Positive Predictive Value of Prostate Imaging Reporting and Data System Version 2 for the Detection of Clinically Significant Prostate Cancer: A Systematic Review and Meta-analysis

Development and validation of risk-stratified biopsy decision pathways incorporating MRI and PSA-derived indicators

OBJECTIVES

Develop risk-adapted conditional biopsy pathways utilizing MRI in combination with prostate-specific antigen (PSA) density (PSAD) and the ratio of free to total PSA (f/tPSA), respectively, to enhance the detection of clinically significant prostate cancer (csPCa) while minimizing 'negative' biopsies in low-risk patients.

METHODS

The Prostate Imaging Reporting and Data System (PI-RADS) category, PSAD, f/tPSA and biopsy-pathology of 1018 patients were collected retrospectively. Subsequently, PSAD and f/tPSA were divided into four intervals, which were then combined with the MRI findings to construct two risk stratification matrix tables. Six biopsy decision pathways were established: three clinical pathways based solely on PSAD and f/tPSA, and three MRI-combined pathways incorporating both PI-RADS and PSA-derived indicators. The biopsy and clinically insignificant PCa (ciPCa) avoidance, csPCa detection rate, and 'negative' biopsies proportion were assessed. Decision curve analysis (DCA) was employed to evaluate the net benefit associated with each pathway.

RESULTS

When reporting PI-RADS 1 - 2, PSAD ≥ 0.20 ng/ml/cm3 or f/tPSA ≤ 0.10 were found to be useful for patient stratification. When reporting PI-RADS 3, PSAD ≥ 0.10 - 0.15 ng/ml/cm3 and f/tPSA ≤ 0.16 - 0.25 were helpful in distinguishing the risk of csPCa. The three MRI-combined pathways showed higher csPCa detection rates (94% to 96%) than the three clinical pathways (85% to 91%); 'MRI + PSAD + f/tPSA' demonstrated a high csPCa detection rate of 94% while maintaining the maximum biopsy avoidance and lowest 'negative' biopsy proportion of 40% and 25%, respectively. The DCA showed significantly higher net benefits for three MRI-combined pathways compared to all clinical pathways.

CONCLUSIONS

The integration of MRI and PSA-derived indicators enables effective patient risk stratification, thereby providing valuable decision-making pathways to enhance the management of csPCa while minimizing 'negative' biopsies.

Radical prostatectomy without prostate biopsy based on a noninvasive diagnostic strategy: a prospective single-center study

BACKGROUND

Prostate biopsy is the most common approach for diagnosing prostate cancer (PCa); however, it has inherent limitations, such as the invasive procedure, postoperative complications, and false negative results. We aimed to provide a noninvasive diagnostic strategy for patients with highly suspected PCa and to evaluate the feasibility of performing biopsy-spared radical prostatectomy.

METHODS

This prospective study included a total of 57 patients between November 10, 2022, and December 1, 2023. All 57 patients underwent radical prostatectomy without prior prostate biopsy based on a noninvasive diagnostic strategy consisting of a diagnostic prediction model [comprised of the prostate imaging-reporting and data system (PI-RADS) score and prostate-specific antigen density (PSAD)] and the 18F-prostate-specific membrane antigen (PSMA)-1007 positron emission tomography (PET)/computed tomography (CT) examination. The primary endpoint was the positive predictive value (PPV) of clinically significant PCa [the International Society of Urological Pathology (ISUP) grade ≥2, Gleason score ≥3 + 4]. The secondary endpoints were a PPV of any-grade PCa (ISUP grade ≥ 1, Gleason score ≥3 + 3) and high-grade PCa (ISUP grade ≥3, Gleason score ≥4 + 3), and the false positive rate of the diagnostic strategy.

RESULTS

Of the 371 screened patients with clinically suspected PCa, 57 patients fulfilled the criteria and consented to participate in this study. The median PSAD level was 0.56 (0.42-0.82) ng/mL2; 13 (22.8%) patients were identified as having a PI-RADS score of 4, and 44 (77.2%) patients with a PI-RADS score of 5. The median SUVmax of 18F-PSMA-1007 PET/CT was 21.6 (15.8-33.0). For the 57 enrolled patients who received radical prostatectomy directly, the PPV of clinically significant PCa was 98.2% [56/57, 95% confidence interval (CI): 90.6-100%]. Only 1.8% (1/57, 95% CI: 0.0-9.4%) of patients were diagnosed with clinically insignificant PCa (ISUP grade = 1, Gleason score = 3 + 3). The PPV of any-grade PCa and high-grade PCa were 100% and 73.7% (42/57, 95% CI: 60.3-84.5%), respectively. No one had a false positive result.

CONCLUSIONS

We proposed a noninvasive diagnostic strategy consisting sequentially of a diagnostic prediction model and the 18F-PSMA-1007 PET/CT examination for diagnosing PCa. Despite some limitations, our initial findings suggest the potential feasibility of radical prostatectomy without prior prostate biopsy.

The added value of artificial intelligence using Quantib Prostate for the detection of prostate cancer at multiparametric magnetic resonance imaging

PURPOSE

Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the prostate. We evaluate the diagnostic performance of radiologists with different levels of experience when reporting mpMRI with the support of available AI-based software (Quantib Prostate).

MATERIAL AND METHODS

This is a single-center study (NCT06298305) involving 110 patients. Those with a positive mpMRI (PI-RADS ≥ 3) underwent targeted plus systematic biopsy (TBx plus SBx), while those with a negative mpMRI but a high clinical suspicion of prostate cancer (PCa) underwent SBx. Three readers with different levels of experience, identified as R1, R2, and R3 reviewed all mpMRI. Inter-reader agreement among the three readers with or without the assistance of Quantib Prostate as well as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for the detection of clinically significant PCa (csPCa) were assessed.

RESULTS

102 patients underwent prostate biopsy and the csPCa detection rate was 47%. Using Quantib Prostate resulted in an increased number of lesions identified for R3 (101 vs. 127). Inter-reader agreement slightly increased when using Quantib Prostate from 0.37 to 0.41 without vs. with Quantib Prostate, respectively. PPV, NPV and diagnostic accuracy (measured by the area under the curve [AUC]) of R3 improved (0.51 vs. 0.55, 0.65 vs.0.82 and 0.56 vs. 0.62, respectively). Conversely, no changes were observed for R1 and R2.

CONCLUSIONS

Using Quantib Prostate did not enhance the detection rate of csPCa for readers with some experience in prostate imaging. However, for an inexperienced reader, this AI-based software is demonstrated to improve the performance.

TRIAL REGISTRATION

Name of registry: clinicaltrials.gov.

TRIAL REGISTRATION NUMBER

NCT06298305. Date of registration: 2022-09.

The Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update

This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans for prostate cancer (PCa). A literature review was conducted on the Medline, Embase, Web of Science, and IEEE Xplore databases. The search focused on studies that utilizes AI to evaluate PSMA PET scans. Original English language studies published from inception to October 2024 were included, while case reports, series, commentaries, and conference proceedings were excluded. AI applications show promise in automating the detection of metastatic disease and anatomical segmentation in PSMA PET scans. AI was also able to predict response to PSMA-based theragnostic and aids in tumor burden segmentation, improving radiotherapy planning. AI could also differentiate intraprostatic PCa with higher histological grade and predict extra-prostatic extension. AI has potential in evaluating PSMA PET scans for PCa, particularly in detecting metastasis, measuring tumor burden, detecting high grade intraprostatic cancer, and predicting treatment outcomes. Larger multicenter prospective studies are necessary to validate and enhance the generalizability of these AI models.

Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels

BACKGROUND

Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences.

PURPOSE

To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance.

STUDY TYPE

Retrospective.

POPULATION

Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa).

FIELD STRENGTH/SEQUENCE

3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image.

ASSESSMENT

CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed.

STATISTICAL TESTS

Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons.

RESULTS

For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61).

