PI-RADS 3 lesions: Does the association of the lesion volume with the prostate-specific antigen density matter in the diagnosis of clinically significant prostate cancer?

Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging-Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Clinical-imaging metrics for the diagnosis of prostate cancer in PI-RADS 3 lesions

OBJECTIVE

To explore the feasibility and efficacy of clinical-imaging metrics in the diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in prostate imaging-reporting and data system (PI-RADS) category 3 lesions.

METHODS

A retrospective analysis was conducted on lesions diagnosed as PI-RADS 3. They were categorized into benign, non-csPCa and csPCa groups. Apparent diffusion coefficient (ADC), T2-weighted imaging signal intensity (T2WISI), coefficient of variation of ADC and T2WISI, prostate-specific antigen density (PSAD), ADC density (ADCD), prostate-specific antigen lesion volume density (PSAVD) and ADC lesion volume density (ADCVD) were measured and calculated. Univariate and multivariate analyses were used to identify risk factors associated with PCa and csPCa. Receiver operating characteristic curve (ROC) and decision curves were utilized to assess the efficacy and net benefit of independent risk factors.

RESULTS

Among 202 patients, 133 had benign prostate disease, 25 non-csPCa and 44 csPCa. Age, PSA and lesion location showed no significant differences (P > 0.05) among the groups. T2WISI and coefficient of variation of ADC (ADCcv) were independent risk factors for PCa in PI-RADS 3 lesions, yielding an area under the curve (AUC) of 0.68. ADC was an independent risk factor for csPCa in PI-RADS 3 lesions, yielding an AUC of 0.65. Decision curve analysis showed net benefit for patients at certain probability thresholds.

CONCLUSIONS

T2WISI and ADCcv, along with ADC, respectively showed considerable promise in enhancing the diagnosis of PCa and csPCa in PI-RADS 3 lesions.

The Impact of Prostate Volume on Prostate Cancer Detection: Comparing Magnetic Resonance Imaging with Transrectal Ultrasound in Biopsy-naïve Men

Background and objective

This study aimed to determine the difference in prostate volume (PV) derived from transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (mpMRI), and to further investigate the role of TRUS prostate-specific antigen density (PSAD) and mpMRI-PSAD in prostate cancer (PCa) detection in biopsy-naïve men.

Methods

Patients who underwent an initial prostate biopsy within 3 mo after mpMRI between January 2016 and December 2021 were analyzed retrospectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both TRUS-PSAD and mpMRI-PSAD for PCa detection were calculated and compared. The Pearson correlation coefficient, Bland-Altman plot, and receiver operating characteristic curve were also utilized to explore the interests of this study.

Key findings and limitations

The median prostate-specific antigen level of 875 patients was 9.79 (interquartile range [IQR]: 7.09-13.50) ng/ml. The median mpMRI-PV and TRUS-PV were 41.92 (IQR: 29.29-60.73) and 41.04 (IQR: 29.24-57.27) ml, respectively, demonstrating a strong linear correlation (r = 0.831, 95% confidence interval: 0.809, 0.850; p < 0.01) and sufficient agreement. No significant difference was observed in terms of the sensitivity, specificity, PPV, and NPV between TRUS-PSAD and mpMRI-PSAD for any PCa and clinically significant PCa (csPCa) detection. The overall discriminative ability of TRUS-PSAD for detecting PCa or non-PCa, as well as csPCa and non-csPCa, was comparable with that of mpMRI-PSAD, and similar results were also observed in the subsequent analysis stratified by mpMRI-PV quartiles, prostate-specific antigen level, and age. The limitations include the retrospective and single-center nature and a lack of follow-up information.

Conclusions and clinical implications

TRUS-PV and MRI-PV exhibited a strong linear correlation and reached sufficient agreement. The efficiency of TRUS-PSAD and mpMRI-PSAD for PCa detection was comparable. TRUS could be used for PV estimation and dynamic monitoring of PSAD, and TRUS-PSAD could effectively guide clinical decision-making and optimize diagnostic strategies.

Patient summary

In this work, prostate volume (PV) derived from transrectal ultrasound (TRUS) exhibited a strong linear correlation with the PV derived from multiparametric magnetic resonance imaging (mpMRI). The efficiency of TRUS prostate-specific antigen density (PSAD) and mpMRI-PSAD for the detection of prostate cancer was comparable. TRUS could be used for PV estimation and TRUS-PSAD could help in clinical decision-making and optimizing diagnostic strategies.

Effects of the lesion size on clinically significant prostate cancer detection rates in PI-RADS category 3-5 lesions

INTRODUCTION

Prostate cancer (PCa) ranks second among prevalent cancers in men, necessitating effective screening tools such as multiparametric magnetic resonance imaging (mpMRI) with the prostate imaging reporting and data system (PI-RADS) classification. This study explores the impact of lesion volume on clinically significant prostate cancer (csPCa) detection rates in PI-RADS 3-5 lesions, aiming to contribute insights into the underexplored relationship between lesion size and csPCa detection.

