI properties. We recommend a value of at least ≥0.20. Our results suggest the need for future studies to look at how to best identify patients who need prostate biopsies despite negative MRI, likely by using individualized risk prediction.

PATIENT SUMMARY

In this study, we investigated whether the commonly used prostate-specific antigen density cutoff of 0.15 is justified to identify patients with negative magnetic resonance imaging (MRI) who should proceed to a prostate biopsy. We found that this cutoff is appropriate only in case of very poor MRI quality, and a higher cutoff (≥0.20) should be used for the average MRI.

A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators

OBJECTIVES

To develop and validate a prostate cancer (PCa) risk calculator (RC) incorporating multiparametric magnetic resonance imaging (mpMRI) and to compare its performance with that of the Prostate Biopsy Collaborative Group (PBCG) RC.

PATIENTS AND METHODS

Men without a PCa diagnosis receiving mpMRI before biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015-2020) were included. Data from a separate institution were used for external validation. The primary outcome was diagnosis of no cancer, grade group (GG)1 PCa, and clinically significant (cs)PCa (≥GG2). Binary logistic regression was used to explore standard clinical and mpMRI variables (prostate volume, Prostate Imaging-Reporting Data System [PI-RADS] version 2.0 lesions) with the final PLUM RC, based on a multinomial logistic regression model. Receiver-operating characteristic curve, calibration curves, and decision-curve analysis were evaluated in the training and validation cohorts.

RESULTS

A total of 1010 patients were included for development (N = 674 training [47.8% PCa, 30.9% csPCa], N = 336 internal validation) and 371 for external validation. The PLUM RC outperformed the PBCG RC in the training (area under the curve [AUC] 85.9% vs 66.0%; P < 0.001), internal validation (AUC 88.2% vs 67.8%; P < 0.001) and external validation (AUC 83.9% vs 69.4%; P < 0.001) cohorts for csPCa detection. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a threshold probability of 15%, the PLUM RC vs the PBCG RC could avoid 13.8 vs 2.7 biopsies per 100 patients without missing any csPCa. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for the PBCG RC. The PLUM RC compared favourably with the Stanford Prostate Cancer Calculator (SPCC; AUC 84.1% vs 81.1%; P = 0.002) and the MRI-European Randomized Study of Screening for Prostate Cancer (ERSPC) RC (AUC 84.5% vs 82.6%; P = 0.05).

CONCLUSIONS

The mpMRI-based PLUM RC significantly outperformed the PBCG RC and compared favourably with other mpMRI-based RCs. A large proportion of biopsies could be avoided using the PLUM RC in shared decision making while maintaining optimal detection of csPCa.

Natural History of Patients with Prostate MRI Likert 1-3 and Development of RosCaP: a Multivariate Risk Score for Clinically Significant Cancer

INTRODUCTION

Clinically significant prostate cancer (csCaP) with Gleason ≥3 + 4 is found in 10% negative prebiopsy multiparametric (mp) MRI cases and varies widely for equivocal mpMRI cases. The objective of this study was to investigate long-term outcomes of patients with negative and equivocal mpMRIs and to develop a predictive score for csCaP risk stratification in this group.

PATIENTS AND METHODS

Patients who underwent an upfront mpMRI between May 2015 and March 2018 with an MRI score Likert 1 to 3 were included in the study. Patients had either a CaP diagnosis at MRI-targeted biopsy or were not diagnosed and attended follow-up in the community. Outcomes were analysed through the Kaplan-Meier estimator and Cox Model. Regression coefficients of significant variables were used to develop a Risk of significant Cancer of the Prostate score (RosCaP).

RESULTS

At first assessment 281/469 patients had mpMRI only and 188/469 mpMRI and biopsy, 26 csCaP were found at biopsy, including 10/26 in Likert 3 patients. 12/371 patients discharged without CaP after first assessment were diagnosed with csCaP during a median of 34.2 months' follow-up, 11/12 diagnosis occurred in patients omitting initial biopsy. csCaP diagnosis-free survival was 95.7% in the MRI group and 99.1% in the biopsy group. From these outcomes, a continuous RosCaP score was developed: RosCaP = 0.083 x Age - 0.202 x (1/PSA Density) + 0.786 (if Likert 3), and 4 risk classes were proposed. Limitations include retrospective design and absence of external validation.