DATA CONCLUSION

AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

The Role of Tumor Volume Ratio in Predicting Clinically Significant Prostate Cancer on Transperineal Biopsy

Objectives: Multiparametric magnetic resonance imaging (mpMRI) has made dramatic inroads into the management of localized prostate cancer (PCa); however, not all suspicious lesions represent clinically significant (cs) PCa. We aimed to analyze the hypothetical effect of incorporating tumor volume ratio (TVR) into prostate biopsy (PBx) decision-making. Materials and Methods: Two hundred and fifty-two patients with suspicious lesions at mpMRI undergoing transperineal PBx under local anesthesia between 2019 and 2022 were retrospectively evaluated. TVR was calculated by dividing the tumor volume by the prostate volume. A regression model was used to assess predictors of csPCa. Descriptive statistics were applied to evaluate the effect of including TVR in PBx decision-making. Results: Overall, 119 patients (47%) were found to have csPCa. Age (p < 0.001), prior negative PBx (p = 0.011), and TVR (p < 0.001) were found to be independent predictors of csPCa. Applying the TVR cutoff of 0.23, a total of 117/252 (46%) PBx would have been avoided at the cost of missing csPCa in 26 (10%) men. Conclusions: Age, previous biopsy status, and TVR were found to be independent predictors of csPCa in men with suspicious lesions at mpMRI. Implementation of TVR into PBx decision-making improves the accuracy of mpMRI. Future studies are required to validate our findings and evaluate the role of TVR in avoiding unnecessary PBx.

MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study

Background Although artificial intelligence is actively being developed for prostate MRI, few studies have prospectively validated these tools. Purpose To compare the diagnostic performance of a commercial deep learning algorithm (DLA) and radiologists' clinical reports for cancer detection in participants from two hospitals using histopathologic findings from biopsy specimens as the reference standard. Materials and Methods This prospective bicenter study enrolled participants suspected of having prostate cancer (PCa) who were scheduled for biopsy based on clinical information, including prostate MRI, from December 2022 to July 2023. Targeted prostate biopsies were performed for lesions with Prostate Imaging Reporting and Data System (PI-RADS) scores of 3 or higher, identified by either radiologists or the DLA. PI-RADS classifications by radiologists (using all imaging sequences), the DLA (using biparametric MRI), and the scenario in which radiologist-based PI-RADS 3 scores were modulated with DLA-based PI-RADS scores were compared using the area under the receiver operating characteristic curve (AUC) with DeLong and McNemar tests. Results A total of 259 lesions, including 117 clinically significant PCas (csPCas) (Gleason grade group ≥2), were evaluated in 205 men (median age, 68 years; age range, 47-90 years). At per-lesion analysis, the DLA had a lower sensitivity (94 of 117; 80%) and higher positive predictive value (PPV) (94 of 163; 58%) for detecting csPCa than did the radiologists (109 of 117 [93%] and 109 of 227 [48%]; P = .02 and P = .008, respectively). At per-participant analysis, incorporation of the DLA increased specificity from 23 of 108 (21%) to 48 of 108 (44%) (P = .001), with similar sensitivity (96 of 97 [99%] vs 93 of 97 [96%]; P = .74). There was no evidence of a difference in the AUC between radiologist-based and DLA-based PI-RADS score (0.77 [95% CI: 0.70, 0.82] vs 0.79 [95% CI: 0.73, 0.85]; P = .73). Conclusion The DLA demonstrated lower sensitivity but a greater PPV than radiologists for detecting csPCa in a biopsy setting. Using DLA results when radiologists' interpretations are indeterminate could improve specificity while maintaining sensitivity. International Clinical Trials Registry Platform registration no. KCT0006947 © RSNA, 2025 Supplemental material is available for this article.

A Simple Nomogram to Predict Clinically Significant Prostate Cancer at MRI-Guided Biopsy in Patients with Mild PSA Elevation and Normal DRE

BACKGROUND

Evidence to help avoid unnecessary prostate biopsies is being actively pursued. Our goal was to develop and internally validate a nomogram for predicting clinically significant prostate cancer (csPC) in men with low suspicion of disease (prostate specific antigen [PSA] < 10 ng/mL, normal digital rectal examination [DRE]), in whom magnetic resonance imaging (MRI) findings are positive.

METHODS

Patients with no prior prostate cancer diagnosis who underwent MRI-ultrasound fusion biopsy of the prostate were retrospectively analyzed. Inclusion criteria were PSA < 10 ng/mL, normal DRE, Prostate Imaging Reporting And Data System (PIRADS) category ≥ 3, and no extraprostatic extension or seminal vesicle invasion reported on MRI. Associations between csPC diagnosis and patient or lesion characteristics were analyzed, and a multivariable model was developed. Internal validation of the model with 5-fold cross-validation and bootstrapping methods was performed.

RESULTS

Among 209 patients, 67 were diagnosed with csPC. Factors incorporated into the model for predicting csPC were age, 5-alpha reductase inhibitor use, PSA, prostate volume, PIRADS > 3, and lesion location in the peripheral zone. The model's ROC AUC was 0.86, with consistent performance at internal validation (0.84 with cross-validation, 0.82 with bootstrapping). With an empirical threshold of <10% csPC probability to omit biopsy, 72 (50.7%) unnecessary biopsies would have been avoided, at the cost of missing 2 (3.0%) csPC cases.

CONCLUSIONS

Our nomogram might serve as a valuable tool in refining selection criteria in men considered for prostate biopsy. The major limitation of the study is its retrospective character. Prospective, external validation of the model is warranted.

Are Patients with Prostate Imaging Reporting and Data System 5 Lesions Eligible for Active Surveillance? A Multicentric European Study

BACKGROUND AND OBJECTIVE

Patients with Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions are at a high risk of clinically significant prostate cancer (PCa), extracapsular extension, and biochemical recurrence (BCR) after local treatment. Managing these patients with active surveillance (AS) can be particularly challenging when targeted biopsies indicate favorable-risk tumors. This study aims to evaluate the outcomes of patients with PI-RADS 5 lesions managed with AS.

METHODS

We analyzed data from 126 patients treated at 16 centers in France, Italy, Switzerland, and Belgium, whose initial magnetic resonance imaging revealed at least one PI-RADS 5 lesion and who subsequently underwent AS. The primary endpoint was BCR-free survival. The secondary endpoints included metastasis-free survival, time to biopsy grade reclassification, and time to AS discontinuation, along with their predictors.

KEY FINDINGS AND LIMITATIONS

After a median follow-up of 36 mo after confirmatory biopsies (95% confidence interval [CI] 23-55), BCR was observed in five patients, with the median time not reached. The 5-yr BCR-free survival rate was 88% (95% CI 79-99%). No metastatic progression was reported. Seventeen patients experienced biopsy grade reclassification (median time not reached), and 55 patients discontinued AS. The median time to AS discontinuation was 55 mo (95% CI 46 mo-not applicable). The 5-yr AS discontinuation-free survival rate was 41% (95% CI 30.8-54.6%). On a multivariate Cox regression analysis, baseline prostate-specific antigen density and the percentage of positive biopsy cores were associated with biopsy grade reclassification, AS discontinuation, and BCR.

CONCLUSIONS AND CLINICAL IMPLICATIONS

With strict monitoring, AS is a safe management option for patients with PI-RADS 5 lesions and favorable-risk PCa. Limitations are mainly inherent to the retrospective design of this study.

A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator

OBJECTIVE

To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).

METHODS

We evaluated a cohort of patients who underwent prostate biopsy for suspected prostate cancer (PCa) in New Zealand, Australia, and Switzerland. We collected data on age, body mass index (BMI), PSA level, prostate volume, PSA density (PSAD), PI-RADS scores, previous biopsy, and corresponding histology results. The dataset was divided into derivation (training) and validation (test) sets using random splits. An independent dataset was obtained from the Harvard Dataverse for external validation. A cohort of 1272 patients was analyzed. We fitted a Lasso model, XGBoost, and LightGBM to the training set and assessed their accuracy.

RESULTS

All models demonstrated ROC-AUC values ranging from 0.830 to 0.851. LightGBM was considered the superior model, with an ROC of 0.851 (95%CI: 0.804-0.897) in the test set and 0.818 (95% CI: 0.798-0.831) in the external dataset. The most important variable was PI-RADS, followed by PSA density, history of previous biopsy, age, and BMI.

