What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel

Variation in Prostate Magnetic Resonance Imaging Performance: Data from the Prostate Biopsy Collaborative Group

BACKGROUND AND OBJECTIVE

The quality and reporting of prostate magnetic resonance imaging (MRI) are operator dependent, leading to variations in estimates such as positive predictive value across sites. This impacts patient counseling, risk modeling, and risk calculators. This study assessed variation in Prostate Imaging Reporting and Data System (PI-RADS) score classification and subsequent probability of grade group (GG) ≥2 + prostate cancer.

METHODS

Data from the Prostate Biopsy Collaborative Group, including multiple sites in North America, Europe, and Asia Pacific, were analyzed. Patients underwent multiparametric MRI (mpMRI) of the prostate followed by prostate biopsy during the years 2010-2023. Only those with MRI-targeted biopsy and PI-RADS score ≥3 were included. The risk of being assigned PI-RADS 4 or 5 and risk of GG ≥2 disease for these scores were estimated using logistic regression.

KEY FINDINGS AND LIMITATIONS

The cohort included 7325 biopsies from 7320 unique patients from 13 sites. A two-fold variation in the probability of PI-RADS 4 or 5 assignment across sites persisted even after adjustment for patient risk (heterogeneity p < 0.001 for both). There were significant differences in the absolute risk of GG ≥2 disease for PI-RADS 4 and 5 (heterogeneity p < 0.001 for both), varying between 23% and 68% and between 49% and 87%, respectively. The use of prostate biopsy as a reference standard has limitations but reflects typical usage of mpMRI in clinical practice.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The probability of being assigned PI-RADS 4 or 5 and subsequent detection of GG ≥2 disease varies widely between institutions. This impacts counseling, risk stratification, and clinical practice, necessitating better standardization in the performance and interpretation of mpMRI.

External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer Detection

PURPOSE

Prostate imaging reporting and data systems (PI-RADS) experiences considerable variability in inter-reader performance. Artificial Intelligence (AI) algorithms were suggested to provide comparable performance to PI-RADS for assessing prostate cancer (PCa) risk, albeit tested in highly selected cohorts. This study aimed to assess an AI algorithm for PCa detection in a clinical practice setting and simulate integration of the AI model with PI-RADS for assessment of indeterminate PI-RADS 3 lesions.

PATIENTS AND METHODS

This retrospective cohort study externally validated a biparametric MRI-based AI model for PCa detection in a consecutive cohort of patients who underwent prostate MRI and subsequently targeted and systematic prostate biopsy at a urology clinic between January 2022 and March 2024. Radiologist interpretations followed PI-RADS v2.1, and biopsies were conducted per PI-RADS scores. The previously developed AI model provided lesion segmentations and cancer probability maps which were compared to biopsy results. Additionally, we conducted a simulation to adjust biopsy thresholds for index PI-RADS category 3 studies, where AI predictions within these studies upgraded them to PI-RADS category 4.

RESULTS

Among 144 patients with a median age of 70 years and PSA density of 0.17ng/mL/cc, AI's sensitivity for detection of PCa (86.6%) and clinically significant PCa (csPCa, 88.4%) was comparable to radiologists (85.7%, p=0.84, and 89.5%, p=0.80, respectively). The simulation combining radiologist and AI evaluations improved clinically significant PCa sensitivity by 5.8% (p=0.025). The combination of AI, PI-RADS and PSA density provided the best diagnostic performance for csPCa (area under the curve [AUC]=0.76).

CONCLUSION

The AI algorithm demonstrated comparable PCa detection rates to PI-RADS. The combination of AI with radiologist interpretation improved sensitivity and could be instrumental in assessment of low-risk and indeterminate PI-RADS lesions. The role of AI in PCa screening remains to be further elucidated.

Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study

BACKGROUND

Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-support ML model for classifying patients with biopsy-negative (cancer-free), clinically significant, and non-clinically significant prostate cancer across two prostate-specific antigen (PSA) cut-offs ≤ 10 ng/ml and > 10 ng/ml.

METHODS

The data for the current study were retrieved from the records of two main hospitals in Riyadh, Saudi Arabia from July 2018 through July 2024. Six machine learning algorithms were employed, and the dataset was randomly divided into a training set and a validation set at a ratio of 8:2. The following metrics were used as performance indicators across the six algorithms: Accuracy, Precision, Recall, F1-score, and area under the curve. Recent data from the two hospitals was utilized for external validation.

RESULTS

The metrics for Random Forest, Extra Tree, and Decision Tree algorithms showed excellent capability in classifying the outcomes of prostate biopsy for the two PSA cut-offs. However, the metrics for the PSA cut-off > 10 ng/ml are higher than those for PSA ≤ 10 ng/ml. For the three-class classification, the accuracy and area under the curve for the cut-off > 10 ng/ml were 0.96 and 0.99, respectively. While for the cut-off ≤ 10 ng/ml they were 0.92 and 0.94 for Random Forest and 0.94 and 0.95 for the Extra Tree algorithm. The metrics of non-clinically significant and biopsy-negative cases outperformed those of clinically significant cases.

CONCLUSION

ML models are proving to be effective tools in differentiating between prostate biopsy outcomes, enhancing diagnostic accuracy, and potentially transforming clinical practices in prostate cancer management.

Correlation of Prostate High-Resolution Microultrasound With Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To assess the correlation between high-resolution microultrasound (microUS) and multiparametric magnetic resonance imaging (MP-MRI) in clinically significant prostate cancer (csPCa) lesion identification.

METHODS

We reviewed our prospectively maintained database of 267 consecutive patients who underwent MP-MRI and transperineal microUS-guided biopsy between February 2021 and April 2023. The Prostate Risk Identification using MicroUS (PRI-MUS) protocol was utilized to risk stratify prostate lesions, with PRI-MUS 3-5 defined as positive. MRI lesions were classified according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Clinicopathologic outcomes were analyzed. Spearman correlation testing was computed to assess the relationship between PRI-MUS and PI-RADS.

RESULTS

A total of 161 patients met inclusion criteria. Mean±standard deviation age was 65.6±1.5years and prostate-specific antigen was 7.6±0.6ng/mL. Ninety-two patients were found to have PIRADS 3-5 lesions. Spearman correlation analysis revealed a moderate positive correlation between PRI-MUS and PI-RADS (r=0.40, P<.001). MicroUS-targeted cores detected higher grade disease than systematic and MRI-targeted cores in 8/161 (5.0%) patients. CsPCa would have been missed in 4/161 (2.5%) patients without microUS-targeted sampling.

CONCLUSION

MicroUS/PRI-MUS demonstrates moderate positive correlation with MP-MRI/PI-RADS and offers improved csPCa detection compared to MRI-targeted biopsy alone. MicroUS may be useful in conjunction with MP-MRI or as an alternative imaging modality in MRI-ineligible patients.

Correlation of clinically significant prostate cancer sites across multiparametric MRI, prostate biopsy, and whole-mount pathology for optimal prostate biopsy strategy

Data from patients with suspicious lesions on multiparametric magnetic resonance (mpMRI) and a diagnosis of clinically significant prostate cancer (csPCa) who underwent radical prostatectomy (RP) were collected. The aim was to compare csPCa sites identified through whole-mount pathological analysis (WMA) after RP with PI-RADS ≥ 3 lesions identified on mpMRI, and with csPCa foci detected through targeted-biopsy (TB) or combined targeted + systematic biopsy (TSB). A paired Student's t-test and Pearson correlation analysis were performed to evaluate the agreement between these diagnostic methods. A total of 106 patients were included in the TSB group and 95 in the TB group. The correlation between mpMRI, PB, and WMA was moderate and comparable in both groups. No correlation between PB and WMA was found in the TB group for PI-RADS3 lesions, while a moderate-strong correlation was observed when comparing mpMRI, PB, and WMA for PI-RADS > 3 lesions. About 50% of csPCa sites remained undetected by mpMRI. TSB was able to identify 16.5% more csPCa sites than TB. mpMRI is an accurate method in the diagnosis of PCa, especially for PI-RADS > 3 lesions, although some csPCa sites remained undetected. The use of TSB improved the location agreement between PB and WMA, increasing detection rates up to 79%.