CONCLUSION

Age, PSA Density and MRI Likert score were significantly associated to the risk of csCaP and utilised to devise the novel RosCap predictive score focused to support risk assessment in patients with negative or equivocal mpMRI results.

When to order genomic tests: development and external validation of a model to predict high-risk prostate cancer at the genotypic level

PURPOSE

The aim of this study was to develop a model to predict high-genomic-risk prostate cancer (PCa) according to Decipher score, a validated 22 gene prognostic panel. By doing so, one might select the individuals who are likely to benefit from genomic testing and improve pre-op counseling about the need for adjuvant treatments.

METHODS

We retrospectively reviewed IRB-approved databases at two institutions. All patients had preoperative magnetic resonance imaging (MRI) and Decipher prostate radical prostatectomy (RP), a validated 22 gene prognostic panel. We used binary logistic regression to estimate high-risk Decipher (Decipher score > 0.60) probability on RP specimen. Area under the curve (AUC) and calibration were used to assess the accuracy of the model in the development and validation cohort. Decision curve analysis (DCA) was performed to assess the clinical benefit of the model.

RESULTS

The development and validation cohort included 622 and 185 patients with 283 (35%) and 80 (43%) of those with high-risk Decipher. The multivariable model included PSA density, biopsy Gleason Grade Group, percentage of positive cores and MRI extracapsular extension. AUC was 0.73 after leave-one-out cross-validation. DCA showed a clinical benefit in a range of probabilities between 15 and 60%. In the external validation cohort, AUC was 0.70 and calibration showed that the model underestimates the actual probability of the outcome.

CONCLUSIONS

The proposed model to predict high-risk Decipher score at RP is helpful to improve risk stratification of patients with PCa and to assess the need for additional testing and treatments.

MRI vs Transrectal Ultrasound to Estimate Prostate Volume and PSAD: Impact on Prostate Cancer Detection

OBJECTIVES

To compare multiparametric magnetic resonance imaging (mpMRI) and transrectal ultrasound (TRUS) to estimate prostate volume and prostate specific antigen density (PSAD) as well as subsequent impact on prostate cancer (PCa) detection.

METHODS

Patients referred for mpMRI prior to mpMRI-TRUS fusion-guided prostate biopsy between 2015 and 2020 were identified. Volume and calculated PSAD by mpMRI and TRUS were compared. Associations with presence of any PCa and clinically significant PCa (csPCa; Gleason ≥3 + 4) were evaluated using linear regression (interaction by volume quartile), logistic regression, and receiver operating characteristics.

RESULTS

Among 640 men, TRUS underestimated prostate volume relative to mpMRI (median 49.2cc vs. 54.1cc) with 8% lower volume per cc up to 77.5cc (First-third quartile) and 39% lower volume per additional cc above 77.5cc (fourth quartile). For men undergoing radical prostatectomy, mpMRI had a higher correlation coefficient relative to TRUS (0.913 vs 0.878) when compared to surgical pathology. mpMRI PSAD had slightly higher odds vs TRUS PSAD for detecting any PCa (OR 2.94 and OR 2.78, both P <.001) or csPCa (OR 4.20 and OR 4.02, both P <.001). AUC improvements were of borderline significance for mpMRI vs. TRUS PSAD for any PCa (0.689 vs 0.675, P = .05) and not significant for csPCa (0.732 vs 0.722, P = .20). PSAD was not associated with PCa detection for prostates ≥77.5cc.

CONCLUSION

TRUS underestimates prostate volume relative to mpMRI. PSAD based on mpMRI may be better associated with detection of PCa compared to TRUS, but utility of PSAD may be limited for larger prostates.

Prebiopsy bpMRI and hematological parameter-based risk scoring model for predicting outcomes in biopsy-naive men with PSA 4-20 ng/mL