CONCLUSION

We developed a predictive model for detecting csPCa that exhibited a high ROC-AUC value for internal and external validations. This suggests that the integration of the clinical parameters outperformed each individual predictor. Additionally, the model demonstrated good calibration metrics, indicative of a more balanced model than the existing models.

Comparison of the incidence of clinically significant prostate cancer in patients with isolated peripheral versus transitional zone PIRADS 3 lesions

INTRODUCTION

We sought to investigate the association between isolated PIRADS 3 lesions of the transitional zone (TZ) versus the peripheral zone (PZ) and the incidence of clinically significant prostate cancer (csPCa) on systematic and targeted prostate biopsy (SB, TB).

METHODS

We retrospectively reviewed our tertiary institutional database of patients who underwent mpMRI-fusion followed by TB + SB between 2016 and 2021. We compared the incidence of csPCa (Gleason Grade Group ⩾ 2) in patients with solitary TZ-only PIRADS 3 and PZ-only PIRADS 3 on SB and TB. We excluded patients with (1)known PCa, (2)PIRADS 4-5 and/or (3)lesions in both TZ and PZ. T-tests, Chi-square tests, were conducted to compare between the groups.

RESULTS

Of 1913 patients, we identified 110 with PZ-only and 38 with TZ-only PIRADS 3 lesions. 73 patients in PZ-only and 19 in TZ-only met inclusion criteria. No statistically significant differences were observed between PZ and TZ groups in terms of age, median prostate-specific antigen (PSA), prostate volume, median PSA-density, or median number of targeted cores obtained, all with p > 0.05.On SB, the incidence of csPCA was higher in patients with PZ rather than TZ PIRADS-3 lesions (10/73 vs 1/19, p = 0.28). Similarly, csPCA was more common in TB of PZ versus TZ PIRADS 3 lesions (7/73 vs 0/19, p = 0.33). Based on these results, the positive predictive values of PIRADS3 as a marker of csPCA were 5.3% and 0% for TZ lesions on SB versus TB, respectively, compared to 17.7% and 9.6% in the PZ.

CONCLUSIONS

PIRADS 3 lesions are rarely associated with csPCA on SB and TB, particularly when located in the TZ, which is an important factor to consider when deciding on a biopsy in patients with isolated TZ lesions.

Diagnostic Performance of Multiparametric MRI for the Detection of suspected Prostate Cancer in Biopsy-Naive Patients: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

This meta-analysis aimed to assess the diagnostic accuracy of multiparametric MRI (mpMRI) in detecting suspected prostate cancer (PCa) in biopsy-naive men.

MATERIALS AND METHODS

PubMed, Scopus, and the Cochrane Library databases were systematically searched for studies published from January 2013 to April 2024. Sixteen studies comprising 4973 patients met the inclusion criteria. Data were extracted to construct 2×2 contingency tables for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A random-effects model was used for pooled estimation, and subgroup analyses were conducted. Summary receiver operating characteristic (SROC) curves were generated to summarize overall diagnostic performance.

RESULTS

The overall detection rate of PCa across studies was 57.3%. For detecting any PCa, mpMRI showed pooled sensitivity of 82% (95% CI, 80-83%) and specificity of 62% (95% CI, 60-64%), with positive likelihood ratio (LR) of 1.97 (95% CI, 1.71-2.26), negative LR of 0.28 (95% CI, 0.24-0.34), and diagnostic odds ratio (DOR) of 7.34 (95% CI, 5.60-9.63), and an area under the SROC curve of 0.81. For clinically significant PCa (csPCa), mpMRI had pooled sensitivity of 88% (95% CI, 87-90%) and specificity of 64% (95% CI, 63-66%), with positive LR of 2.49 (95% CI, 2.03-3.05), negative LR of 0.20 (95% CI, 0.16-0.25), DOR of 13.83 (95% CI, 9.14-20.9), and area under the curve of 0.90.

CONCLUSION

This meta-analysis suggests that mpMRI is effective in detecting PCa in biopsy-naive patients, particularly for csPCa. It can help reduce unnecessary biopsies and lower the risk of missing clinically significant cases, thereby guiding informed biopsy decisions.

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features

BACKGROUND

To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4) and avoid unnecessary biopsies.

METHODS

This study retrospectively analyzed 350 patients with suspicious prostate lesions from our institution who underwent 3.0 Tesla multiparametric magnetic resonance imaging (mpMRI) prior to biopsy (training set, n = 191, testing set, n = 83, and a temporal validation set, n = 76). Intratumoral and peritumoral volumes of interest (VOIintra, VOIperi)) were manually segmented by experienced radiologists on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Radiomic features were extracted separately from the VOIintra and VOIperi. After feature selection via the recursive feature elimination (RFE) algorithm, intratumoral radiomic score (intra-rad-score) and peritumoral radiomic score (peri-rad-score) were constructed. The clinical model, MRS model, and combined model integrating radiomic, clinicoradiological and metabolic features were constructed via the eXtreme Gradient Boosting (XGBoost) algorithm. The predictive performance of the models was evaluated in both the training and testing sets using receiver operating characteristic (ROC) curve analysis. SHapley Additive exPlanations (SHAP) analysis was applied to the combined model to visualize and interpret the prediction process.

RESULTS

A total of 350 patients were included, comprising 173 patients with csPCa (49.4%) and 177 patients with non-csPCa (50.6%). The intra-rad-score and peri-rad-score were constructed via 10 and 16 radiomic features. The combined model demonstrated the highest AUC, accuracy, F1 score, sensitivity, and specificity in the testing set (0.968, 0.928, 0.927, 0.932, and 0.923, respectively) and in the temporal validation set (0.940, 0.895, 0.890, 0.923, and 0.875, respectively). SHAP analysis revealed that the intra-rad-score, PSAD, peri-rad-score, and PI-RADS score were the most important predictors of the combined model.

CONCLUSION

We developed and validated a robust machine learning model incorporating intratumoral and peritumoral radiomic features, along with clinicoradiological and metabolic parameters, to accurately identify csPCa. The prediction process was visualized via SHAP analysis to facilitate clinical decision- making.

Precision in prostate cancer detection: integrating prostate-specific antigen density (PSAD) and the Prostate Imaging Reporting and Data System (PI-RADS) to provide additional risk stratification for a more accurate diagnostic decision

PURPOSE

This study focuses on integrating prostate-specific antigen density (PSAD) and Prostate Imaging Reporting and Data System (PI-RADS) for enhanced risk stratification in biopsy-naïve patients.

METHODS

A prospective study was conducted on 339 patients with suspected prostate cancer, utilizing PSAD and PI-RADS in combination. Logistic regression models were employed, and receiver operating characteristic (ROC) analysis performed to evaluate predictive performance. The patient cohort underwent multiparametric MRI, targeted biopsy, and systematic biopsy.

RESULTS

When patients were stratified into four PSAD risk groups, the rate of clinically significant prostate cancer (csPCa) increased significantly with higher PSAD levels. Logistic regression confirmed the independent contribution of PI-RADS and PSAD, highlighting their role in the prediction of csPCa. Combined models showed superior performance, as evidenced by the area under the curve (AUC) for PI-RADS category and PSAD (0.756), which exceeded that of the individual predictors (PSA AUC, 0.627, PI-RADS AUC 0.689, PSAD AUC 0.708).

CONCLUSION

This study concludes that combining PSAD and PI-RADS improves diagnostic accuracy and predictive value for csPCa in biopsy-naïve men, resulting in a promising strategy to provide additional risk stratification for more accurate diagnostic decision in biopsy-naïve patients, especially in the PI-RADS 3 group.

Oncological outcomes after radical prostatectomy of localized prostate cancer: stratified by magnetic resonance imaging and risk classification

Background

We investigated whether combining T2-weighted magnetic resonance imaging (MRI) findings and clinical risk categories improves upon established prognostic indicators of oncological outcomes in prostate cancer.