Epidemiology, diagnosis and treatment of anterior prostate cancer

Anterior prostate cancers (APCs) are a group of impalpable neoplasms located in regions anterior to the urethra, which comprise the transition zone, apical peripheral zone and anterior fibromuscular stroma. These regions are typically undersampled using conventional biopsy schemes, leading to a low detection rate for APC and a high rate of false negatives. Radical prostatectomy series suggest prevalence rates of at least 10-30%, but transperineal systematic biopsy is ideal for diagnosis, particularly where multiparametric MRI is unavailable. Combined MRI-targeted and systematic biopsies demonstrate high concordance with final histopathology and lead to the fewest incidences of upgrading and upstaging at radical prostatectomy. Thus, the use of combined biopsy techniques has important implications for preoperative work-up and surgical planning, as APCs are associated with larger cancer volumes and a higher rate of positive surgical margins than posterior prostate cancer. Nevertheless, anterior tumour location might confer a relative resistance to stage progression, as APCs exhibit lower rates of extraprostatic extension, seminal vesical invasion and lymph node metastases than the more commonly seen posterior neoplasms. Few studies have examined the long-term outcomes of partial gland approaches to APCs, but MRI-targeted techniques have the potential to provide real-time intraoperative guidance and maximize the oncological safety of anterior focal treatment options in patients with APC.

Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men

OBJECTIVE

Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing  clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process. In this study, we aimed to externally validate a previously developed urine and plasma biomarker test in a biopsy-naïve population.

MATERIALS AND METHODS

Urine and blood samples were prospectively collected from 362 biopsy-naïve men with suspected PC before they underwent transrectal prostate biopsies. The expression levels of a 10-gene mRNA panel were quantified using reverse transcription/quantitative polymerase chain reaction of both urine and plasma. These gene expression levels, combined with clinical features and plasma prostate-specific antigen (PSA) levels, were used to predict the presence of International Society of Urological Pathology grade group ≥ 2 PC.

RESULTS

Complete data were available for 314 patients. The sensitivity and specificity of the biomarker test were 87% (95% CI: 79-93%) and 42% (95% CI: 36-49%), respectively. The area under the curve was 0.76 (95% CI: 0.7-0.82) for the biomarker test probability and 0.65 (95% CI: 0.59-0.72) for PSA (p = 0.02). The test's negative predictive value was 89% (CI: 81-94%).

CONCLUSION

This study did not replicate the previously reported high accuracy of the biomarker test, highlighting the need for further refinement and robust external validation to ensure reliable performance across diverse patient populations.

A contemporary review: mpMRI in prostate cancer screening and diagnosis

Prostate cancer (PCa) screening has evolved beyond PSA and digital rectal exam to include multiparametric prostate MRI (mpMRI). Incorporating this advanced imaging tool has further limited the well-established problem of overdiagnosis, aiding in the identification of higher grade, clinically significant cancers. For this reason, mpMRI has become an important part of the diagnostic pathway and is recommended across guidelines in biopsy naïve patients or for patients with prior negative biopsy. This contemporary review evaluates the most recent literature on the role of mpMRI in the screening and diagnosis of prostate cancer. Barriers to utilization of mpMRI still exist including variable access, high cost, and requisite expertise, encouraging evaluation of novel techniques such as biparametric MRI. Future screening and diagnostic practice patterns will undoubtedly evolve as our understanding of novel biomarkers and artificial intelligence improves.

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.

Diagnostic Performance of Multiparametric MRI for Detection of Prostate Cancer After Focal Therapy

BACKGROUND

Minimally invasive focal therapy of low- to intermediate-risk prostate cancer is becoming more common and has demonstrated lower morbidity compared to other treatments. Multiparametric prostate magnetic resonance imaging (mpMRI) has the potential to be an effective posttreatment evaluation method for residual/recurrent neoplasm.

OBJECTIVE

This study aimed to evaluate the ability of mpMRI to detect residual/recurrent neoplasm after focal therapy treatment of prostate cancer using a 3-point Likert scale.

METHODS

This retrospective study included patients who underwent focal therapy utilizing cryoablation, high-frequency ultrasound, and radiofrequency ablation for low- to intermediate-risk prostate cancer with baseline mpMRI and biopsy and a 6- to 12-month follow-up mpMRI and biopsy. Three abdominal fellowship-trained readers were asked to evaluate the follow-up mpMRI utilizing a 3-point Likert scale based on the level of suspicion as "nonviable," "equivocal," or "viable." Diagnostic statistics and Light's κ for interreader variability were calculated.

RESULTS

A total of 142 patients were included (mean age, 65 ± 7 years). When considering "equivocal" or "viable" as positive, the overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) for detecting recurrent grade group (GG) 2 or greater disease for Reader 1 were 0.47, 0.83, 0.24, 0.93, and 0.65; for Reader 2, 0.73, 0.75, 0.26, 0.96, and 0.74; and for Reader 3, 0.73, 0.57, 0.17, 0.95, and 0.65. When considering "viable" as positive, the overall sensitivity, specificity, PPV, NPV, and AUC for Reader 1 were 0.47, 0.92, 0.41, 0.94, and 0.69; for Reader 2, 0.33, 0.97, 0.56, 0.93, and 0.65; and for Reader 3, 0.53, 0.84, 0.29, 0.94, and 0.69. κ was 0.39.

CONCLUSIONS

This study suggests that DCE and DWI are the most important sequences in mpMRI and demonstrates the efficacy of utilizing a 3-point grading system in detecting and diagnosing prostate cancer after focal therapy.

CLINICAL IMPACT

mpMRI can be used to monitor for residual/recurrent disease after focal therapy.

Comparison of tracer kinetic models in differentiating malignant from normal prostate tissue using dynamic contrast-enhanced MRI

Purpose

The aim of this study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived kinetic parameters with high spatiotemporal resolution in discriminating malignant from normal prostate tissues.

Methods

Fifty patients with suspicious of malignant diseases in prostate were included in this study. Regions of interest (ROI) were manually delineated by experienced radiologists. Voxel-wise kinetic parameters were produced with the following tracer kinetic models (TKMs): Tofts model, extended Tofts model (ETM), Brix's conventional two-compartment model (Brix), adiabatic tissue homogeneity model (ATH), and distributed parameter model (DP). The initial area under the signal-time curve (IAUC) with an uptake integral approach was also included. Mann-Whitney U test and receiver operating characteristic (ROC) curves were used to evaluate the capability of distinguishing tumor lesions from normal tissues. A p-value of 0.05 or less is considered statistically significant. ROI based parameters correlation analysis between DP and ETM were performed.

Results

624 lesions and 269 normal tissue ROIs were obtained. Thirty parameters were derived from the six kinetic models. Except for PS from Brix, statistically significant differences between lesions and normal tissues (P<0.05) were observed in other parameters.Ve from DP, ATH and Brix and PS from ATH have AUC values less than 0.6 in the ROC analysis. MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, IAUC parameters and F from Brix have AUC values larger than 0.8. Ve and Vp from DP and ETM are correlated (r> 0.65). The correlation coefficient between Ktrans from ETM and PS from DP is 0.751.

Conclusion

MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, F from Brix and IAUC parameters can be used to differentiate malignant lesions from normal tissues in the prostate.