Excessive prostate biopsy is a common problem for clinicians. Although some hematological and bi-parametric magnetic resonance imaging (bpMRI) parameters might help increase the rate of positive prostate biopsies, there is a lack of studies on whether their combination can further improve clinical detection efficiency. We retrospectively enrolled 394 patients with PSA levels of 4-20 ng/mL who underwent prebiopsy bpMRI during 2010-2021. Based on bpMRI and hematological indicators, six models and a nomogram were constructed to predict the outcomes of biopsy. Furthermore, we constructed and evaluated a risk scoring model based on the nomogram. Age, prostate-specific antigen (PSA) density (PSAD), systemic immune-inflammation index, cystatin C level, and the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 score were significant predictors of prostate cancer (PCa) on multivariable logistic regression analyses (P < 0.05) and the five parameters were used to construct the XYFY nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was 0.916. Based on the nomogram, a risk scoring model (XYFY risk model) was constructed and then we divided the patients into low-(XYFY score: < 95), medium-(XYFY score: 95-150), and, high-risk (XYFY score: > 150) groups. The predictive values for diagnosis of PCa and clinically-significant PCa among the three risk groups were 3.0%(6/201), 41.8%(51/122), 91.5%(65/71); 0.5%(1/201), 19.7%(24/122), 60.6%(43/71), respectively. In conclusion, in this study, we used hematological and bpMRI parameters to establish and internally validate a XYFY risk scoring model for predicting the biopsy outcomes for patients with PSA levels of 4-20 ng/mL and this risk model would support clinical decision-making and reduce excessive biopsies.

Diagnostic value of combining PI-RADS v2.1 with PSAD in clinically significant prostate cancer

PURPOSE

To investigate the diagnostic value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CsPCa). We also aimed to combine PI-RADS v2.1 with prostate-specific antigen (PSA) derivatives to improve the predictive value of CsPCa.

METHODS

We retrospectively collected relevant data who underwent standard MRI examinations of the prostate and subjected to a prostate biopsy at Shenzhen People's hospital from November 2014 to November 2019. Included 125 cases of CsPCa and 383 cases of non-CsPCa. All cases were scored using the PI-RADS v2.1. The clinical data collected included age, PSA, free PSA/total PSA, prostate volume and PSA density (PSAD). A univariate analysis was performed to identify statistically significant indicators. Logistic regression was used to analyze the predictive value of the multi-parameter combination on CsPCa.

RESULTS

Except age, the difference in all of indicators between the CsPCa group and non-CsPCa group was statistically significant. The PI-RADS score and PSAD value had the highest diagnostic value. Logistic regression analysis revealed that the PI-RADS score and PSAD value were independent predictors of CsPCa, with a regression model AUC of 0.935. CsPCa detection rates were low when the PI-RADS score ≤ 2 or the PI-RADS score = 3 and the PSAD value ≤ 0.33 ng/ml/ml.

CONCLUSION

Combining the PI-RADS score and PSAD value improved the predictive performance of CsPCa. Patients with a PI-RADS score ≤ 2 or a PI-RADS score = 3 and a PSAD value ≤ 0.33 ng/ml/ml can avoid an unnecessary biopsy.

Implications of the European Association of Urology Recommended Risk Assessment Algorithm for Early Prostate Cancer Detection

Patient summary

Adverse upgrading and/or upstaging in contemporary low-risk prostate cancer patients

Background

Upgrading and/or upstaging in low-risk prostate cancer (PCa) patients may represent an indication for active treatment instead of active surveillance (AS). We addressed contemporary upgrading and/or upstaging rates in a large population based-cohort of low-risk PCa patients.

Materials and methods

Whitin the SEER database (2010–2015), NCCN low-risk PCa patients were identified across management modalities: radical prostatectomy (RP), radiotherapy (RT) and non-local treatment (NLT). In RP patients, upgrading and/or upstaging rates were assessed in logistic regression models.

Results

Overall, of 27,901 low-risk PCa patients, 38% underwent RP vs 28% RT vs 34% NLT. RP patients were the youngest and harbored the highest percentage of positive cores and a higher rate of cT2a than NLT. At RP, 46.2% were upgraded to GGG ≥ 2, 6.0% to GGG ≥ 3 and 10.5% harbored nonorgan-confined stage (NOC, pT3-4 or pN1). Of NOC patients, 1.6% harbored GGG ≥ 3, 6.3% harbored GGG2 and 2.6% harbored GGG1. Of pT2 patients, 4.4% harbored GGG ≥ 3, 33.9% harbored GGG2 and 51.3% harbored GGG1. Age, PSA, percentage of positive cores and number of positive cores independently predicted the presence of NOC and/or GGG ≥ 3, but with low accuracy (63.9%).