Methods

Patients who underwent radical prostatectomy, but not preoperative hormone therapy, radiotherapy, or chemotherapy, for localized prostate cancer at Seoul National University Bundang Hospital from October 2007 to April 2016 were included. MRIs were classified according to the Prostate Imaging-Reporting and Data System (PI-RADS). Patients were divided into the following five groups: 1, no focal suspicious lesion; 2, organ-confined suspicious lesion PI-RADS ≤3; 3, organ-confined suspicious lesion PI-RADS 4 or 5; 4, suspicious lesion with extraprostatic extension (EPE), no seminal vesicle invasion (SVI); 5, suspicious lesion with EPE and SVI. Risk classified according to the National Comprehensive Cancer Network (NCCN) and MRI findings were combined to analyze survival curves for biochemical recurrence (BCR)-free and metastasis-free survival. The area under a time-dependent receiver operating characteristic was analyzed for event prediction after 5 years.

Results

We analyzed 1,290 patients. In multivariate Cox regression models, PI-RADS ≥4 (hazard ratio [HR] 2.33, P < 0.001), EPE (HR 1.46, P = 0.027), SVI (HR 5.03, P < 0.001) and NCCN high-risk (HR 2.33, 95% CI 1.66-3.26, P < 0.001) were associated with BCR. For metastasis, EPE (HR 2.33, P = 0.047), SVI (HR 13.08, P < 0.001) and NCCN high-risk (HR 2.78, P = 0.026) were independent risk factors. Depending on MRI group, BCR-free survival significantly decreased in NCCN intermediate-risk (P = 0.001) and high-risk (P < 0.001) groups, and metastasis-free survival decreased in the intermediate-risk group (P = 0.39) and significantly decreased in the high-risk (P < 0.001) group. Adding MRI group to NCCN risk classification significantly improved the predictive accuracy for BCR in comparison with NCCN risk classification alone (P = 0.042), but not for metastasis (P = 0.012).

Conclusion

Combining prostate MRI with NCCN risk classification improves the prediction value of BCR following radical prostatectomy for localized prostate cancer.

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study

OBJECTIVES

To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives.

METHODS

One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions.

RESULTS

csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only.

CONCLUSION

Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

Target Volume Optimization for Localized Prostate Cancer

PURPOSE

To provide a comprehensive review of the means by which to optimize target volume definition for the purposes of treatment planning for patients with intact prostate cancer with a specific emphasis on focal boost volume definition.

METHODS

Here we conduct a narrative review of the available literature summarizing the current state of knowledge on optimizing target volume definition for the treatment of localized prostate cancer.

RESULTS

Historically, the treatment of prostate cancer included a uniform prescription dose administered to the entire prostate with or without coverage of all or part of the seminal vesicles. The development of prostate magnetic resonance imaging (MRI) and positron emission tomography (PET) using prostate-specific radiotracers has ushered in an era in which radiation oncologists are able to localize and focally dose-escalate high-risk volumes in the prostate gland. Recent phase 3 data has demonstrated that incorporating focal dose escalation to high-risk subvolumes of the prostate improves biochemical control without significantly increasing toxicity. Still, several fundamental questions remain regarding the optimal target volume definition and prescription strategy to implement this technique. Given the remaining uncertainty, a knowledge of the pathological correlates of radiographic findings and the anatomic patterns of tumor spread may help inform clinical judgement for the definition of clinical target volumes.

CONCLUSION

Advanced imaging has the ability to improve outcomes for patients with prostate cancer in multiple ways, including by enabling focal dose escalation to high-risk subvolumes. However, many questions remain regarding the optimal target volume definition and prescription strategy to implement this practice, and key knowledge gaps remain. A detailed understanding of the pathological correlates of radiographic findings and the patterns of local tumor spread may help inform clinical judgement for target volume definition given the current state of uncertainty.

The role of the radiologist in the prostate cancer multidisciplinary conference

The broad range of disease aggressiveness together with imperfect screening, diagnostic, and treatment options in prostate cancer (PCa) makes medical decision-making complex. The primary goal of a multidisciplinary conference is to improve patient outcomes by combining evidence-based data and expert opinion to discuss optimal management, including for those patients with challenging presentations. The primary purpose of the genitourinary imaging specialist in the prostate cancer multidisciplinary conference is to use imaging findings to reduce uncertainty by answering clinical questions. In this review, we discuss the role and the opportunities for an imaging specialist to add value in the care of men with prostate cancer discussed at multidisciplinary conferences.

The Role of Digital Rectal Examination for Early Detection of Significant Prostate Cancer in the Era of Magnetic Resonance Imaging

The role of digital rectal examination (DRE) in the early detection of significant prostate cancer (PCa) is being questioned in the era of magnetic resonance imaging (MRI). However, some men with suspected PCa may still be identified solely through DRE, even with low serum prostate-specific antigen (PSA) levels. Additionally, most predictive models designed to improve significant PCa diagnostic pathways incorporate DRE findings. We assessed the role of DRE among 5005 men with serum PSA levels > 3.0 ng/mL and/or suspicious DRE findings, who underwent pre-biopsy MRI and targeted and/or systematic biopsies, as part of the significant PCa opportunistic screening program in Catalonia (Spain) between 2016 and 2023. Significant PCa, defined as grade group > 2, was detected in 2097 men (41.9%). Suspicion of PCa was based solely on DRE in 206 cases (4.1%) with significant PCa detected in 50 of them (2.4%). Two pathways using the Barcelona predictive models, before and after MRI, with and without DRE findings showed specificities of 52.8 and 38.7%, respectively (p < 0.001), after fixing sensitivity at 90%. Prostate biopsy was avoided in 35.1 and 26.7%, respectively (p < 0.001), while its efficacy increased from 52.8 to 58%. We conclude that DRE improved the effectiveness of an opportunistic significant PCa-screening program.

Prostate cancer diagnostic pathway in men with lower urinary tract symptoms or performing opportunistic screening: The Italian Society of Urology (SIU) position paper

BACKGROUND

Voluntary PCa screening frequently results in excessive use of unnecessary diagnostic tests and an increasing risk of detection of indolent PCa and unaffordable costs for the various national health systems. In this scenario, the Italian Society of Urology (Società Italiana di Urologia, SIU) proposes an organized flow chart guiding physicians to improve early diagnosis of significant PCa avoiding unnecessary diagnostic tests and prostate biopsy.

METHODS

According to available evidence and international guidelines [i.e., European Association of Urology (EAU), American Association of Urology (AUA) and National Comprehensive Cancer Network (NCCN)] on PCa, a Panel of expert urologists selected by Italian Society of Urology (SIU, Società Italiana di Urologia) proposed some indications to develop a stepwise diagnostic pathway based on the diagnostic tests mainly used in the clinical practice. The final document was submitted to six expert urologists for external revision and approval. Moreover, the final document was shared with patient advocacy groups.

RESULTS

In voluntary men and symptomatic patients with elevated PSA value (>3 ng/mL), the Panel strongly discourage the use of antibiotic agents in absence of urinary tract infection confirmed by urine culture. DRE remains a key part of the urologic physical examination helping urologists to correctly interpret PSA elevation and prioritizing the execution of multiparametric Magnetic Resonance Imaging (mpMRI) in presence of suspicious PCa. Men with negative mpMRI and low clinical suspicion of PSA (PSA density < 0.20 ng/mL/cc, negative DRE findings, no family history) can be further monitored. Men with negative mpMRI and a higher risk of PCa (familial history, suspicious DRE, PSAD>0.20 ng/mL/cc or PSA>20 ng/mL) should be considered for systematic prostate biopsy. While PI-RADS 4-5 lesions represent a strong indication for prostate biopsy, PI-RADS 3 lesions should be further stratified according to PSAD values and prostate biopsy performed when PSAD is higher than 0.20. Accreditation, certification, and quality audits of radiologists and centers performing prostatic mpMRI should be strongly considered. The accessibility and/or the waiting list for MRI examinations should be also evaluated in the diagnostic pathway. The panel suggests performing transperineal or transrectal targeted plus systematic biopsies as standard of care.