MATERIALS AND METHODS

A retrospective analysis was conducted on data from 754 patients undergoing mpMRI-guided transrectal ultrasound (TRUS) prostate biopsy between January 2016 and 2023. Patients with PI-RADS 3, 4, and 5 lesions were included. Lesion size and PI-RADS categories were assessed through mpMRI, followed by MR fusion biopsy.

RESULTS

Of the patients, 33.7%, 52.3%, and 14.1% had PI-RADS 3, 4, and 5 lesions, respectively. Lesion sizes correlated significantly with csPCa detection in PI-RADS 4 and 5 categories. For PI-RADS 3 lesions, no significant differences in csPCa rates were observed based on lesion size. However, in PI-RADS 4 and 5 groups, larger lesions showed higher csPCa rates.

CONCLUSION

This study suggests that subgroup categorizations based on lesion volume could predict clinically significant PCa with high accuracy, potentially reducing unnecessary biopsies and associated overtreatment. Future research should further explore the relationship between lesion size and csPCa, clarifying discussions regarding the inclusion of systematic biopsies in diagnostic protocols.

Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions

PURPOSE OF REVIEW

Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions present a clinical dilemma due to their uncertain nature, which complicates the development of a definitive management strategy. These lesions have an incidence rate of approximately 22-32%, with clinically significant prostate cancer (csPCa) accounting for about 10-30%. Therefore, a thorough evaluation is warranted.

RECENT FINDINGS

This review highlights the need for radiology peer review, including the confirmation of dynamic contrast-enhanced (DCE) compliance, as the initial step. Additional MRI models such as VERDICT or Tofts need to be verified. Current evidence shows that imaging and clinical indicators can be used for risk stratification of PI-RADS 3 lesions. For low-risk lesions, a safety net monitoring approach involving annual repeat MRI can be employed. In contrast, lesions deemed potentially risky based on prostate-specific antigen density (PSAD), 68 Ga-PSMA PET/CT, MPS, Proclarix, or AI/machine learning models should undergo biopsy. It is recommended to establish a multidisciplinary team that takes into account factors such as age, PSAD, prostate, and lesion size, as well as previous biopsy pathological findings. Combining expert opinions, clinical-imaging indicators, and emerging methods will contribute to the development of management strategies for PI-RADS 3 lesions.

A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score

PURPOSE

This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa).

METHODS

The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed.

RESULTS

The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model.

CONCLUSION

P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.

The Predictive Value of Prostate-Specific Antigen Density: A Retrospective Analysis of Likert 3 Multiparametric MRI of the Prostate

Background Many international studies have covered the predictors of prostate cancer, but there is limited information pertaining to Likert 3 MRI scores and the diagnosis of clinically significant prostate cancer (cs-PCa). Therefore, this study aimed to assess the detection rate of significant prostate cancer in men with a Likert 3 score multiparametric MRI (mp-MRI) and the predictive value of prostate-specific antigen (PSA) density in detecting significant prostate cancer. Methods This is a retrospective analysis of patients referred for suspected confined prostate cancer. Inclusion criteria were patients with prostate mp-MRI score of Likert 3 and a prostate biopsy performed. Exclusion criteria included grossly abnormal feeling prostate, no biopsy performed, and an mp-MRI score (Prostate Imaging-Reporting and Data System/Likert) of 1, 2, 4, and 5. cs-PCa was defined as ≥ Gleason 3+4 prostate cancer. PSA density (PSAD) was calculated from MRI estimation of prostate volume. PSAD and histology results were subjected to receiver operating characteristic (ROC) curve analysis with the intention to assess the detection rate of significant prostate cancer in men with Likert 3 mp-MRI and the predictive value of PSAD in detecting significant prostate cancer. Results A total of 819 eligible men had a pre-biopsy mp-MRI scan taken between October 2019 and March 2022. A total of 177 men (21.6%, n = 819) were Likert 3 positive, and 31 did not proceed to take prostate biopsies. A total of 146 patients were included in the study. The median PSAD was 0.19 in men with cs-PCa. Prostate cancer was detected in 42 men (28.8% of the total included set), of which 27 (18.5%) had a Gleason 3+3 prostate cancer and 15 (10.3%) had Gleason ≥ 3+4 prostate cancer. Therefore, 35.7% (n = 42) of biopsy-positive men with Likert 3 mp-MRI had cs-PCa. The ROC curve analysis confirms that PSAD is a predictor of cs-PCa. The optimal PSAD threshold was 0.16 (95% CI: 0.14-0.19), which gives an accuracy of 0.7371, a sensitivity of 0.7333, and a specificity of 0.7375. Conclusion The specificity of PSAD is arguably insufficient for it to stand alone as a decision-making tool when counseling men with equivocal mp-MRI on whether or not to undergo prostate biopsy. A predictive model will need to incorporate other independent risk factors. These may include lesion size, multiplicity, location of lesion(s), and age.