Conclusions

In low-risk PCa, critical changes between tumor grade and stage at biopsy vs RP may be expected in very few patients: NOC with GGG ≥ 3 in 1.6% and NOC with GGG2 in 6.3%. Other patients with upgrading and/or upstaging combinations will invariably harbor either pT2 or GGG1 that far less critically affect PCa prognosis.

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Simple Summary

Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications.

Abstract

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.

Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI

The aim of this study is to show the usefulness of collaborative work in the evaluation of prostate cancer from T2-weighted MRI using a dedicated software tool. The variability of annotations on images of the prostate gland (central and peripheral zones as well as tumour) by two independent experts was firstly evaluated, and secondly compared with a consensus between these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate (peripheral and central zones) and cancer. One of the experts then drew the ROI with knowledge of the other expert’s ROI. The surface area of each ROI was used to measure the Hausdorff distance and the Dice coefficient was measured from the respective contours. They were evaluated between the different experiments, taking the annotations of the second expert as the reference. The results showed that the significant differences between the two experts disappeared with collaborative work. To conclude, this study shows that collaborative work with a dedicated tool allows consensus between expertise in the evaluation of prostate cancer from T2-weighted MRI.

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

This study is a head-to-head comparison between mPSAD and MRI-PMbdex. The MRI-PMbdex was created from 2432 men with suspected PCa; this cohort comprised the development and external validation cohorts of the Barcelona MRI predictive model. Pre-biopsy 3-Tesla multiparametric MRI (mpMRI) and 2 to 4-core transrectal ultrasound (TRUS)-guided biopsies for suspicious lesions and/or 12-core TRUS systematic biopsies were scheduled. Clinically significant PCa (csPCa), defined as Gleason-based Grade Group 2 or higher, was detected in 934 men (38.4%). The area under the curve was 0.893 (95% confidence interval [CI]: 0.880−0.906) for MRI-PMbdex and 0.764 (95% CI: 0.774−0.783) for mPSAD, with p < 0.001. MRI-PMbdex showed net benefit over biopsy in all men when the probability of csPCa was greater than 2%, while mPSAD did the same when the probability of csPCa was greater than 18%. Thresholds of 13.5% for MRI-PMbdex and 0.628 ng/mL2 for mPSAD had 95% sensitivity for csPCa and presented 51.1% specificity for MRI-PMbdex and 19.6% specificity for mPSAD, with p < 0.001. MRI-PMbdex exhibited net benefit over mPSAD in men with prostate imaging report and data system (PI-RADS) <4, while neither exhibited any benefit in men with PI-RADS 5. Hence, we can conclude that MRI-PMbdex is more accurate than mPSAD for the proper selection of candidates for prostate biopsy among men with suspected PCa, with the exception of men with a PI-RAD S 5 score, for whom neither tool exhibited clinical guidance to determine the need for biopsy.

Individualised non-contrast MRI-based risk estimation and shared decision-making in men with a suspicion of prostate cancer: protocol for multicentre randomised controlled trial (multi-IMPROD V.2.0)

INTRODUCTION

European Association of Urology and UK National Institute for Health and Care Excellence guidelines recommend that all men with suspicions of prostate cancer should undergo prebiopsy contrast enhanced, that is, multiparametric prostate MRI. Subsequent prostate biopsies should also be performed if MRI is positive, that is, Prostate Imaging-Reporting and Data System (PI-RADS) scores 3-5. However, several retrospective post hoc analyses have shown that this approach still leads to many unnecessary biopsy procedures. For example, 88%-96% of men with PI-RADS, three findings are still diagnosed with clinically non-significant prostate cancer or no cancer at all.

METHODS AND ANALYSIS

This is a prospective, randomised, controlled, multicentre trial, being conducted in Finland, to demonstrate non-inferiority in clinically significant cancer detection rates among men undergoing prostate biopsies post-MRI and men undergoing prostate biopsies post-MRI only after a shared decision based on individualised risk estimation. Men without previous diagnosis of prostate cancer and with abnormal digital rectal examination findings and/or prostate-specific antigen between 2.5 ug/L and 20.0 ug/L are included. We aim to recruit 830 men who are randomised at a 1:1 ratio into control (all undergo biopsies after MRI) and intervention arms (the decision to perform biopsies is based on risk estimation and shared decision-making). The primary outcome of the study is the proportion of men with clinically significant prostate cancer (Gleason 4+3 prostate cancer or higher). We will also compare the overall biopsy rate, benign biopsy rate and the detection of non-significant prostate cancer between the two study groups.