CONCLUSIONS

Scientific societies must support the use of shared diagnostic pathway with the aim to increase the early detection of significant PCa reducing a delayed diagnosis of advanced PCa. Moreover, a shared diagnostic pathway can reduce the incorrect use of antibiotic, the number of unnecessary laboratory and radiologic examinations as well as of prostate biopsies.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models

OBJECTIVE

To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.

METHODS

Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.

RESULTS

The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.

CONCLUSIONS

Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.

CLINICAL RELEVANCE STATEMENT

The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.

KEY POINTS

• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Organized prostate cancer screening program: a proposal from the Italian Society of Urology (SIU)

To contrast opportunistic PCa screening, the European Union Council suggested extending screening programs to PCa by recommending the implementation of a stepwise approach in the EU Countries to evaluate the feasibility and effectiveness of an organized program based on PSA testing in combination with additional MRI as a follow-up test. The objective of this expert-based document is to propose an organized PCa screening program according to the EU Council recommendations. The Italian Society of Urology (SIU) developed a team of experts with the aim to report 1) the most recent epidemiologic data about incidence, prevalence, and mortality of PCa; 2) the most important risk factors to identify categories of men with an increased risk to eventually develop the disease; 3) the most relevant studies presenting data on population-based screening; and 4) the current recommendations of the leading International Guidelines. According to previous evidence, the Panel proposed some indications to develop a new organized PCa screening program for asymptomatic men with a life-expectancy of at least fifteen years. The SIU Panel strongly supports the implementation of a pilot, organized PCa screening program inviting asymptomatic men in the age range of 50-55 years. Invited men who are already performing opportunistic screening will be randomized to continue opportunistic screening or to cross into the organized protocol. Men with PSA level ≤3 ng/mL and familiarity for PCa received a DRE as well as all those with PSA levels >3 ng/mL. All other men with PSA levels greater than 3 ng/mL proceed to secondary testing represented by mpMRI. Men with Prostate Imaging-Reporting and Data System (PI-RADS) lesions 3 and PSAD 0.15 ng/mL/cc or higher as well as those with PI-RADS 4-5 lesions proceed to targeted plus systematic prostate biopsy. The primary outcome of the proposed pilot PCa screening program will be the detection rate of clinically significant PCa defined as a tumor with a ISUP Grade Group ≥2. Main secondary outcomes will be the detection rate of aggressive PCa (ISUP Grade Group ≥4); the detection rate of insignificant PCa (ISUP Grade Group 1); the number of unnecessary prostate biopsy avoided, the metastasis-free survival, and the overall survival. Men will be invited over a one-year period. Preliminary analyses will be planned 2 and 5 years after the baseline enrollment. According to the recent EU Council recommendations on cancer screening, pilot studies evaluating the feasibility and effectiveness of PCa screening programs using PSA as the primary and mpMRI as the secondary screening test in selected cohorts of patients must be strongly promoted by scientific societies and supported by national governments.

Robot-Assisted Radical Prostatectomy in PIRADS 5 Lesions Without Prior Biopsy: Is Biopsy Really Necessary in This Cohort?

Introduction: Conventionally, confirmation of clinically significant prostate cancer (csPCa) (Gleason grade group ≥ 2) involves an initial multiparametric magnetic resonance imaging (mpMRI) followed by biopsy. Prostate biopsy incurs inherent risks of infection, bleeding, patient discomfort, and a 6-week delay before robot-assisted laparoscopic radical prostatectomy (RALP). We explored the feasibility of immediate RALP in men with PIRADS 5 lesions without preceding biopsy. Methodology: After obtaining institutional review board approval, a prospective analysis was conducted on 235 patients with PIRADS 5 lesions on mpMRI from December 2018 to February 2023. Patients were divided into 2 groups as follows: Group NoBiopsy (biopsy not done before RALP, cases, n = 118) and Group YesBiopsy (biopsy done before RALP, controls, n = 117). Baseline preoperative, intraoperative, and postoperative parameters were analyzed. Functional outcomes were monitored at 1, 3-, 6-, 9-, and 12-months follow-up post-RALP. Statistical analysis was performed using SPSS and STATA. Results: Ninety-five percent of cases and 87.17% controls had csPCa on final pathology post-RALP. Multivariable analysis did not find significant association between biopsy status and csPCa. Abnormal digital rectal examination (DRE), family history, preoperative PSA, and MRI lesion volume predicted csPCa. Significant differences were observed in console time (NoBiopsy vs. YesBiopsy, 60 ± 10 vs. 70 ± 9 minutes, p < 0.001) and estimated blood loss (80 ± 20 vs. 100 ± 30 mL, p < 0.01) between groups. At 6 months post-RALP, 96% of men in Group NoBiopsy were continent, compared with 88% of men in Group YesBiopsy (p < 0.04). All men in the study cohort were continent (0 pads) at 12 months post-RALP. Ninety-eight percent of cases and 92% of controls at 9 months and 12 months, respectively, were able to have penetrative sex with or without PDE-5 inhibitors post-RALP. Conclusion: RALP without antecedent prostate biopsy in men with PIRADS 5 lesions demonstrated substantial csPCa detection rates and superior functional outcomes, warranting further validation.

The predictive impact of hematological inflammatory markers in detecting prostate cancer in patients with PI-RADS 3 lesions on multiparametric magnetic resonance imaging

BACKGROUND

The diagnostic accuracy of suspicious lesions that are classified as PI-RADS 3 in multiparametric prostate magnetic-resonance imaging (mpMRI) is controversial. This study aims to assess the predictive capacity of hematological inflammatory markers such as neutrophil-lymphocyte ratio (NLR), pan-immune-inflammation value (PIV), and systemic immune-response index (SIRI) in detecting prostate cancer in PI-RADS 3 lesions.

METHODS

276 patients who underwent mpMRI and subsequent prostate biopsy after PI-RADS 3 lesion detection were included in the study. According to the biopsy results, the patients were distributed to two groups as prostate cancer (PCa) and no cancer (non-PCa). Data concerning age, PSA, prostate volume, PSA density, PI-RADS 3 lesion size, prostate biopsy results, monocyte counts (109/L), lymphocyte counts (109/L), platelet counts (109/L), neutrophils count (109/L) were recorded from the complete blood count. From these data; PIV value is obtained by monocyte × neutrophil × platelet/lymphocyte, NLR by neutrophil/lymphocyte, and SIRI by monocyte number × NLR.

RESULTS

Significant variations in neutrophil, lymphocyte, and monocyte levels between PCa and non-PCa patient groups were detected (p = 0.009, p = 0.001, p = 0.005 respectively, p < 0.05). NLR, PIV, and SIRI exhibited significant differences, with higher values in PCa patients (p = 0.004, p = 0.001, p < 0.001 respectively, p < 0.05). The area under curve of SIRI was 0.729, with a cut-off value of 1.20 and with a sensitivity 57.70%, and a specificity of 68.70%.

CONCLUSION

SIRI outperformed NLR and PIV in detecting PCa in PI-RADS 3 lesions, showcasing its potential as a valuable biomarker. Implementation of this parameter to possible future nomograms has the potential to individualize and risk-stratify the patients in prostate biopsy decision.

Natural history of histologically benign PIRADS 4-5 lesions in multiparametric MRI: Real-life experience in an academic center

INTRODUCTION

The follow-up findings of patients who underwent prostate biopsy for prostate image reporting and data system (PIRADS) 4 or 5 multiparametric magnetic resonance imaging (mpMRI) findings and had benign histology were retrospectively reviewed.

METHODS

There were 190 biopsy-naive patients. Patients with at least 12 months of follow-up between 2012 and 2023 were evaluated. All MRIs were interpreted by two very experienced uroradiologists. Of the patients, 125 had either cognitive or software fusion MR-targeted biopsies with 4 + 8/10 cores. The remaining 65 patients had in-bore biopsies with 4-5 cores. Prostate-specific antigen (PSA) levels below 4 ng/mL were defined as PSA regression following biopsy. PIRADS 1-3 lesions on new MRI images were classified as MRI regression.