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.

Predictors of Contralateral Disease in Men With Unilateral Lesions on Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To evaluate predictors of contralateral clinically significant prostate cancer (csPCa) in men with biopsy-proven unilateral lesions on magnetic resonance imaging (MRI).

METHODS

We retrospectively identified men with no prior diagnosis of PCa with unilateral biopsy-confirmed csPCa within PI-RADS 2-5 lesions within our institutional biopsy database. Multivariate logistic regression was used to identify clinical predictors of contralateral disease.

RESULTS

Four hundred ninety men met study inclusion criteria, of which 385 men (78.6%) had no contralateral csPCa and 105 men (21.4%) had contralateral csPCa (Fig. 1). Prior negative biopsy (OR 0.34 [0.14, 0.75], P = .012), prostate-specific antigen density (OR 18.8 [2.77, 249], P = .017), and tumor location in the transverse plane ("Posterior": OR 1.93 [1.02, 3.87], P = .048; "Throughout Transverse Plane": OR 6.56 [2.26, 19.6], P < .001) were significantly associated with contralateral csPCa in multivariate logistic regression models. However, there appear to be no attributes within the MRI-targeted tumor that reliably predict contralateral csPCa (Table 2).

CONCLUSION

Approximately 20% of men with unilateral MRI findings and csPCa on targeted biopsy were found to have contralateral csPCa on systematic biopsy (SB). Prior negative biopsy was associated with a decreased odds of contralateral csPCa. Prostate-specific antigen density and tumor in the posterior aspect of or throughout the transverse plane were associated with increased odds of contralateral csPCA. Consideration of these clinical factors may afford an opportunity to only use SB in cases in which the odds of contralateral csPCa are high.

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer

BACKGROUND

Multiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited.

PURPOSE

To investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1.

STUDY TYPE

Retrospective.

SUBJECTS

One thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022.

FIELD STRENGTH/SEQUENCE

3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences.

ASSESSMENT

Integrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated.

STATISTICAL TESTS

A weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05.

RESULTS

A total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively.

DATA CONCLUSION

Spatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa were studied. Our results demonstrated that there is a spatial correlation between mpMRI performance and locations of PCa on the prostate.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Advanced Imaging for Localized Prostate Cancer

BACKGROUND/OBJECTIVES

Prostate cancer is a prevalent malignancy often presenting without early symptoms. Advanced imaging technologies have revolutionized its diagnosis and management. This review discusses the principles, benefits, and clinical applications of multiparametric magnetic resonance imaging (mpMRI), micro-ultrasound (microUS), and prostate-specific membrane antigen positron emission tomography-computed tomography (PSMA PET/CT) in localized prostate cancer.

METHODS

We conducted a comprehensive literature review of recent studies and guidelines on mpMRI, microUS, and PSMA PET/CT in prostate cancer diagnosis, focusing on their applications in biopsy-naïve patients, those with previous negative biopsies, and patients under active surveillance.

RESULTS

MpMRI has demonstrated high sensitivity and negative predictive value in detecting clinically significant prostate cancer (csPCa). MicroUS, a newer technology, has shown promising results in early studies, with sensitivity and specificity comparable to mpMRI. PSMA PET/CT has emerged as a highly sensitive and specific imaging modality, particularly valuable for staging and detecting metastatic disease. All three technologies have been incorporated into urologic practice for prostate cancer diagnosis and management, with each offering unique advantages in different clinical scenarios.

CONCLUSIONS

Advanced imaging techniques, including mpMRI, microUS, and PSMA PET/CT, have significantly improved the accuracy of prostate cancer diagnosis, staging, and management. These technologies enable more precise targeting of suspicious lesions during biopsy and therapy planning. However, further research, especially randomized controlled trials, is needed to fully establish the optimal use and inclusion of these imaging modalities in various stages of prostate cancer care.

The efficacy of different biomarkers and endpoints to refine referrals for suspected prostate cancer: the TARGET study (Tiered integrAted tests for eaRly diaGnosis of clinically significant ProstatE Tumours)

BACKGROUND

The majority of men referred with a raised PSA for suspected prostate cancer will receive unnecessary tertiary investigations including MRI and biopsy. Here, we compared different types of biomarkers to refine tertiary referrals and when different definitions of clinically significant cancer were used.

METHODS

Data and samples from 798 men referred for a raised PSA (≥ 3 ng/mL) and investigated through an MRI-guided biopsy pathway were accessed for this study. Bloods were acquired pre-biopsy for liquid biomarkers and germline DNA. Variables explored included PSA + Age (base model), free/total PSA (FTPSA), Prostate Health Index (phi), PSA density (PSAd), polygenic risk score (PRS) and MRI (≥ LIKERT 3). Different diagnostic endpoints for significant cancer (≥ grade group 2 [GG2], ≥ GG3, ≥ Cambridge Prognostic Group 2 [CPG2], ≥ CPG3) were tested. The added value of each biomarker to the base model was evaluated using logistic regression models, AUC and decision curve analysis (DCA) plots.

RESULTS

The median age and PSA was 65 years and 7.13 ng/mL respectively. Depending on definition of clinical significance, ≥ grade group 2 (GG2) was detected in 57.0% (455/798), ≥ GG3 in 27.5% (220/798), ≥ CPG2 in 61.6% (492/798) and ≥ CPG3 in 42.6% (340/798). In the pre-MRI context, the PSA + Age (base model) AUC for prediction of ≥ GG2, ≥ GG3, ≥ CPG2 and ≥ CPG3 was 0.66, 0.68, 0.70 and 0.75 respectively. Adding phi and PSAd to base model improved performance across all diagnostic endpoints but was notably better when the composite CPG prognostic score was used: AUC 0.82, 0.82, 0.83, 0.82 and AUC 0.74, 0.73, 0.79, 0.79 respectively. In contrast, neither FTPSA or PRS scores improved performance especially in detection of ≥ GG3 and ≥ CPG3 disease. Combining biomarkers did not alter results. Models using phi and PSAd post-MRI also improved performances but again benefit varied with diagnostic endpoint. In DCA analysis, models which incorporated PSAd and phi in particular were effective at reducing use of MRI and/or biopsies especially for ≥ CPG3 disease.

CONCLUSION

Incorporating phi or PSAd can refine and tier who is referred for tertiary imaging and/or biopsy after a raised PSA test. Incremental value however varied depending on the definition of clinical significance and was particularly useful when composite prognostic endpoints are used.

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.

Negative magnetic resonance imaging cannot be used to omit an initial prostate biopsy - An ambispective study

Introduction

Up to 40% of patients with suspected prostate cancer (PCa) have a negative prebiopsy magnetic resonance imaging (nMRI), and up to 15% of them may have clinically significant PCa (csPCa). The ability to predict the presence of csPCa despite nMRI may help avoid unnecessary biopsies. We aimed to determine the negative predictive value (NPV) of mpMRI, the influence of MRI reporting patterns in clinical practice, and the factors that might predict csPCa among men with an nMRI.

Methodology

In an IRB-approved, ambispective study, men who underwent prostate biopsy from 2016 to 2023 and had a prebiopsy MRI, were included to determine the presence of csPCa. The reporting patterns of institutional and noninstitutional MRI were evaluated. Age, digital rectal examination (DRE) findings, prostate specific antigen (PSA), PSA density (PSAD), and MRI reports were evaluated for their ability to predict csPCa in men with nMRI.