ETHICS AND DISSEMINATION

The study (protocol V.2.0, 4 January 2021) was approved by the Ethics Committee of the Hospital District of Southwest Finland (IORG number: 0001744, IBR number: 00002216; trial number: 99/1801/2019). Participants are required to provide written informed consent. Full reports of this study will be submitted to peer-reviewed journals, mainly urology and radiology.

TRIAL REGISTRATION NUMBER

NCT04287088; the study is registered at ClinicalTrials.gov.

Prostate cancer biomarkers: a practical review based on different clinical scenarios

Traditionally, diagnosis and staging of prostate cancer (PCa) have been based on prostate-specific antigen (PSA) level, digital rectal examination (DRE), and transrectal ultrasound (TRUS) guided prostate biopsy. Biomarkers have been introduced into clinical practice to reduce the overdiagnosis and overtreatment of low-grade PCa and increase the success of personalized therapies for high-grade and high-stage PCa. The purpose of this review was to describe available PCa biomarkers and examine their use in clinical practice. A nonsystematic literature review was performed using PubMed and Scopus to retrieve papers related to PCa biomarkers. In addition, we manually searched websites of major urological associations for PCa guidelines to evaluate available evidence and recommendations on the role of biomarkers and their potential contribution to PCa decision-making. In addition to PSA and its derivates, thirteen blood, urine, and tissue biomarkers are mentioned in various PCa guidelines. Retrospective studies have shown their utility in three main clinical scenarios: (1) deciding whether to perform a biopsy, (2) distinguishing patients who require active treatment from those who can benefit from active surveillance, and (3) defining a subset of high-risk PCa patients who can benefit from additional therapies after RP. Several validated PCa biomarkers have become commercially available in recent years. Guidelines now recommend offering these tests in situations in which the assay result, when considered in combination with routine clinical factors, is likely to affect management. However, the lack of direct comparisons and the unproven benefits, in terms of long-term survival and cost-effectiveness, prevent these biomarkers from being integrated into routine clinical use.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Objectives

Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.

Materials and Methods

The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.

Results

The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.

Conclusions

In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

One-Day Prostate Cancer Diagnosis: Biparametric Magnetic Resonance Imaging and Digital Pathology by Fluorescence Confocal Microscopy

In this prospective observational study, we tested the feasibility and efficacy of a novel one-day PCa diagnosis path based on biparametric magnetic resonance (bpMRI) and digital pathology by fluorescence confocal microscopy (FCM). Patients aged 55–70 years scheduled for PBx due to increased PSA levels (3–10 ng/mL) and/or abnormal digitorectal examination were enrolled. All patients underwent bpMRI and PBx with immediate FCM evaluation of biopsy cores. Patients were asked to fill out a dedicated Patient Satisfaction Questionnaire. Patients’ satisfaction rates and concordance between digital pathology and standard HE evaluation were the outcomes of interest. Twelve patients completed our one-day PCa diagnosis path. BpMRI showed suspicious lesions in 7 patients. Digital pathology by FCM identified PCa in 5 (41.7%) of the 12 patients. Standard pathology confirmed the diagnosis made through digital pathology in all the cases. At a per patient level, high concordance between the methods was achieved in Gleason Grading (4 out of 5 patients). The level of agreement in the number of positive cores was lower but did not affect the choice of treatment in any of the 5 PCa cases. At a per core level, the agreement was very high for the diagnosis of anyPCa (96.2%) and csPCa (97.3%), with a k coefficient of 0.90 and 0.92, respectively (near perfect agreement). In conclusion, one-day PCa diagnosis by FCM represents a feasible, reliable, and fast diagnostic method that provides significant advantages in optimizing time and resources, leading to patients having a higher quality standard of care perception.

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.

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer

PURPOSE

⁶⁸ Ga-PSMA PET/CT has high specificity and sensitivity for the detection of both intraprostatic tumor focal lesions and metastasis. However, approximately 10% of primary prostate cancer are invisible on PSMA-PET (exhibit no or minimal uptake). In this work, we investigated whether machine learning-based radiomics models derived from PSMA-PET images could predict invisible intraprostatic lesions on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer.