RESULTS

Median patient age and PSA were 62 (39-82) years and six (0.4-33) ng/mL, respectively, at the initial work-up. During a median follow-up period of 44 months, 37 (19.4%) patients were lost to follow-up. Of the remaining 153 patients, 82 (53.6%) had persistently high PSA. Among them, 72 (87.8%) had repeat mpMRI within 6-24 months which showed regressive findings (PIRADS 1-3) in 53 patients (73.6%) and PIRADS 4-5 index lesion persistence in 19 cases (26.4%). The latter group was recommended to have rebiopsy. Of these 19 patients, 16 underwent MRI-targeted rebiopsy. Prostate cancer was diagnosed in six (37.5%) patients and of these four (25%) were clinically significant (>Grade Group 1). Totally, clinically significant prostate cancer was detected in 4/153 (2.6%) patients followed up.

CONCLUSION

Patients should be warned against the relative relaxing effect of a negative biopsy after identification of PIRADS 4-5 index lesion. While PSA decrease was observed in many patients during follow-up, persistent MRI findings were present in nearly a quarter of patients with persistently high PSA. A rebiopsy is warranted in these patients, with significant prostate cancer diagnosed in a quarter of patients with rebiopsy.

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.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml

OBJECTIVES

To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy.

PATIENTS AND METHODS

Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit.

RESULTS

A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively).

CONCLUSION

The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

Select black men are potential candidates for prostate hemi-ablation based on radical prostatectomy histopathology for intermediate-risk prostate cancer-a multicenter SEARCH cohort study

IMPORTANCE AND OBJECTIVE

Partial gland ablation (PGA) is increasingly popular as a treatment for men with intermediate-risk prostate cancer (IR-PCa) to preserve functional outcomes while controlling their cancer. We aimed to determine the impact of race and clinical characteristics on the risk of upstaging (≥pT2c) and having adverse pathological outcomes including seminal vesicle invasion (SVI), extra prostatic extension (EPE) and lymph node invasion (LNI) at radical prostatectomy (RP) among men with IR disease eligible for PGA with hemi-ablation (HA).

DESIGN

Retrospective analysis.

SETTING

Multicenter.

PARTICIPANTS AND MEASURES

We studied patients diagnosed with unilateral IR-PCa treated with RP between 1988 and 2020 at 9 different Veterans Affairs hospitals within the SEARCH cohort. We analyzed differences in clinicopathological characteristics and outcome variables (odds of ≥pT2c and SVI, EPE and LNI) by race using multivariable logistic regression after adjusting for covariates.

RESULTS

Among 3127 patients, 33% were African American (AA) men with unilateral IR-PCa undergoing RP. Compared to non-AA men, AA individuals were younger (61 vs. 65 years, p < 0.001), presented with a higher prostate specific antigen (PSA) category (≥10 ng/ml; 34 vs. 26%, p < 0.001), and had a lower clinical stage (p < 0.001). Among the 2,798 (89.5%) with ≥pT2c stage, AA men exhibited higher ≥ pT2c rates (93 vs. 89%, p < 0.001), primarily due to increased pT2c staging (64 vs. 57%), where upstaging beyond pT2 was lower than non-AA men (29 vs. 32%). On multivariable analysis, AA men were found to have higher odds of ≥pT2c (odds ratio [OR]: 1.39 CI, 1.02-1.88, p = 0.04), lower odds of EPE (OR: 0.73 CI, 0.58-0.91, p < 0.01) and no statistically significant associations with LNI (OR: 0.79 CI, 0.42-1.46, p = 0.45) and SVI (OR: 1 CI, 0.74-1.35, p = 0.99) compared to non-AA men. On multivariable analysis, clinical features associated with higher odds of ≥pT2c were pre-operative PSA ≥ 15 (OR = 2.07, P = 0.01) and higher number of positive cores (HPC) on biopsy (OR = 1.36, P < 0.001). Similarly, PSA ≥ 15, Gleason grade ≥3 and HPC on biopsy were associated with higher odds of SVI, EPE and LNI, respectively.

CONCLUSIONS

In men with IR-PCa undergoing RP, AA men demonstrated an overall higher likelihood of ≥pT2c with lower upstaging beyond pT2, lower likelihood of EPE and no significant difference in likelihood of SVI and LNI compared to non-AA men. These findings support select AA men to be potential candidates for PGA, such as HA. Clinical factors are predictive of higher pathological stage and adverse pathological outcomes at RP and could be considered when selecting candidates for PGA.

Digital Rectal Exam in Prostate Cancer Screening and Elevated PSA Work-up-Is there a role anymore?

PURPOSE OF REVIEW

Prostate cancer (PCa) screening tools, particularly digital rectal examination (DRE), are under scrutiny. This review assesses the utility of DRE in PCa screening.

RECENT FINDINGS

Recent studies reaffirm the DRE's sensitivity and specificity, a higher PCa detection rate with PSA in conjunction with DRE, and a slightly elevated risk of clinically significant PCa (CSPC) in those with elevated PSA and suspicious DRE. Studies confirm high accuracy of MRI in identifying CSPC, with ongoing research exploring its screening potential. DRE alone lacks accuracy for PCa screening, often resulting in missed diagnoses and unnecessary biopsies. Its supplementary use with PSA marginally increases detection rates of identifying a small percentage of CSPC, but the benefit remains questionable. Emerging evidence suggests MRI has the potential as a superior screening tool compared to DRE, although direct comparative studies are lacking. Overall, the DRE has a limited role in current PCa screening.

Management of Patients With a Negative Multiparametric Prostate MRI Examination: AJR Expert Panel Narrative Review

Multiparametric MRI (mpMRI) of the prostate aids risk stratification of patients with elevated PSA levels. Although most clinically significant prostate cancers are detected by mpMRI, insignificant cancers are less evident. Thus, multiple international prostate cancer guidelines now endorse routine use of prostate MRI as a secondary screening test before prostate biopsy. Nonetheless, management of patients with negative mpMRI results (defined as PI-RADS category 1 or 2) remains unclear. This AJR Expert Panel Narrative Review summarizes the available literature on patients with an elevated screening PSA level and a negative prostate mpMRI result and provides guidance for these patients' management. Systematic biopsy should not be routinely performed after a negative mpMRI examination in patients at average risk but should be considered in patients at high risk. In patients who undergo PSA screening rather than systematic biopsy after negative mpMRI, clear triggers should be established for when to perform a repeat MRI examination. Patients with a negative MRI result followed by negative biopsy should follow their health care practitioners' preferred guidelines concerning subsequent PSA screening for the patient's risk level. Insufficient high-level data exist to support routine use of adjunctive serum or urine biomarkers, artificial intelligence, or PSMA PET to determine the need for prostate biopsy after a negative mpMRI examination.

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.

Interaction of patient age and high-grade prostate cancer on targeted biopsies of MRI suspicious lesions

OBJECTIVES

To evaluate the interaction of patient age and Prostate Imaging-Reporting and Data System (PI-RADS) score in determining the grade of prostate cancer (PCa) identified on magnetic resonance imaging (MRI)-targeted biopsy in older men.

PATIENTS AND METHODS

From a prospectively accrued Institutional Review Board-approved comparative study of MRI-targeted and systematic biopsy between June 2012 and December 2022, men with at least one PI-RADS ≥3 lesion on pre-biopsy MRI and no prior history of PCa were selected. Ordinal and binomial logistic regression analyses were performed.

RESULTS

A total of 2677 men met study criteria. The highest PI-RADS score was 3 in 1220 men (46%), 4 in 950 men (36%), and 5 in 507 men (19%). The median (interquartile range [IQR]) patient age was 66.7 (60.8-71.8) years, median (IQR) prostate-specific antigen (PSA) level was 6.1 (4.6-9.0) ng/mL, median (IQR) prostate volume was 48 (34-68) mL, and median (IQR) PSA density was 0.13 (0.08-0.20) ng/mL/mL. Clinically significant (cs)PCa and high-risk PCa were identified on targeted biopsy in 1264 (47%) and 321 (12%) men, respectively. Prevalence of csPCa and high-risk PCa were significantly higher in the older age groups. On multivariable analyses, patient age was significantly associated with csPCa but not high-risk PCa; PI-RADS score and the interaction of age and PI-RADS score were significantly associated with high-risk PCa but not csPCa.