Results

1660 patients who underwent prostate biopsy were assessed for eligibility, and 685 patients were enrolled in the study. The median age, PSA and PSAD were 60 years, 11.63 ng/ml and 0.23 ng/ml/cm3, respectively. 62 (9%) men had an nMRI, among which csPCa, non-csPCa, and negative biopsy were found in 34%, 5%, and 61% of men, respectively. 61% had an institutional MRI, while 39% had a noninstitutional MRI. The sensitivity and NPV of any MRI for csPCa were 93% and 66%, respectively, which improved to 96% and 81% for institutional MRI. Univariate and multivariate analyses showed abnormal DRE and PSAD ≥0.25 ng/ml/cc as predictive factors for csPCa in men with an nMRI.

Conclusion

34% of men with negative MRIs were found to harbor csPCa on prostate biopsy. The NPV of institutional MRI was higher than for noninstitutional MRI. Men with an abnormal DRE or PSAD ≥0.25 ng/ml/cc had a higher incidence of csPCa despite an nMRI.

Targeted Prostate Biopsy: How, When, and Why? A Systematic Review

OBJECTIVE

Prostate cancer, the second most diagnosed cancer among men, requires precise diagnostic techniques to ensure effective treatment. This review explores the technological advancements, optimal application conditions, and benefits of targeted prostate biopsies facilitated by multiparametric magnetic resonance imaging (mpMRI).

METHODS

A systematic literature review was conducted to compare traditional 12-core systematic biopsies guided by transrectal ultrasound with targeted biopsy techniques using mpMRI. We searched electronic databases including PubMed, Scopus, and Web of Science from January 2015 to December 2024 using keywords such as "targeted prostate biopsy", "fusion prostate biopsy", "cognitive prostate biopsy", "MRI-guided biopsy", and "transrectal ultrasound prostate biopsy". Studies comparing various biopsy methods were included, and data extraction focused on study characteristics, patient demographics, biopsy techniques, diagnostic outcomes, and complications.

CONCLUSION

mpMRI-guided targeted biopsies enhance the detection of clinically significant prostate cancer while reducing unnecessary biopsies and the detection of insignificant cancers. These targeted approaches preserve or improve diagnostic accuracy and patient outcomes, minimizing the risks associated with overdiagnosis and overtreatment. By utilizing mpMRI, targeted biopsies allow for precise targeting of suspicious regions within the prostate, providing a cost-effective method that reduces the number of biopsies performed. This review highlights the importance of integrating advanced imaging techniques into prostate cancer diagnosis to improve patient outcomes and quality of life.

MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study

BACKGROUND

Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases.

METHOD

A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation.

RESULTS

All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%.

CONCLUSIONS

The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

ProsTAV, a clinically useful test in prostate cancer: an extension study

PURPOSE

To assess the clinical performance of ProsTAV®, a blood-based test based on telomere associate variables (TAV) measurement, to support biopsy decision-making when diagnosing suspicious prostate cancer (PCa).

METHODS

Preliminary data of a prospective observational pragmatic study of patients with prostate-specific antigen (PSA) levels 3-10 ng/ml and suspicious PCa. Results were combined with other clinical data, and all patients underwent prostate biopsies according to each center's routine clinical practice, while magnetic resonance imaging (MRI) before the prostate biopsy was optional. Sensitivity, specificity, positive and negative predicted values, and subjects where biopsies could have been avoided using ProsTAV were determined.

RESULTS

The mean age of the participants (n = 251) was 67.4 years, with a mean PSA of 5.90 ng/ml, a mean free PSA of 18.9%, and a PSA density of 0.14 ng/ml. Digital rectal examination was abnormal in 21.1% of the subjects, and according to biopsy, the prevalence of significant PCa was 47.8%. The area under the ROC curve of ProsTAV was 0.7, with a sensitivity of 0.90 (95% CI, 0.85-0.95) and specificity of 0.27 (95% CI, 0.19-0.34). The positive and negative predictive values were 0.53 (95% CI, 0.46-0.60) and 0.74 (95% CI, 0.62-0.87), respectively. ProsTAV could have reduced the biopsies performed by 27% and showed some initial evidence of a putative benefit in the diagnosis pathway combined with MRI.

CONCLUSIONS

ProsTAV increases the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/ml and could be considered a complementary tool to improve the patient diagnosis pathway.

Comparison of Multiparametric MRI-targeted and Systematic Biopsies for Detection of Cribriform and Intraductal Carcinoma Prostate Cancer

Background Intraductal carcinoma (IDC) and invasive cribriform (Cr) subtypes of prostate cancer (PCa) are an indication of aggressiveness, but the evidence regarding whether MRI can be used to detect Cr/IDC-pattern PCa is contradictory. Purpose To compare the detection of Cr/IDC-pattern PCa at multiparametric MRI (mpMRI)-targeted biopsy versus systematic biopsy in biopsy-naive men at risk for PCa. Materials and Methods This study was a secondary analysis of a prospective randomized controlled trial that recruited participants with a clinical suspicion of PCa between April 2017 and November 2019 at five centers. Participants were randomized 1:1 to either the MRI arm or the systematic biopsy arm. Targeted biopsy was performed in participants with a Prostate Imaging Reporting and Data System score of at least 3. MRI features were recorded, and biopsy slides and prostatectomy specimens were reviewed for the presence or absence of Cr/IDC histologic patterns. Comparison of Cr/IDC patterns was performed using generalized linear mixed modeling. Results A total of 453 participants were enrolled, with 226 in the systematic biopsy arm (median age, 65 years [IQR, 59-70 years]; 196 biopsies available for assessment) and 227 in the mpMRI-targeted biopsy arm (median age, 67 years [IQR, 60-72 years]; 132 biopsies available for assessment). Identification of Cr/IDC PCa was lower in the systematic biopsy arm compared with the mpMRI arm (31 of 196 biopsies [16%] vs 33 of 132 biopsies [25%]; P = .01). No evidence of a difference in mean cancer core length (CCL) (11.3 mm ± 4.4 vs 9.7 mm ± 4.5; P = .09), apparent diffusion coefficient (685 µm2/sec ± 178 vs 746 µm2/sec ± 245; P = .52), or dynamic contrast-enhanced positivity (27 [82%] vs 37 [90%]; P = .33) for clinically significant PCa (csPCa) was observed between participants with or without Cr/IDC disease in the MRI arm. Cr/IDC-positive histologic patterns overall had a higher mean CCL compared with Cr/IDC-negative csPCa (11.1 mm ± 4.4 vs 9.2 mm ± 4.1; P = .009). Conclusion MRI-targeted biopsy showed increased detection of Cr/IDC histologic patterns compared with systematic biopsy. Clinical trial registration no. NCT02936258 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Scialpi and Martorana in this issue.

Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL: a systematic review and meta-analysis

OBJECTIVE

To investigate the diagnostic performance of MRI in detecting clinically significant prostate cancer (csPCa) and prostate cancer (PCa) in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL.

METHODS

A computerized search of PubMed, Embase, Cochrane Library, Medline, and Web of Science was conducted from inception until October 31, 2023. We included articles on the use of MRI to detect csPCa or PCa at 4-10 ng/mL PSA. The primary and secondary outcomes were MRI performance in csPCa and PCa detection, respectively; the estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were pooled in a bivariate random-effects model.

RESULTS

Among the 19 studies (3879 patients), there were 10 (2205 patients) and 13 studies (2965 patients) that reported MRI for detecting csPCa or PCa, respectively. The pooled sensitivity and specificity for csPCa detection were 0.84 (95% confidence interval [CI], 0.79-0.88) and 0.76 (95%CI, 0.65-0.84), respectively, for PCa detection were 0.82 (95%CI, 0.75-0.87) and 0.74 (95%CI, 0.65-0.82), respectively. The pooled NPV for csPCa detection was 0.91 (0.87-0.93). Biparametric magnetic resonance imaging also showed a significantly higher sensitivity and specificity relative to multiparametric magnetic resonance imaging (both p < 0.01).