The role of tumor density in predicting significant cancer on targeted biopsy of the prostate

OBJECTIVE

Multiparametric magnetic resonance imaging (mpMRI) is central to diagnosing prostate cancer; however, not all imaged lesions represent clinically significant tumors. We aimed to evaluate the association between the relative tumor volume on mpMRI and clinically significant prostate cancer on biopsy.

MATERIALS AND METHODS

We retrospectively reviewed the medical records of 340 patients who underwent combined transperineal targeted and systematic prostate biopsies between 2017 and 2021. Tumor volume was estimated based on the mpMRI diameter of suspected lesions. Relative tumor volume (tumor density) was calculated by dividing the tumor and prostate volumes. The study outcome was clinically significant cancer on biopsy. Logistic regression analyses were used to evaluate the association between tumor density and the outcome. The cutoff for tumor density was determined with ROC curves.

RESULTS

Median estimated prostate and peripheral zone tumor volumes were 55cm3 and 0.61cm3, respectively. Median PSA density was 0.13 and peripheral zone tumor density was 0.01. Overall, 231 patients (68%) had any cancer and 130 (38%) had clinically significant cancer. On multivariable logistic regression age, PSA, previous biopsy, maximal PI-RADS score, prostate volume, and peripheral zone tumor density were significant predictors of outcome. Using a threshold of 0.006, the sensitivity, specificity, positive and negative predictive values of peripheral zone tumor density were 0.9, 0.51, 0.57, and 0.88, respectively.

CONCLUSION

Peripheral zone tumor density is associated with clinically significant prostate cancer in patients with PI-RADS 4 and 5 mpMRI lesions. Future studies are required to validate our findings and evaluate the role of tumor density in avoiding unnecessary biopsies.

PI-RADS 3 lesions: a critical review and discussion of how to improve management

Since the publication of PI-RADS v1 in 2012, the debate regarding the question of how to manage PI-RADS 3 lesions has been mostly unsolved. However, based on our review of the current literature we discuss possible solutions and improvements to the original classification, factors such as PSAD (Prostate Specific Antigen Density), age, and tumor volume, in the decision of whether to proceed with a biopsy or not.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

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

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

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.

New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

Purpose

The aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa).

Patients and Methods

The demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves.

Results

The AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold.

Conclusions

This study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.

Concordance between biparametric MRI, transperineal targeted plus systematic MRI-ultrasound fusion prostate biopsy, and radical prostatectomy pathology

We aimed to confirm the reliability of the results of bi-parametric magnetic resolution imaging-ultrasound fusion targeted and systematic biopsies (bpMRI-US transperineal FTSB) compared to prostatectomy specimens. We retrospectively analyzed the records of 80 men who underwent bpMRI-US transperineal FTSB with region of interest (ROI) and subsequent robot-assisted radical prostatectomy. Changes in the grade group determined by MRI and biopsy versus surgical specimens were analyzed. Thirty-five patients with insignificant prostate cancer and 45 with significant cancer were diagnosed using bpMRI-US transperineal FTSB. Among those with insignificant PCa, 25 (71.4%) were upgraded to significant PCa in prostatectomy specimens: 9/12 (75.0%) with Prostate Imaging Reporting and Data System (PI-RADS) 3, 12/16 (75.0%) with PI-RADS 4, and 4/7 (57.1%) with PI-RADS 5. In the PI-RADS 3 group, the upgraded group showed higher prostate specific antigen (PSA) and PSA density (PSAD) than the concordance group; PSA 8.34(2.73) vs. 5.31(2.46) (p = 0.035) and PSAD 0.29(0.11) vs. 0.18(0.09) (p = 0.025). The results of prostate biopsy and prostatectomy specimens were inconsistent and underestimated in patients with MRI-visible lesions. Therefore, for precise and individualized treatment strategies for PCa with MRI-visible lesions, careful interpretation of biopsy result is required.