CONCLUSION

In our cohort, the substantial rate of high-risk PCa on MRI-ultrasound fusion targeted biopsies in older men, and its significant association with MRI findings, supports the value of pre-biopsy MRI to localise disease that could cause cancer mortality even in older men.

Expect the unexpected: investigating discordant prostate MRI and biopsy results

OBJECTIVES

To evaluate discrepant radio-pathological outcomes in biopsy-naïve patients undergoing prostate MRI and to provide insights into the underlying causes.

MATERIALS AND METHODS

A retrospective analysis was conducted on 2780 biopsy-naïve patients undergoing prostate MRI at a tertiary referral centre between October 2015 and June 2022. Exclusion criteria were biopsy not performed, indeterminate MRI findings (PI-RADS 3), and clinically insignificant PCa (Gleason score 3 + 3). Patients with discrepant findings between MRI and biopsy results were categorised into two groups: MRI-negative/Biopsy-positive and MRI-positive/Biopsy-negative (biopsy-positive defined as Gleason score ≥ 3 + 4). An expert uroradiologist reviewed discrepant cases, retrospectively re-assigning PI-RADS scores, identifying any missed MRI targets, and evaluating the quality of MRI scans. Potential explanations for discrepancies included MRI overcalls (including known pitfalls), benign pathology findings, and biopsy targeting errors.

RESULTS

Patients who did not undergo biopsy (n = 1258) or who had indeterminate MRI findings (n = 204), as well as those with clinically insignificant PCa (n = 216), were excluded, with a total of 1102 patients analysed. Of these, 32/1,102 (3%) were classified as MRI-negative/biopsy-positive and 117/1102 (11%) as MRI-positive/biopsy-negative. In the MRI-negative/Biopsy-positive group, 44% of studies were considered non-diagnostic quality. Upon retrospective image review, target lesions were identified in 28% of cases. In the MRI-positive/Biopsy-negative group, 42% of cases were considered to be MRI overcalls, and 32% had an explanatory benign pathological finding, with biopsy targeting errors accounting for 11% of cases.

CONCLUSION

Prostate MRI demonstrated a high diagnostic accuracy, with low occurrences of discrepant findings as defined. Common reasons for MRI-positive/Biopsy-negative cases included explanatory benign findings and MRI overcalls.

CLINICAL RELEVANCE STATEMENT

This study highlights the importance of optimal prostate MRI image quality and expertise in reducing diagnostic errors, improving patient outcomes, and guiding appropriate management decisions in the prostate cancer diagnostic pathway.

KEY POINTS

• Discrepancies between prostate MRI and biopsy results can occur, with higher numbers of MRI-positive/biopsy-negative relative to MRI-negative/biopsy-positive cases. • MRI-positive/biopsy-negative cases were mostly overcalls or explainable by benign biopsy findings. • In about one-third of MRI-negative/biopsy-positive cases, a target lesion was retrospectively identified.

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.

Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center

PURPOSE

mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy.

METHODS

Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33-88 (range) (years)), PSA (7.2; 0.3-439 (ng/ml)), prostate volume (45; 10-300 (ml)), PSA density (0.15; 0.01-8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2-22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information.

RESULTS

Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa.

CONCLUSION

mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach.

A PSMA PET/CT-based risk model for prediction of concordance between targeted biopsy and combined biopsy in detecting prostate cancer

PURPOSE

This study is to investigate the diagnostic value of 68Ga-PSMA-11 in improving the concordance between mpMRI-TB and combined biopsy (CB) in detecting PCa.

METHODS

115 consecutive men with 68Ga-PSMA-11 PET/CT prior to prostate biopsy were included for analysis. PSMA intensity, quantified as maximum standard uptake value (SUVmax), minimum apparent diffusion coefficient (ADCmin) and other clinical characteristics were evaluated relative to biopsy concordance using univariate and multivariate logistic regression analyses. A prediction model was developed based on the identified parameters, and a dynamic online diagnostic nomogram was constructed, with its discrimination evaluated through the area under the ROC curve (AUC) and consistency assessed using calibration plots. To assess its clinical applicability, a decision curve analysis (DCA) was performed, while internal validation was conducted using bootstrapping methods.

RESULTS

Concordance between mpMRI-TB and CB occurred in 76.5% (88/115) of the patients. Multivariate logistic regression analyses performed that SUVmax (OR= 0.952; 95% CI 0.917-0.988; P= 0.010) and ADCmin (OR= 1.006; 95% CI 1.003-1.010; P= 0.001) were independent risk factors for biopsy concordance. The developed model showed a sensitivity, specificity, accuracy and AUC of 0.67, 0.78, 0.81 and 0.78 in the full sample. The calibration curve demonstrated that the nomogram's predicted outcomes closely resembled the ideal curve, indicating consistency between predicted and actual outcomes. Furthermore, the decision curve analysis (DCA) highlighted the clinical net benefit achievable across various risk thresholds. These findings were reinforced by internal validation.

CONCLUSIONS

The developed prediction model based on SUVmax and ADCmin showed practical value in guiding the optimization of prostate biopsy pattern. Lower SUVmax and Higher ADCmin values are associated with greater confidence in implementing mono-TB and safely avoiding SB, effectively balancing benefits and risks.

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.

Reducing the demand for magnetic resonance imaging scans and prostate biopsies during the early detection of clinically significant prostate cancer: Applying the Barcelona risk-stratified pathway in Catalonia

PURPOSE

To analyze the reduction in multiparametric magnetic resonance imaging (mpMRI) demand and prostate biopsies after the hypothetical implementation of the Barcelona risk-stratified pathway (BCN-RSP) in a population of the clinically significant prostate cancer (csCaP) early detection program in Catalonia.

MATERIALS AND METHODS

A retrospective comparation between the hypothetical application of the BCN-RSP and the current pathway, which relied on pre-biopsy mpMRI and targeted and/or systematic biopsies, was conducted. The BCN-RSP stratify men with suspected CaP based on a prostate specific antigen (PSA) level >10 ng/ml and a suspicious rectal examination (DRE), and the Barcelona-risk calculator 1 (BCN-RC1) to avoid mpMRI scans. Subsequently, candidates for prostate biopsy following mpMRI are selected based on the BCN-RC2. This comparison involved 3,557 men with serum PSA levels > 3.0 ng/ml and/or suspicious DRE. The population was recruited prospectively in 10 centers from January 2021 and December 2022. CsCaP was defined when grade group ≥ 2.

RESULTS

CsCaP was detected in 1,249 men (35.1%) and insignificant CaP was overdeteced in 498 (14%). The BCN-RSP would have avoid 705 mpMRI scans (19.8%), and 697 prostate biopsies (19.6%), while 61 csCaP (4.9%) would have been undetected. The overdetection of insignificant CaP would have decrease in 130 cases (26.1%), and the performance of prostate biopsy for csCaP detection would have increase to 41.5%.

CONCLUSION

The application of the BCN-RSP would reduce the demand for mpMRI scans and prostate biopsies by one fifth while less than 5% of csCaP would remain undetected. The overdetection of insignificant CaP would decrease by more than one quarter and the performance of prostate biopsy for csCaP detection would increase to higher than 40%.

Prediction of clinically significant prostate cancer by [68 Ga]Ga-PSMA-11 PET/CT: a potential tool for selecting patients for active surveillance

PURPOSE

In our study, our aim was to investigate the role of [68 Ga]Ga-PSMA-11 PET /CT imaging in the diagnosis of clinically significant prostate cancer (csPCa) (ISUP GG 2 and higher) in patients initially diagnosed with ISUP GG 1 and 2 after prostate biopsy.

MATERIALS AND METHODS

We retrospectively reviewed 147 patient records in whom [68 Ga]Ga-PSMA-11 PET/CT imaging was performed preoperatively. All patients were initially diagnosed with ISUP GG 1 and 2 PCa by biopsy. Final pathology reports were obtained after radical prostatectomy. The [68 Ga]Ga-PSMA-11 PET/CT images were evaluated to determine the PRIMARY score. Patients' mpMRI-PIRADS scores were also recorded when available and analyzed in correlation with the pathology results.