CONCLUSION

Prostate MRI enables the detection of csPCa and PCa with satisfactory performance in the PSA gray zone. The excellent NPV for csPCa detection indicates the possibility of biopsy decision-making in patients in the PSA gray zone, but substantial heterogeneity among the included studies should be taken into account.

CLINICAL RELEVANCE STATEMENT

Prostate MRI can be considered a reliable and satisfactory tool for detecting csPCa and PCa in patients with PSA in the "gray zone", allowing for reducing unnecessary biopsy and optimizing the overall examination process.

KEY POINTS

Prostate-specific antigen (PSA) is a common screening tool for prostate cancer but risks overdiagnosis. MRI demonstrated excellent negative predictive value for prostate cancer in the PSA gray zone. MRI can influence decision-making for these patients, and biparametric MRI should be further evaluated.

Added value of randomised biopsy to multiparametric magnetic resonance imaging-targeted biopsy of the prostate in a contemporary cohort

OBJECTIVE

To assess the added value of concurrent systematic randomised ultrasonography-guided biopsy (SBx) to multiparametric magnetic resonance imaging (mpMRI)-targeted biopsy and the additional rate of overdiagnosis of clinically insignificant prostate cancer (ciPCa) by SBx in a large contemporary, real-world cohort.

PATIENTS AND METHODS

A total of 1552 patients with positive mpMRI and consecutive mpMRI-targeted biopsy and SBx were enrolled. Added value and the rate of overdiagnosis by SBx was evaluated.

PRIMARY OUTCOME

added value of SBx, defined as detection rate of clinically significant PCa (csPCa; International Society of Urological Pathology [ISUP] Grade ≥2) by SBx, while mpMRI-targeted biopsy was negative or showed ciPCa (ISUP Grade 1).

SECONDARY OUTCOME

rate of overdiagnosis by SBx, defined as detection of ciPCa in patients with negative mpMRI-targeted biopsy and PSA level of <10 ng/mL.

RESULTS

Detection rate of csPCa by mpMRI-targeted biopsy and/or SBx was 753/1552 (49%). Added value of SBx was 145/944 (15%). Rate of overdiagnosis by SBx was 146/656 (22%). Added value of SBx did not change when comparing patients with previous prostate biopsy and biopsy naïve patients. In multivariable analysis, a Prostate Imaging-Reporting and Data System (PI-RADS) 4 index lesion (odds ratio [OR] 3.19, 95% confidence interval [CI] 1.66-6.78; P = 0.001), a PI-RADS 5 index lesion (OR 2.89, 95% CI 1.39-6.46; P = 0.006) and age (OR 1.05, 95% CI 1.03-1.08; P < 0.001) were independently associated with added value of SBx.

CONCLUSIONS

In our real-world analysis, we saw a significant impact on added value and added rate of overdiagnosis by SBx. Subgroup analysis showed no significant decrease of added value in any evaluated risk group. Therefore, we do not endorse omitting concurrent SBx to mpMRI-guided biopsy of the prostate.

Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology

BACKGROUND

Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration.

PURPOSE

This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration.

METHODS

In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM.

RESULTS

Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment.

CONCLUSIONS

This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.

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.

Incorporating PHI in decision making: external validation of the Rotterdam risk calculators for detection of prostate cancer

PURPOSE

External validation of existing risk calculators (RC) to assess the individualized risk of detecting prostate cancer (PCa) in prostate biopsies is needed to determine their clinical usefulness. The objective was to externally validate the Rotterdam Prostate Cancer RCs 3 and 4 (RPCRC-3/4) and that incorporating PHI (RPCRC-PHI) in a contemporary Spanish cohort.

METHODS

Multicenter prospective study that included patients suspicious of harboring PCa. Men who attended the urology consultation were tested for PHI before prostate biopsy. To evaluate the performance of the prediction models: discrimination (receiver operating characteristic (ROC) curves), calibration and net benefit [decision curve analysis (DCA)] were calculated. These analyses were carried out for detection of any PCa and clinically significant (cs)PCa, defined as ISUP grade ≥ 2.

RESULTS

Among the 559 men included, 337 (60.28%) and 194 (34.7%) were diagnosed of PCa and csPCa, respectively. RPCRC-PHI had the best discrimination ability for detection of PCa and csPCa with AUCs of 0.85 (95%CI 0.82-0.88) and 0.82 (95%CI 0.78-0.85), respectively. Calibration plots showed that RPCRC-3/4 underestimates the risk of detecting PCa showing the need for recalibration. In DCA, RPCRC-PHI shows the highest net benefit compared to biopsy all men.

CONCLUSIONS

The RPCRC-PHI performed properly in a contemporary clinical setting, especially for prediction of csPCa.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS  = -0.21, p = 0.001), Decipher (rS  = -0.193, p = 0.003), and three-tier risk (rS  = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

Opportunistic Prostate Cancer Screening with Biparametric Magnetic Resonance Imaging (VISIONING)

BACKGROUND

This study investigates the use of biparametric magnetic resonance imaging (bpMRI) as primary opportunistic screening for prostate cancer (PCa) without using a prostate-specific antigen (PSA) cut-off.

OBJECTIVE

The primary endpoint was to assess the efforts and effectiveness of identifying 20 participants with clinically significant prostate cancer (csPCa) using bpMRI.

DESIGN, SETTING, AND PARTICIPANTS

Biopsy-naïve men aged over 45 yr were included. All participants underwent 3 Tesla bpMRI, PSA, and digital rectal examination (DRE). Targeted-only biopsy was performed in participants with Prostate Imaging Reporting and Data System (PI-RADS) ≥3. Men with negative bpMRI but suspicious DRE or elevated PSA/PSA density had template biopsies. Preintended protocol adjustments were made after an interim analysis for PI-RADS 3 lesions: no biopsy and follow-up MRI after 6 mo and biopsy only if lesions persisted or upgraded.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Biopsy results underwent a comparison using Fisher's exact test and univariable logistic regression to identify prognostic factors for positive biopsy.

RESULTS AND LIMITATIONS

A total of 229 men were enrolled in this study, of whom 79 underwent biopsy. Among these men, 77 displayed suspicious PI-RADS lesions. PCa was detected in 29 participants (12.7%), of whom 21 had csPCa (9.2%). Biparametric MRI detected 21 csPCa cases, while PSA and DRE would have missed 38.1%. Protocol adjustment led to a 54.6% biopsy reduction in PI-RADS 3 lesions. Overall, in this cohort of men with a median PSA value of 1.26 ng/ml, 10.9 bpMRI scans were needed to identify one participant with csPCa. A major limitation of the study is the lack of a control cohort undergoing systematic biopsies.

CONCLUSIONS

Opportunistic screening utilising bpMRI as a primary tool has higher sensitivity in detecting csPCa than classical screening methods.

PATIENT SUMMARY

Screening with biparametric magnetic resonance imaging (bpMRI) and targeted biopsy identified clinically significant prostate cancer in every 11th man, regardless of the prostate-specific antigen (PSA) levels. Preselecting patients based on PSA >1 ng/ml and a positive family history of prostate cancer, as well as other potential blood tests may further improve the effectiveness of bpMRI in this setting.

Role of psa levels and pathological stadiation before radiation therapy in predicting mp-MRI results in patients with prostate cancer recurrence after radical prostatectomy

OBJECTIVE

To evaluate PSA value in mp-MRI results prediction, analyzing patients with high (GS≥8, pT≥3, pN1) and low grade (GS<8, pT<3, pN0) Prostate Cancer (PCa).