METHODS

In this retrospective study, patients with or without prostate cancer who underwent ⁶⁸ Ga-PSMA PET/CT and presented negative on PSMA-PET image at either of two different institutions were included: institution 1 (between 2017 and 2020) for the training set and institution 2 (between 2019 and 2020) for the external test set. Three random forest (RF) models were built using selected features extracted from standard PET images, delayed PET images, and both standard and delayed PET images. Then, subsequent tenfold cross-validation was performed. In the test phase, the three RF models and PSA density (PSAD, cut-off value: 0.15 ng/ml/ml) were tested with the external test set. The area under the receiver operating characteristic curve (AUC) was calculated for the models and PSAD. The AUCs of the radiomics model and PSAD were compared.

RESULTS

A total of 64 patients (39 with prostate cancer and 25 with benign prostate disease) were in the training set, and 36 (21 with prostate cancer and 15 with benign prostate disease) were in the test set. The average AUCs of the three RF models from tenfold cross-validation were 0.87 (95% CI: 0.72, 1.00), 0.86 (95% CI: 0.63, 1.00), and 0.91 (95% CI: 0.69, 1.00), respectively. In the test set, the AUCs of the three trained RF models and PSAD were 0.903 (95% CI: 0.830, 0.975), 0.856 (95% CI: 0.748, 0.964), 0.925 (95% CI:0.838, 1.00), and 0.662 (95% CI: 0.510, 0.813). The AUCs of the three radiomics models were higher than that of PSAD (0.903, 0.856, and 0.925 vs. 0.662, respectively; P = .007, P = .045, and P = .005, respectively).

CONCLUSION

Random forest models developed by ⁶⁸ Ga-PSMA-11 PET-based radiomics features were proven useful for accurate prediction of invisible intraprostatic lesion on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer and showed better diagnostic performance compared with PSAD.

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

Background

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

Methods

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

Results

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

Conclusions

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

How to Improve TRUS-Guided Target Biopsy following Prostate MRI

TRUS is a basic imaging modality when radiologists or urologists perform cognitive fusion or image fusion biopsy. This modality plays the role of the background images to add to an operator's cognitive function or MRI images. Operators need to know how to make TRUS protocols for lesion detection or targeting. Tumor location, size, and shape on TRUS are different from those on MRI because the scan axis is different. TRUS findings of peripheral or transition tumors are not well known to radiologists and urologists. Moreover, it remains unclear if systematic biopsy is necessary after a tumor is targeted. The purpose of this review is to introduce new TRUS protocols, new imaging features, new biopsy techniques, and to assess the necessity of systematic biopsy for improving biopsy outcomes.

Analysis of the usefulness of magnetic resonance imaging and clinical parameters in the detection of prostate cancer in the first systematic biopsy combined with targeted cognitive biopsy

Introduction

The study aimed to assess the suitability of multiparametric magnetic resonance prostate imaging (mpMRI) in combination with clinical parameters [prostate-specific antigen (PSA), digital rectal examination (DRE)] in the identification of men at risk of the presence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa, Gleason Score ≥3+4) in the cognitive fusion with systematic prostate biopsy.

Material and methods

We retrospectively evaluated a population of 215 biopsy – naive patients with a clinical suspicion of prostate cancer. The results of mpMRI, DRE, PSA and biopsy were analyzed. MpMRI of the prostate according to the Prostate Imaging Reporting and Data System (PI-RADS) v.2.0 scheme preceded cognitive fusion and systematic transrectal prostate biopsy. Uni- and multivariable logistic regression analysis (MVA) was used to identify the variables determining the risk of detecting PCa overall and csPCa.

Results

In MVA, it was established that the combination of variables such as PSA level [odds ratio (OR) 1.195; p = 0.002], PI-RADS ≥3 (OR 7.7; p = 0.002), prostate volume (OR 0.98; p = 0.017) significantly determines the probability of PCa detection in biopsy, while for csPCa it is PSA level (OR 1.14; p = 0.004), DRE (+) (OR 5.75; p <0.001), PI-RADS ≥4 (OR 6.5; p <0.001). Analysis of mpMRI diagnostic value for PI-RADS ≥4 revealed better sensitivity (88.9% vs 82.6%) and better negative predictive value (NPV) (94.5% vs 82.4%) for detection of csPCa than for PCa overall.

Conclusions

MpMRI results combining with DRE and PSA parameters help to identify men at high – or low risk of csPCa detection in the first – time biopsy.