RESULTS

For the 114 patients scored using PRIMARY, 19 out of 37 patients with scores of 1 and 2 (51%) were diagnosed with csPCa. Of the 77 patients with PRIMARY scores between 3 and 5, 64 (83%) had csPCa. Notably, every patient with a PRIMARY score of 5 had csPCa. PRIMARY scoring had a sensitivity of 77% and specificity of 58%, with a positive predictive value of 83%. A moderate correlation was observed between PRIMARY scores and ISUP GG (Rho = 0.54, p < 0.001). In contrast, the PIRADS score displayed a sensitivity and specificity of 86% and 25% respectively, with a positive predictive value of 68%. No substantial correlation was found between PIRADS and ISUP GG. Statistical analysis revealed a significant correlation between PRIMARY and ISUP GG (p < 0.001), but not between PIRADS and ISUP GG (p = 0.281). Comparatively, PRIMARY scoring was significantly more reliable than PIRADS scoring in identifying csPCa.

CONCLUSION

[68 Ga]Ga-PSMA-11 PET/CT imaging is promising for distinguishing high-risk prostate cancer patients from those apt for active surveillance, potentially aiding in the identification of csPCa.

Shifting to transperineal prostate biopsy: A narrative review

To address the limitations and challenges associated with transrectal (TR) biopsy and to present transperineal (TP) biopsy as a viable and potentially safer alternative to TR biopsy. Prostate cancer (PCa) is a significant global health concern. The prevalence of advanced-stage prostate cancer in Asia is higher than that in the United States, emphasizing the need for effective screening and diagnosis methods. The gold standard of diagnosis is a TR biopsy. However, it has limitations due to the risk of infection and potential complications, such as injury to the rectal artery. Efforts have been made to address issues such as false-negative biopsies, under-sampling, and over-sampling through MRI-guided biopsies. However, the TR approach makes it difficult to access the apical and anterior regions of the prostate. TP biopsy has emerged as an alternative to address the limitations of TR biopsy. Nevertheless, a TP biopsy is a painful procedure, requiring the use of general anesthesia and expensive equipment. As a result, it has been perceived as costly and time-consuming. In addition, it requires a steep learning curve. The introduction of local anesthesia such as pudendal nerve block and the adoption of freehand techniques have contributed to the feasibility of performing TP biopsy. Recent research indicates that freehand TP biopsy can yield comparable diagnostic results to template-guided approaches. The diagnostic performance, cancer detection rates, and complication rates of TP biopsy have demonstrated its potential as a safe and effective diagnostic method.

Do we need MRI in all biopsy naïve patients? A multicenter cohort analysis

PURPOSE

The combined approach (CB) of magnetic resonance imaging (MRI)-guided biopsy (TB) and systematic biopsy (SB) is strongly recommended based on numerous studies in biopsy naïve men with suspicion of clinically significant prostate cancer (csPCA). However, the unbalanced accessibility of MRI, challenges related to reimbursement and the scarcity of specialized medical practitioners continue to impede a widespread implementation. Therefore, our objective was to determine a subset of men that could undergo SB without an increased risk of underdiagnosis at reduced expenses.

METHODS

A multicenter analysis of 2714 men with confirmed PCA and suspicious MRI who underwent CB were enrolled. Cancer detection rates were compared between the different biopsy routes SB, TB and CB using McNemar paired test. Additionally, Gleason grade up- and down-grading was determined.

RESULTS

CB detected more csPCA than TB and SB (p < 0.001), irrespective of MRI findings or biopsy route (transperineal vs. transrectal). Thereby, single biopsy approaches misgraded > 50% of csPCA. TB showed higher diagnostic efficiency, defined as csPCA detection per biopsy core than CB and SB (p < 0.001). For patients with abnormal DRE and PSA levels > 12.5 ng/ml, PSAD > 0.35 ng/ml/cm3, or > 75 years, SB and CB showed similar csPCA detection rates.

CONCLUSION

Conducting CB provides the highest level of diagnostic certainty and minimizes the risk of underdiagnosis in almost all biopsy-naive men. However, in patients with suspicious DRE and high PSA levels, PSAD, or advanced age solely using SB leads to similar csPCA detection rates. Thus, a reduced biopsy protocol may be considered for these men in case resources are limited.

Investigating Efficient Risk-Stratified Pathways for the Early Detection of Clinically Significant Prostate Cancer

Risk-stratified pathways (RSPs) are recommended by the European Association of Uro-logy (EAU) to improve the early detection of clinically significant prostate cancer (csPCa). RSPs can reduce magnetic resonance imaging (MRI) demand, prostate biopsies, and the over-detection of insignificant PCa (iPCa). Our goal is to analyze the efficacy and cost-effectiveness of several RSPs by using sequential stratifications from the serum prostate-specific antigen level and digital rectal examination, the Barcelona risk calculators (BCN-RCs), MRI, and Proclarix™. In a cohort of 567 men with a serum PSA level above 3.0 ng/mL who underwent multiparametric MRI (mpMRI) and targeted and/or systematic biopsies, the risk of csPCa was retrospectively assessed using Proclarix™ and BCN-RCs 1 and 2. Six RSPs were compared with those recommended by the EAU that, stratifying men from MRI, avoided 16.7% of prostate biopsies with a prostate imaging-reporting and data system score of <3, with 2.6% of csPCa cases remaining undetected. The most effective RSP avoided mpMRI exams in men with a serum PSA level of >10 ng/mL and suspicious DRE, following stratifications from BCN-RC 1, mpMRI, and Proclarix™. The demand for mpMRI decreased by 19.9%, prostate biopsies by 19.8%, and over-detection of iPCa by 22.7%, while 2.6% of csPCa remained undetected as in the recommended RSP. Cost-effectiveness remained when the Proclarix™ price was assumed to be below EUR 200.

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.

Diagnostic accuracy of multiparametric ultrasound in the diagnosis of prostate cancer: systematic review and meta-analysis

OBJECTIVES

Ultrasound (US) technology has recently made advances that have led to the development of modalities including elastography and contrast-enhanced ultrasound. The use of different US modalities in combination may increase the accuracy of PCa diagnosis. This study aims to assess the diagnostic accuracy of multiparametric ultrasound (mpUS) in the PCa diagnosis.

METHODS

Through September 2023, we searched through Cochrane CENTRAL, PubMed, Embase, Scopus, Web of Science, ClinicalTrial.gov, and Google Scholar for relevant studies. We used standard methods recommended for meta-analyses of diagnostic evaluation. We plot the SROC curve, which stands for summary receiver operating characteristic. To determine how confounding factors affected the results, meta-regression analysis was used.

RESULTS

Finally, 1004 patients from 8 studies that were included in this research were examined. The diagnostic odds ratio for PCa was 20 (95% confidence interval (CI), 8-49) and the pooled estimates of mpUS for diagnosis were as follows: sensitivity, 0.88 (95% CI, 0.81-0.93); specificity, 0.72 (95% CI, 0.59-0.83); positive predictive value, 0.75 (95% CI, 0.63-0.87); and negative predictive value, 0.82 (95% CI, 0.71-0.93). The area under the SROC curve was 0.89 (95% CI, 0.86-0.92). There was a significant heterogeneity among the studies (p < 0.01). According to meta-regression, both the sensitivity and specificity of mpUS in the diagnosis of clinically significant PCa (csPCa) were inferior to any PCa.

CONCLUSION

The diagnostic accuracy of mpUS in the diagnosis of PCa is moderate, but the accuracy in the diagnosis of csPCa is significantly lower than any PCa. More relevant research is needed in the future.

CRITICAL RELEVANCE STATEMENT

This study provides urologists and sonographers with useful data by summarizing the accuracy of multiparametric ultrasound in the detection of prostate cancer.

KEY POINTS

• Recent studies focused on the role of multiparametric ultrasound in the diagnosis of prostate cancer. • This meta-analysis revealed that multiparametric ultrasound has moderate diagnostic accuracy for prostate cancer. • The diagnostic accuracy of multiparametric ultrasound in the diagnosis of clinically significant prostate cancer is significantly lower than any prostate cancer.