MATERIALS AND METHODS

One hundred eighty-eight patients underwent 1.5-Tmp-MRI after Radical Prostatectomy (RP) and before Radiotherapy (RT). They were divided into 2 groups: A and B, for patients with biochemical recurrence (BCR) and without BCR but with high local recurrence risk. Considering Gleason Score (GS), pT and pN as independent grouping variables, ROC analyses of PSA levels at primary PCa diagnosis and PSA before RT were performed in order to identify the optimal cut-off to predict mp-MRI result.

RESULTS

Group A and B showed higher AUC for PSA before RT than PSA at PCa diagnosis, in low and high grade tumors. For low grade tumors the best AUC was 0.646 and 0.685 in group A and B; for high grade the best AUC was 0.705 and 1 in group A and B, respectively. For low grade tumors the best PSA cut-off was 0.565-0.58ng/mL in group A (sensitivity, specificity: 70.5%, 66%), and 0.11-0.13ng/mL in B (sensitivity, specificity: 62.5%, 84.6%). For high grade tumors, the best PSA cut-off obtained was 0.265-0.305ng/mL in group A (sensitivity, specificity: 95%, 42.1%), and 0.13-0.15ng/mL in B (sensitivity, specificity: 100%).

CONCLUSION

Mp-MRI should be performed as added diagnostic tool always when a BCR is detected, especially in high grade PCa. In patients without BCR, mp-MRI results, although poorly related to pathological stadiation, still have a good diagnostic performance, mostly when PSA>0.1-0.15ng/mL.

Clinical significance of prostate cancer identified by transperineal standard template biopsy in men with nonsuspicious multiparametric magnetic resonance imaging

OBJECTIVE

Multiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csCaP). However, whether a negative mpMRI in patients with a clinical suspicion of CaP can omit a confirmatory biopsy remains less understood and without consensus. Transperineal (TP) standard template biopsy (SBx) provides an effective approach to CaP detection. Our aim is to provide a comprehensive understanding of the CaP characteristics detected through TP SBx that are systematically overlooked by mpMRI.

METHODS

We conducted a retrospective analysis of all men who underwent prebiopsy mpMRI and subsequent a 20-core TP SBx at our hospital from September 2019 to February 2021. Patients with suspicious mpMRI received a combined TP SBx and targeted biopsy (TBx) (suspicious group), while those without suspicious (negative) mpMRI and who proceeded to biopsy, received TP SBx only (nonsuspicious group). A negative mpMRI was defined as the absence of suspicious findings and/or the presence of low-risk areas with a PI-RADS score of ≤2. Subsequently, we compared and evaluated the clinical and biopsy characteristics between these 2 groups.

RESULTS

We identified 301 men in suspicious group and 215 men in nonsuspicious group. The overall CaP detection rate and csCaP detection rate by TP SBx were 74.1%, 38.9% for suspicious group and 43.3%, 14.9% for nonsuspicious group, respectively. csCaP NPV of mpMRI was 85.1% with a csCaP prevalence 28.9%. The greatest percentage of cancer involvement (GPC) in biopsy core from nonsuspicious group was significantly lower than those of suspicious group (40% vs. 50%, p = 0.005), In multivariate logistic analysis, only PSAD > 0.15 ng/ml/cc was identified as an independent and significant predictor of csCaP in nonsuspicious group.

CONCLUSION

Within our cohort, false-negative rates of mpMRI for csCaP are substantial, reaching 15%. Nonsuspicious cases may contain a large volume tumor since the high GPC of SBx. For cases with nonsuspicious imaging and higher PSAD, a confirmatory biopsy may be necessary due to the increased risk of missed csCaP by mpMRI.

Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions

The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.

Prostate Cancer Local Staging with Magnetic Resonance Imaging

Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.

Prostate Cancers Invisible on Multiparametric MRI: Pathologic Features in Correlation with Whole-Mount Prostatectomy

We investigated why some prostate cancers (PCas) are not identified on multiparametric MRI (mpMRI) by using ground truth reference from whole-mount prostatectomy specimens. A total of 61 patients with biopsy-confirmed PCa underwent 3T mpMRI followed by prostatectomy. Lesions visible on MRI prospectively or retrospectively identified after correlating with histology were considered "identified cancers" (ICs). Lesions that could not be identified on mpMRI were considered "unidentified cancers" (UCs). Pathologists marked the Gleason score, stage, size, and density of the cancer glands and performed quantitative histology to calculate the tissue composition. Out of 115 cancers, 19 were unidentified on MRI. The UCs were significantly smaller and had lower Gleason scores and clinical stage lesions compared with the ICs. The UCs had significantly (p < 0.05) higher ADC (1.34 ± 0.38 vs. 1.02 ± 0.30 μm2/ms) and T2 (117.0 ± 31.1 vs. 97.1 ± 25.1 ms) compared with the ICs. The density of the cancer glands was significantly (p = 0.04) lower in the UCs. The percentage of the Gleason 4 component in Gleason 3 + 4 lesions was nominally (p = 0.15) higher in the ICs (20 ± 12%) compared with the UCs (15 ± 8%). The UCs had a significantly lower epithelium (32.9 ± 21.5 vs. 47.6 ± 13.1%, p = 0.034) and higher lumen volume (20.4 ± 10.0 vs. 13.3 ± 4.1%, p = 0.021) compared with the ICs. Independent from size and Gleason score, the tissue composition differences, specifically, the higher lumen and lower epithelium in UCs, can explain why some of the prostate cancers cannot be identified on mpMRI.

Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images: evaluation in a cohort of patients on active surveillance

PURPOSE

To assess a region-of-interest-based computer-assisted diagnosis system (CAD) in characterizing aggressive prostate cancer on magnetic resonance imaging (MRI) from patients under active surveillance (AS).

METHODS

A prospective biopsy database was retrospectively searched for patients under AS who underwent MRI and subsequent biopsy at our institution. MRI lesions targeted at baseline biopsy were retrospectively delineated to calculate the CAD score that was compared to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score assigned at baseline biopsy.

RESULTS

186 patients were selected. At baseline biopsy, 51 and 15 patients had International Society of Urological Pathology (ISUP) grade ≥ 2 and ≥ 3 cancer respectively. The CAD score had significantly higher specificity for ISUP ≥ 2 cancers (60% [95% confidence interval (CI): 51-68]) than the PI-RADS score (≥ 3 dichotomization: 24% [CI: 17-33], p = 0.0003; ≥ 4 dichotomization: 32% [CI: 24-40], p = 0.0003). It had significantly lower sensitivity than the PI-RADS ≥ 3 dichotomization (85% [CI: 74-92] versus 98% [CI: 91-100], p = 0.015) but not than the PI-RADS ≥ 4 dichotomization (94% [CI:85-98], p = 0.104). Combining CAD findings and PSA density could have avoided 47/184 (26%) baseline biopsies, while missing 3/51 (6%) ISUP 2 and no ISUP ≥ 3 cancers. Patients with baseline negative CAD findings and PSAd < 0.15 ng/mL2 who stayed on AS after baseline biopsy had a 9% (4/44) risk of being diagnosed with ISUP ≥ 2 cancer during a median follow-up of 41 months, as opposed to 24% (18/74) for the others.

CONCLUSION

The CAD could help define AS patients with low risk of aggressive cancer at baseline assessment and during subsequent follow-up.

Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI

PURPOSE

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI.

MATERIALS AND METHODS

The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated.

RESULTS

A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%.

CONCLUSION

The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

Prostate magnetic resonance imaging and the value of experience: An intrareader variability study

Objective

To measure the intraobserver concordance of an experienced genitourinary radiologist reporting of multiparametric magnetic resonance imaging of the prostate (mpMRIp) scans over time.