Bioptic prostatic inflammation correlates with false positive rates of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer

Introduction

The aim of this article was to determine the impact of bioptic prostatic inflammation (PI) on the false positive rate of multiparametric magnetic resonance imaging (mp-MRI) in detecting clinically significant prostate ancer (csPCa).

Material and methods

Our prostate biopsy database was queried to identify patients who underwent mp-MRI before PB at our institution. A dedicated uropathologist prospectively assessed bioptic PI using the Irani scores. We evaluated the association between mp-MRI findings, bioptic Gleason grade (GG) and aggressiveness of PI, and PCa detection.

Results

In total, 366 men were included. In patients with Prostate Imaging Reporting and Data System (PIRADS) 4-5 lesions, the csPCa (GG ≥2) rate was significantly higher in those with low-grade than in those with high-grade PI (36% vs 29.7%; p = 0.002), and in those with low-aggressive than in those with high-aggressive PI (37.7% vs 30.1%; p = 0.0003). The false positive rates of PIRADS 4–5 lesions for any PCa were 34.2% and 57.8% for low- and high-grade PI, respectively (p = 0.002); similarly, they were 29.5% and 59.4% for mildly and highly-aggressive PI (p = 0.0003). Potential study limitations include its retrospective analysis and single-center study and lack of assessment of the type of PI.

Conclusions

Bioptic PI directly correlates with false positive rates of mp-MRI in detecting csPCa. Clinicians should be aware that PI remains the most common pitfall of mp-MRI.

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.

Is transrectal ultrasound-guided systematic biopsy necessary after PI-RADS 4 is targeted?

Purpose: Target biopsy is usually performed in Prostate Imaging Reporting and Data System (PI-RADS) 4. Still, it is unclear if adding systematic biopsy to target biopsy influences cancer detection. The aim was to assess the role of systematic biopsy for detecting significant cancer after PI-RADS 4 is targeted.Methods: Between March 2014 and November 2018, 182 men with PI-RADS 4 underwent transrectal ultrasound (TRUS)-guided biopsy. Systematic biopsy was added to target biopsy in 128 men (Group I) by May 2018 because PI-RADS 4 was not completely visible on TRUS, while it was done in 54 men (Group II) from June 2018 regardless of lesion visibility. Significant cancer detection rates (CDRs) were compared between the groups regarding target and systematic biopsies. Major complication rate was also compared. Significant cancer was defined as a Gleason score ≥7 tumor. Standard reference was biopsy examination. Fisher’s exact were used for statistical analysis.Results: The significant CDRs were 21.9% (28/128) in the Group I and 38.9% (21/54) in the Group II (P= 0.0273). The significant cancers of Group I and II were missed in two (1.6%) and in one (1.9%) by target biopsy, respectively. Major complication rates of these groups were 0.8% (1/128) and 0% (0/54), respectively (P= 0.999).Conclusion: Systematic biopsy should be added to target biopsy even though PI-RADS 4 is clearly visible on ultrasound. A significant number of significant cancers are detected with systematic biopsy.

Significance of the prostate central gland and total gland volume ratio in the diagnosis of prostate cancer patients in the prostate specific antigen grey zone

Objective

To explore the significance of the prostate central gland to total gland volume ratio (PVc/PV) in the diagnosis of prostate cancer (PCa) in patients with prostate specific antigen (PSA) levels in the grey zone (4–10 ng/ml).

Methods

This retrospective study enrolled patients that had undergone prostate biopsy. The volume of the prostate and the central prostate gland were measured. The differences in PSA, the ratio of free to total PSA (f/tPSA), PSA density (PSAD) and PVc/PV between the PCa and non-PCa groups were compared. Receiver operating characteristic curve analysis for PCa and clinically significant PCa (csPCa) diagnosis were calculated according to PSA (reference), f/tPSA, PSAD and PVc/PV.

Results

This study enrolled 136 patients. There was no significant difference in PSA and f/tPSA between the PCa and non-PCa groups, while there were significant differences in PSAD and PVc/PV. The area under the curve values of PVc/PV for PCa or csPCa diagnosis were 0.876 and 0.933, respectively; and for PSAD, they were 0.705 and 0.790, respectively. These were significantly different compared with the PSA curve, whereas f/tPSA showed no significant difference from the PSA curve.