Considering Predictive Factors in the Diagnosis of Clinically Significant Prostate Cancer in Patients with PI-RADS 3 Lesions

The use of multi-parametric magnetic resonance imaging (mpMRI) in conjunction with the Prostate Imaging Reporting and Data System (PI-RADS) is standard practice in the diagnosis, surveillance, and staging of prostate cancer. The risk associated with lesions graded at a PI-RADS score of 3 is ambiguous. Further characterization of the risk associated with PI-RADS 3 lesions would be useful in guiding further work-up and intervention. This study aims to better characterize the utility of PI-RADS 3 and associated risk factors in detecting clinically significant prostate cancer. From a prospectively maintained IRB-approved dataset of all veterans undergoing mpMRI fusion biopsy at the Southeastern Louisiana Veterans Healthcare System, we identified a cohort of 230 PI-RADS 3 lesions from a dataset of 283 consecutive UroNav-guided biopsies in 263 patients from October 2017 to July 2020. Clinically significant prostate cancer (Gleason Grade ≥ 2) was detected in 18 of the biopsied PI-RADS 3 lesions, representing 7.8% of the overall sample. Based on binomial analysis, PSA densities of 0.15 or greater were predictive of clinically significant disease, as was PSA. The location of the lesion within the prostate was not shown to be a statistically significant predictor of prostate cancer overall (p = 0.87), or of clinically significant disease (p = 0.16). The majority of PI-RADS 3 lesions do not represent clinically significant disease; therefore, it is possible to reduce morbidity through biopsy. PSA density is a potential adjunctive factor in deciding which patients with PI-RADS 3 lesions require biopsy. Furthermore, while the risk of prostate cancer for African-American men has been debated in the literature, our findings indicate that race is not predictive of identifying prostate cancer, with comparable Gleason grade distributions on histology between races.

Magnetic resonance imaging /ultrasonography fusion transperineal prostate biopsy for prostate cancer: Initial experience at a Middle Eastern tertiary medical centre

Objective: To report on the outcomes of magnetic resonance imaging (MRI)/ultrasonography (US)-fusion transperineal prostate (TP) biopsy at a tertiary medical centre in the Middle East including detection rate of clinically significant prostate cancer (csPCa), complications, and tolerability of the procedure. Patients and methods: Between May 2019 and June 2020, 98 MRI/US-fusion TP biopsies were performed in the US suite using light sedation. All patients had pre-biopsy 3-T multiparametric MRI. Data on patient characteristics, PCa detection rate and complication rates were collected retrospectively. A Gleason score ≥3 + 4 was defined as csPCa.

RESULTS

There were 98 patients, with a mean (SD) age of 65 (9.1) years, and a median (SD) prostate-specific antigen level prior to biopsy of 7.53 (12.97) ng/mL and prostate volume of 51 (31.1) mL. PCa was detected in 54 (55%) patients, with csPCa detected in 43 (44%). A total of 124 Prostate Imaging-Reporting and Data System (PI-RADS) 3-5 lesions were targeted. Grade Group ≥2 PCa was found in 35.5% of the targeted lesions. Random biopsies detected one csPCa Gleason score 3 + 4 in one patient with a negative target. None of the patients had post-biopsy haematuria or retention. Only one patient developed acute prostatitis requiring in-patient intravenous antibiotics.

CONCLUSIONS

MRI/US-fusion TP biopsy has an adequate detection rate of csPCa with minimal complications and low infection rates after biopsy. This is one of the first TP biopsy series in the Middle East paving the way for wider adoption in the region.

ABBREVIATIONS

AS: active surveillance; AUR: acute urinary retention; GG: Grade Group; IQR: interquartile range; mpMRI: multiparametric MRI; (cs)PCa: (clinically significant) prostate cancer; PI-RADS: Prostate Imaging-Reporting and Data System; TP: transperineal; US: ultrasonography; TRUS: transrectal Ultrasound guided.

[PI-RADS 2.1 and structured reporting of magnetic resonance imaging of the prostate]

Klinisches/methodisches Problem

Die Identifikation klinisch signifikanter Prostatakarzinome bei gleichzeitigem Vermeiden einer Überdiagnostik niedrigmaligner Tumoren stellt eine Herausforderung in der klinischen Routine dar.

Radiologische Standardverfahren

Die gemäß PI-RADS-Richtlinien (Prostate Imaging Reporting and Data System Guidelines) akquirierte und interpretierte multiparametrische Magnetresonanztomographie (MRT) der Prostata ist als klinischer Standard bei Urologen und Radiologen akzeptiert.

Methodische Innovationen

Die PI-RADS-Richtlinien sind neu auf Version 2.1 aktualisiert worden und beinhalten neben präzisierten technischen Anforderungen einzelne Änderungen in der Läsionsbewertung.

Leistungsfähigkeit

Die PI-RADS-Richtlinien haben entscheidende Bedeutung in der Standardisierung der multiparametrischen MRT der Prostata erlangt und bieten Vorlagen zur strukturierten Befundung, was die Kommunikation mit dem Zuweiser erleichtert.

Bewertung

Die nun auf Version 2.1 aktualisierten Richtlinien stellen eine Verfeinerung der verbreiteten Version 2.0 dar. Dabei wurden viele Aspekte der Befundung präzisiert, einige vorbekannte Limitationen bleiben jedoch bestehen und erfordern die weitere Verbesserung der Richtlinien in kommenden Versionen.