Methods

An experienced genitourinary radiologist re-reported his original 100 consecutive mpMRIp scans using Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) after 5 years of further experience comprising >1000 scans. Intraobserver agreement was measured using Cohen's kappa. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy were calculated, and comparison of sensitivity was performed using McNemar's test.

Results

Ninety-six mpMRIp scans were included in our final analysis. Of the 96 patients, 53 (55.2%) patients underwent subsequent biopsy (n=43) or prostatectomy (n=15), with 73 lesions targeted. Moderate agreement (Cohen's kappa 0.55) was seen in the number of lesions identified at initial reporting and on re-reading (81 vs. 39 total lesions; and 71 vs. 37 number of PI-RADS ≥3 lesions). For clinically significant prostate cancer, re-reading demonstrated an increase in specificity (from 43% to 89%) and PPV (from 62% to 87%), but a decrease in sensitivity (from 94% to 72%, p=0.01) and NPV (from 89% to 77%).

Conclusion

The intraobserver agreement for a novice to experienced radiologist reporting mpMRIp using PI-RADS v2 is moderate. Reduced sensitivity is off-set by improved specificity and PPV, which validate mpMRIp as a gold standard for prebiopsy screening.

Clinical Application of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation Score for Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer

Background

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) score has been developed to standardise prostate magnetic resonance imaging (MRI) reporting in men on active surveillance (AS) for prostate cancer (PCa).

Objective

To evaluate the feasibility of PRECISE scoring and assess its diagnostic accuracy.

Design setting and participants

All PCa patients on AS with a baseline MRI and at least one follow-up MRI scan between January 2008 and September 2022 at a single tertiary referral centre were included in a database. The follow-up protocol of the Prostate Cancer International Active Surveillance (PRIAS) study was used. All scans were retrospectively re-reported by a dedicated uroradiologist and appointed a Prostate Imaging Reporting and Data System (version 2.1) and PRECISE score.

Outcome measurements and statistical analysis

Clinically significant progression was defined by histopathological upgrading (on biopsy or radical prostatectomy) to grade group ≥3 and/or evolution to T3 stage. A survival analysis was performed to assess differential progression-free survival (PFS) according to the PRECISE score.

Results and limitations

A total of 188 patients were included for an analysis with a total of 358 repeat MRI scans and 144 repeat biopsies. The median follow-up was 46 mo (interquartile range 21-74). Radiological progression (PRECISE 4-5) had sensitivity, specificity, negative predictive value, and positive predictive value of, respectively, 78%, 70%, 90%, and 49% for clinically significant progression. Four-year PFS was 91% for PRECISE 1-3 versus 66% for PRECISE 4-5 (p < 0.001). In total, 137 patients underwent a confirmation MRI scan within 18 mo after diagnosis. Four-year PFS in this group was 81% for PRECISE 1-3 versus 43% for PRECISE 4-5 (p < 0.001). Limitations include retrospective design and no strict adherence to AS protocol.

Conclusions

Implementation of PRECISE scoring for PCa patients on AS is feasible and offers a prognostic value. Patients with PRECISE score 4-5 on confirmation MRI within 18 mo after diagnosis have a three-fold higher risk of clinically significant progression after 4 yr.

Patient summary

Patients with low-risk prostate cancer can be followed up carefully. In this study, we evaluate the standardised reporting of repeat magnetic resonance imaging scans (using the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation [PRECISE] recommendations). PRECISE scoring is feasible and helps identify patients in need of further treatment.

A multi-institutional study of 1,111 men with 4K score, multiparametric magnetic resonance imaging, and prostate biopsy

OBJECTIVE

Prostate magnetic resonance imaging (MRI) and biomarkers are often used in conjunction to enhance the selection process for prostate biopsy. However, the optimal sequence of ordering these tests has not been established. A comprehensive evaluation was conducted on a large multi-institutional cohort of patients who underwent MRI, 4K score, and biopsy of the prostate to examine the impact of utilizing both tests vs. either test alone and to determine if the order in which these tests are administered affects the ability to detect clinically significant prostate cancer (csCaP).

METHODS AND MATERIALS

We evaluated men from 8 different institutions who were referred for prostate cancer evaluation and underwent MRI, 4K score test, and prostate biopsy. The primary outcome was the presence of csCaP, defined as grade group 2 or higher cancer on a biopsy of the prostate. We used logistic regression, calibration plots, and decision curve analysis to evaluate using a 4K score or MRI alone vs. both tests together for detecting csCaP. In addition, we evaluated several strategies using one or both tests for selecting men for biopsy and compared them based on the proportion of biopsies avoided and the csCaP's missed.

RESULTS

Among the 1,111 men who formed the final cohort, 553 (49.8%) had prostate cancer, and 353 (31.8%) had csCaP. We found that using MRI and 4K score together had better discrimination, calibration, and a higher clinical utility on decision curve analysis compared to using either test individually. Using both tests together resulted in fewer biopsies avoided and missed cancers compared to using either test alone. Strategies that sequence MRI and 4K score tests resulted in the largest biopsy reduction, with no appreciable difference between starting with an MRI vs. a biomarker.

CONCLUSIONS

We found that using both an MRI and 4K score together was superior to using either test alone but found no appreciable difference between starting with an MRI vs. starting with a 4K score. Prospective studies are needed to identify the best strategy to sequence MRI and biomarkers in the evaluation of csCaP.

Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI

BACKGROUND AND OBJECTIVE

Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging.

METHODS

This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism.

RESULTS

The results of our proposed network's prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively.

CONCLUSIONS

Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.

MRI-derived tumor volume as a predictor of biochemical recurrence and adverse pathology in patients after radical prostatectomy: a propensity score matching study

PURPOSE

To investigate the predictive value of MRI-derived tumor volume (TV) of biochemical recurrence (BCR) and adverse pathology (AP) in patients following radical prostatectomy (RP).

METHODS

The data of 565 patients receiving RP in a single institution between 2010 and 2021 were retrospectively analyzed. All suspicious tumor foci were delineated manually using ITK-SNAP software as the regions of interest (ROIs). The sum of the TV of all lesions was calculated automatically based on the voxel in the ROIs to acquire the final TV parameter. TV was categorized as low-volume (≤ 6.5 cm3) and high-volume (> 6.5 cm3) based on the cut-off value. Univariate and multivariate Cox and logistic regression analyses were performed to identify independent predictors of BCR and AP. The Kaplan-Meier with the log-rank test was conducted to compare the BCR-free survival (BFS) between the low and high-volume groups.

RESULTS

All the included patients were divided into the low-volume group (n = 337) and the high-volume group (n = 228). The TV was an independent predictor of BFS in the multivariate Cox regression analysis (Hazard Ratio (HR) [95% CI]: 1.550 [1.066-2.256], P = 0.022). The Kaplan-Meier analysis demonstrated that low volume was associated with a better BFS than high volume before propensity score matching (PSM) (P < 0.001). One hundred and fifty-eight pairs were obtained by 1:1 PSM to balance the baseline parameters between the two groups. After the PSM, low-volume remained to be associated with a better BFS than high-volume (P = 0.006). TV as a categorical variable was an independent factor of AP in multivariate logistic regression analysis (Odd ratio (OR) [95% CI]: 1.821 [1.064-3.115], P = 0.029). After balancing the potential factors influencing AP by 1:1 PSM, 162 new pairs were identified. The high-volume group had a higher AP rate than the low-volume group after PSM (75.9 vs. 64.8%, P = 0.029).

CONCLUSION

We adopted a novel approach to acquiring the TV on preoperative MRI. TV was significantly associated with BFS and AP of patients undergoing RP, which was further illustrated by PSM analysis. MRI-derived TV may serve as a predictive marker for assessing BFS and AP in further studies, which will facilitate clinical decision-making and patient counseling.

Prostate MRI and PSMA-PET in the Primary Diagnosis of Prostate Cancer

Over the last years, prostate magnetic resonance imaging (MRI) has gained a key role in the primary diagnosis of clinically significant prostate cancer (csPCa). While a negative MRI can avoid unnecessary prostate biopsies and the overdiagnosis of indolent cancers, a positive examination triggers biopsy samples targeted to suspicious imaging findings, thus increasing the diagnosis of csPCa with a sensitivity and negative predictive value of around 90%. The limitations of MRI, including suboptimal positive predictive values, are fueling debate on how to stratify biopsy decisions and management based on patient risk and how to correctly estimate it with clinical and/or imaging findings. In this setting, "next-generation imaging" imaging based on radiolabeled Prostate-Specific Membrane Antigen (PSMA)-Positron Emission Tomography (PET) is expanding its indications both in the setting of primary staging (intermediate-to-high risk patients) and primary diagnosis (e.g., increasing the sensitivity of MRI or acting as a problem-solving tool for indeterminate MRI cases). This review summarizes the current main evidence on the role of prostate MRI and PSMA-PET as tools for the primary diagnosis of csPCa, and the different possible interaction pathways in this setting.

Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review

Prostate cancer (PCa) faces great challenges in early diagnosis, which often leads not only to unnecessary, invasive procedures, but to over-diagnosis and treatment as well, thus highlighting the need for modern PCa diagnostic techniques. The review aims to provide an up-to-date summary of chronologically existing diagnostic approaches for PCa, as well as their potential to improve clinically significant PCa (csPCa) diagnosis and to reduce the proliferation and monitoring of PCa. Our review demonstrates the primary outcomes of the most significant studies and makes comparisons across the diagnostic efficacies of different PCa tests. Since prostate biopsy, the current mainstream PCa diagnosis, is an invasive procedure with a high risk of post-biopsy complications, it is vital we dig out specific, sensitive, and accurate diagnostic approaches in PCa and conduct more studies with milestone findings and comparable sample sizes to validate and corroborate the findings.

Re-evaluating the diagnostic efficacy of PSA as a referral test to detect clinically significant prostate cancer in contemporary MRI-based image-guided biopsy pathways

Introduction

Modern image-guided biopsy pathways at diagnostic centres have greatly refined the investigations of men referred with suspected prostate cancer. However, the referral criteria from primary care are still based on historical prostate-specific antigen (PSA) cut-offs and age-referenced thresholds. Here, we tested whether better contemporary pathways and biopsy methods had improved the predictive utility value of PSA referral thresholds.

Methods

PSA referral thresholds, age-referenced ranges and PSA density (PSAd) were assessed for positive predictive value (PPV) in detection of clinically significant prostate cancer (csPCa - histological ⩾ Grade Group 2). Data were analysed from men referred to three diagnostics centres who used multi-parametric magnetic resonance imaging (mpMRI)-guided prostate biopsies for disease characterisation. Findings were validated in a separate multicentre cohort. Results: Data from 2767 men were included in this study. The median age, PSA and PSAd were 66.4 years, 7.3 ng/mL and 0.1 ng/mL2, respectively. Biopsy detected csPCa was found in 38.7%. The overall area under the curve (AUC) for PSA was 0.68 which is similar to historical performance. A PSA threshold of ⩾ 3 ng/mL had a PPV of 40.3%, but this was age dependent (PPV: 24.8%, 32.7% and 56.8% in men 50-59 years, 60-69 years and ⩾ 70 years, respectively). Different PSA cut-offs and age-reference ranges failed to demonstrate better performance. PSAd demonstrated improved AUC (0.78 vs 0.68, p < 0.0001) and improved PPV compared to PSA. A PSAd of ⩾ 0.10 had a PPV of 48.2% and similar negative predictive value (NPV) to PSA ⩾ 3 ng/mL and out-performed PSA age-reference ranges. This improved performance was recapitulated in a separate multi-centre cohort (n = 541).

Conclusion

The introduction of MRI-based image-guided biopsy pathways does not appear to have altered PSA diagnostic test characteristics to positively detect csPCa. We find no added value to PSA age-referenced ranges, while PSAd offers better PPV and the potential for a single clinically useful threshold (⩾0.10) for all age groups.

Level of evidence

IV.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

Contemporary Review of Multimodality Imaging of the Prostate Gland

Tissue changes and the enlargement of the prostate, whether benign or malignant, are among the most common groups of diseases that affect men and can have significant impacts on length and quality of life. The prevalence of benign prostatic hyperplasia (BPH) increases significantly with age and affects nearly all men as they grow older. Other than skin cancers, prostate cancer is the most common cancer among men in the United States. Imaging is an essential component in the diagnosis and management of these conditions. Multiple modalities are available for prostate imaging, including several novel imaging modalities that have changed the landscape of prostate imaging in recent years. This review will cover the data relating to commonly used standard-of-care prostate imaging modalities, advances in newer technologies, and newer standards that impact prostate gland imaging.

ACR Appropriateness Criteria® Pretreatment Detection, Surveillance, and Staging of Prostate Cancer: 2022 Update

Prostate cancer is second leading cause of death from malignancy after lung cancer in American men. The primary goal during pretreatment evaluation of prostate cancer is disease detection, localization, establishing disease extent (both local and distant), and evaluating aggressiveness, which are the driving factors of patient outcomes such as recurrence and survival. Prostate cancer is typically diagnosed after the recognizing elevated serum prostate-specific antigen level or abnormal digital rectal examination. Tissue diagnosis is obtained by transrectal ultrasound-guided biopsy or MRI-targeted biopsy, commonly with multiparametric MRI without or with intravenous contrast, which has recently been established as standard of care for detecting, localizing, and assessing local extent of prostate cancer. Although bone scintigraphy and CT are still typically used to detect bone and nodal metastases in patients with intermediate- or high-risk prostate cancer, novel advanced imaging modalities including prostatespecific membrane antigen PET/CT and whole-body MRI are being more frequently utilized for this purpose with improved detection rates. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Single-center, prospective phase 2 trial of high-intensity focused ultrasound (HIFU) in patients with unilateral localized prostate cancer: good functional results but oncologically not as safe as expected

PURPOSE

Focal therapy (FT) for localized prostate cancer (PCa) is only recommended within the context of clinical trials by international guidelines. We aimed to investigate oncological follow-up and safety data of focal high-intensity focused ultrasound (HIFU) treatment.

METHODS

We conducted a single-center prospective study of 29 patients with PCa treated with (focal) HIFU between 2016 and 2021. Inclusion criteria were unilateral PCa detected by mpMRI-US-fusion prostate biopsy and maximum prostate specific antigen (PSA) of 15 ng/ml. Follow-up included mpMRI-US fusion-re-biopsies 12 and 24 months after HIFU. No re-treatment of HIFU was allowed. The primary endpoint was failure-free survival (FFS), defined as freedom from intervention due to cancer progression.

RESULTS

Median follow-up of all patients was 23 months, median age was 67 years and median preoperative PSA was 6.8 ng/ml. One year after HIFU treatment PCa was still detected in 13/ 29 patients histologically (44.8%). Two years after HIFU another 7/29 patients (24.1%) were diagnosed with PCa. Until now, PCa recurrence was detected in 11/29 patients (37.93%) which represents an FFS rate of 62%.One patient developed local metastatic disease 2 years after focal HIFU. Adverse events (AE) were low with 70% of patients remaining with sufficient erectile function for intercourse and 97% reporting full maintenance of urinary continence.

CONCLUSION

HIFU treatment in carefully selected patients is feasible. However, HIFU was oncologically not as safe as expected because of progression rates of 37.93% and risk of progression towards metastatic disease. Thus, we stopped usage of HIFU in our department.