Conclusion

PVc/PV could be a predictor of PCa when PSA is between 4–10 ng/ml.

Prostate cancer screening: A survey of medical students' knowledge in Lome, Togo, and associated determinants in a resource-limited African context

OBJECTIVES

The aims of this study were to assess the knowledge of medical students in Lomé about these means of screening for prostate cancer in a context of limited resources and controversy about prostate cancer screening, and to identify the determinants associated with these results.

METHODS

This was a prospective descriptive and cross-sectional study conducted in the form of a survey of medical students regularly enrolled at the Faculty of Health Sciences of the University of Lomé for the 2019-2020 academic years.

RESULTS

Of the 1635 eligible students, 1017 correctly completed the form, corresponding to a rate of 62.20%. The average age was 22 ± 3.35 years. The sex ratio (M/F) was 2.5. Undergraduate students were the most represented (53.69%). Students who had not received any training on prostate cancer were the most represented (57.13%). Only 12.88% of the students had completed a training course in urology. Concerning the prostate-specific antigen blood test, there was a statistically significant relationship between the students' knowledge and some of their socio-demographic characteristics, namely age (p value = 0.0037; 95% confidence interval (0.50-1.77)); gender (p value = 0.0034; 95% confidence interval (1.43-2.38)); study cycle (p value ˂ 0.0001; 95% confidence interval (0.56-5.13)) and whether or not they had completed a placement in a urology department (p value ˂ 0.0001; 95% confidence interval (0.49-1.55)). On the contrary, there was no statistically significant relationship between students' knowledge of the digital rectal examination and their study cycle (p value = 0.082; 95% confidence interval (0.18-3.44)).

CONCLUSION

Medical students in Lomé have a good theoretical knowledge and a fair practical level of the digital rectal examination clinical examination and an average theoretical knowledge and a below average practical level of prostate-specific antigen, increasing however along the curriculum in the context of prostate cancer screening.

Can Prostate-Specific Antigen Density Be an Index to Distinguish Patients Who Can Omit Repeat Prostate Biopsy in Patients with Negative Magnetic Resonance Imaging?

Purpose

We evaluated the negative predictive value (NPV) of multiparametric magnetic resonance imaging (mpMRI) in detecting clinically significant prostate cancer (csPCa) according to biopsy setting and prostate-specific antigen density (PSAD) using transperineal template-guided saturation prostate biopsy (TPB) as the reference standard.

Methods

A total of 161 patients with biopsy histories and negative pre-biopsy mpMRI (Prostate Imaging Reporting and Data System version 2 scores of less than 3) participated in the study. TPB was performed on the following indications: “prior negative biopsy” in patients with persistent suspicion of prostate cancer (n = 91) or “confirmatory biopsy” in patients who were candidates for active surveillance (n = 70). The csPCa was defined as a Gleason score of 3 + 4 or greater. We calculated the NPV of mpMRI in detecting csPCa according to biopsy history and prostate-specific antigen density (PSAD) and conducted a logistic regression analysis to determine the clinical predicator for the absence of csPCa.

Results

The detection rate of csPCa was 5.5% in the prior negative biopsy group and 14.3% in the confirmatory biopsy group (P = 0.057). None of the variables in the logistic regression models including PSAD <0.15 ng/mL/cc and prior negative biopsy could predict the absence of csPCa. The NPV of mpMRI in detecting csPCa in patients with a prior negative biopsy worsen from 94.5% to 93.3% when combined with PSAD <0.15 ng/mL/cc.

Conclusion

Patients with negative mpMRI findings may not omit repeat biopsy even if their prior biopsy histories are negative and PSADs are <0.15 ng/mL/cc.

Cancer detection rates of the PI-RADSv2.1 assessment categories: systematic review and meta-analysis on lesion level and patient level

BACKGROUND

The Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level.

METHODS

Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions.

RESULTS

A total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0-8%) for PI-RADS 1, 4% (1-9%) for PI-RADS 2, 20% (13-27%) for PI-RADS 3, 52% (43-61%) for PI-RADS 4, 89% (76-97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0-20%) for PI-RADS 1, 9% (5-13%) for PI-RADS 2, 16% (7-27%) for PI-RADS 3, 59% (39-78%) for PI-RADS 4, 85% (73-94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis.

CONCLUSIONS

Our estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories.