The primacy of multiparametric MRI in men with suspected prostate cancer

Navigating the evolving diagnostic and therapeutic landscape of low- and intermediate-risk prostate cancer

ABSTRACT

In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients' treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure.

Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study

BACKGROUND AND OBJECTIVE

Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone.

METHODS

A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test.

KEY FINDINGS AND LIMITATIONS

Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

PATIENT SUMMARY

The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

ParaCM-PNet: A CNN-tokenized MLP combined parallel dual pyramid network for prostate and prostate cancer segmentation in MRI

The precise prostate gland and prostate cancer (PCa) segmentations enable the fusion of magnetic resonance imaging (MRI) and ultrasound imaging (US) to guide robotic prostate biopsy systems. This precise segmentation, applied to preoperative MRI images, is crucial for accurate image registration and automatic localization of the biopsy target. Nevertheless, describing local prostate lesions in MRI remains a challenging and time-consuming task, even for experienced physicians. Therefore, this research work develops a parallel dual-pyramid network that combines convolutional neural networks (CNN) and tokenized multi-layer perceptron (MLP) for automatic segmentation of the prostate gland and clinically significant PCa (csPCa) in MRI. The proposed network consists of two stages. The first stage focuses on prostate segmentation, while the second stage uses a prior partition from a previous stage to detect the cancerous regions. Both stages share a similar network architecture, combining CNN and tokenized MLP as the feature extraction backbone to creating a pyramid-structured network for feature encoding and decoding. By employing CNN layers of different scales, the network generates scale-aware local semantic features, which are integrated into feature maps and inputted into an MLP layer from a global perspective. This facilitates the complementarity between local and global information, capturing richer semantic features. Additionally, the network incorporates an interactive hybrid attention module to enhance the perception of the target area. Experimental results demonstrate the superiority of the proposed network over other state-of-the-art image segmentation methods for segmenting the prostate gland and csPCa tissue in MRI images.

A single-center, multi-factor, retrospective study to improve the diagnostic accuracy of primary prostate cancer using [68Ga]Ga-PSMA-11 total-body PET/CT imaging

PURPOSE

To improve the diagnostic accuracy of initial detection in patients with suspected primary prostate cancer (PCa).

METHODS

Eighty-four patients who underwent Gallium-68-labeled prostate-specific membrane antigen ([68Ga]Ga-PSMA-11) total-body positron emission tomography/computed tomography (PET/CT) imaging before treatment in our department were enrolled. The maximum standard uptake value (SUVmax) of the prostate (SUVmax-PSMA), liver (SUVmax-PSMA-L), and mediastinal blood pool (SUVmax-PSMA-M) was measured using [68Ga]Ga-PSMA-11 total-body PET/CT imaging. The [68Ga]Ga-PSMA-11 derived metabolic tumor volume (MTV), the total lesion (TLP), and the cross-sectional areas of focal concentration in the prostate (CAP) were also determined. Besides, the prostate-specific antigen (PSA) levels and the above imaging characteristics were analyzed using receiver operating characteristic curves to identify the cutoff value to improve the diagnostic accuracy of suspected PCa. Finally, a multivariate regression analysis was conducted to discover the independent predictor to improve the diagnostic accuracy on [68Ga]Ga-PSMA-11 total-body imaging.

RESULTS

There was no significant difference between the PCa and Non-PCa groups in age, height, weight, injected dose, except for the PSA levels, the SUVmax-PSMA, TLP, MTV, and CAP. Besides, the SUVmax-PSMA-T/L and SUVmax-PSMA-T/M derived from SUVmax-PSMA were both significantly different. In addition, the areas under the curve of PSA levels, SUVmax-PSMA, SUVmax-PSMA-T/L, SUVmax-PSMA-T/M, TLP, MTV, and CAP to predict PCa on [68Ga]Ga-PSMA-11 imaging were 0.620 (95% confidence interval (CI) 0.485-0.755), 0.864 (95% CI 0.757-0.972), 0.819 (95% CI 0.704-0.935), 0.876 (95% CI 0.771-0.980), 0.845 (95% CI 0.741-0.949), 0.820 (95% CI 0.702-0.938), 0.627 (95% CI 0.499-0.754), respectively. However, a multivariate regression analysis showed that SUVmax-PSMA was an independent predictor, with a cutoff value of 11.5 and an odds ratio of 1.221.

CONCLUSION

The SUVmax-PSMA with a cutoff value of 11.5 was an independent predictor to improve the diagnostic accuracy of PCa on [68Ga]Ga-PSMA-11 total-body imaging.

Evaluation of Prostate Cancer Recurrence with MR Imaging and Prostate Imaging for Recurrence Reporting Scoring System

Detection of prostate cancer recurrence after whole-gland treatment with curative intent is critical to identify patients who may benefit from local salvage therapy. Among the different imaging modalities used in clinical practice, MR imaging is the most accurate in identifying local prostate cancer recurrence; indeed, it is an excellent technique for local recurrence detection superior to PET/CT, even at low PSA, but provides no information about extra-pelvic lymph nodes or bone metastasis. In 2021, a group of experts developed the Prostate Imaging for local Recurrence Reporting scoring system to standardize acquisition, interpretation, and reporting of prostate cancer recurrence.

Quantification and significance of extraprostatic findings on prostate MRI: a retrospective analysis and three-tier classification

OBJECTIVES

To quantify extraprostatic findings (EPFs) on prostate MRI, estimate the proportion of reported and unreported EPFs, assess their clinical importance, and propose standardized reporting of EPFs.

MATERIALS AND METHODS

Prostate 3-T MRI studies, reports, and clinical data from 623 patients (age 67.9 ± 8.2 years) were retrospectively analyzed and re-evaluated for the presence of EPFs and their clinical significance: E1-no finding or findings that have no clinical significance; E2-potentially significant findings; and E3-significant findings.

RESULTS

Secondary reading identified 1236 EPFs in 593 patients (1.98 ± 1.13 EPFs per patient, no EPFs in 30 patients), from which 468 (37.8%) were mentioned in the original report. The most common findings included diverticulosis (44% of patients), hydrocele (34%), inguinal fat hernia (16%), and bladder wall trabecular hypertrophy (15%). There were 80 (6.5%) E2 EPFs and 30 (2.4%) E3 EPFs. From E3 EPFs, 10 (33%) were not originally reported. A workup was suggested in 35 (52%) of the 67 originally reported E2 and E3 findings with follow-up and performed in 20 (30%). Fourteen (21%) EPFs in 11 patients influenced their management. Four experienced radiologists originally reported 1.8 to 2.5 findings per patient (p < 0.0001).

CONCLUSIONS

EPFs on prostate MRI are frequent, but only 2.4% are clinically significant (E3), and 33% of these are not reported. Only 30% of E2 and E3 findings are further explored, and 21% influence patient management. We suggest that an "E" category should be attached to the PI-RADS system to identify the presence of EPFs that require further workup.

CRITICAL RELEVANCE STATEMENT

Extraprostatic findings on prostate MRI are frequent, but only 2.4% are clinically significant (E3), and 33% of these are not reported. We advocate standardized reporting of extraprostatic findings indicating their clinical significance.

KEY POINTS

• Extraprostatic findings on prostate MRI are frequent with an average of two findings per patient. • 2.4% of extraprostatic findings are significant, and 33% of these are not reported. • There is a significant variability among experienced radiologists in reporting extraprostatic findings.

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.

The diagnostic accuracy of radiolabeled PSMA-ligand PET for tumour staging in newly diagnosed prostate cancer patients compared to histopathology: a systematic review and meta-analysis

PURPOSE

The current clinical recommendations posit the deployment of specific approved radiolabeled prostate-specific membrane antigen-ligand positron emission tomography (PSMA PET) for detecting metastatic prostate cancer during primary staging. Nevertheless, the precise efficacy of such ligands in localizing intraprostatic tumours (index tumour) and T-staging is not well established. Consequently, the objective of this inquiry is to ascertain the diagnostic accuracy of PSMA-PET in the tumour staging of newly diagnosed prostate cancer by means of a meta-analysis that integrates studies utilizing histological confirmation as the reference standard.

METHODS

In this study, we conducted a systematic literature search of the PubMed, Embase, Web of Science, and Cochrane Library databases using a predefined collection of search terms. These terms included 'PSMA PET', 'primary staging', and 'prostate cancer'. Subsequently, two independent reviewers evaluated all the studies based on predetermined inclusion criteria, extracted pertinent data, and assessed the quality of evidence. Any disparities were resolved by a third reviewer. A random effects Sidik-Jonkman model was applied to conduct a meta-analysis and estimate the diagnostic accuracy on a per-patient basis, along with 95% confidence intervals. Moreover, an appraisal regarding the likelihood of publication bias and the impact of small-study effects was performed utilizing both Egger's test and a graphical examination of the funnel plot.

RESULTS

The present analysis comprised a total of twenty-three scientific papers encompassing 969 patients and involved their analysis by both qualitative and quantitative approaches. The results of this study demonstrated that the estimated diagnostic accuracy of PSMA PET/CT and PSMA PET/MRI, for the detection of intraprostatic tumours, regardless of the type of PSMA-ligand, was 86% (95% CI: 76-96%) and 97% (95% CI: 94-100%), respectively. Furthermore, the diagnostic accuracy for the detection of extraprostatic extension (EPE) was 73% (95% CI: 64-82%) and 77% (95% CI: 69-85%), while the diagnostic accuracy for the detection of seminal vesicle involvement (SVI) was 87% (95% CI: 80-93) and 90% (95% CI: 82-99%), respectively.

CONCLUSION

The present investigation has demonstrated that PSMA PET/MRI surpasses currently recommended multiparametric magnetic resonance imaging (mpMRI) in terms of diagnostic accuracy as inferred from a notable data trajectory, whereas PSMA-PET/CT exhibited comparable diagnostic accuracy for intraprostatic tumour detection and T-staging compared to mpMRI. Nevertheless, the analysis has identified certain potential limitations, such as small-study effects and a potential for publication bias, which may impact the overall conclusions drawn from this study.

Towards the Definition of Radiomic Features and Clinical Indices to Enhance the Diagnosis of Clinically Significant Cancers in PI-RADS 4 and 5 Lesions

Prostate cancer (PC) is the most frequently diagnosed cancer among adult men, and its incidence is increasing worldwide [...].

Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader

PURPOSE

The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers.

MATERIALS AND METHODS

ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions.

RESULTS

The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively.

CONCLUSION

The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.

Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis

INTRODUCTION

Multiparametric MRI (mpMRI) has transformed the prostate cancer diagnostic pathway, allowing for improved risk stratification and more targeted subsequent management. However, concerns exist over the interobserver variability of images and the applicability of this model long term, especially considering the current shortage of radiologists and the growing ageing population. Artificial intelligence (AI) is being integrated into clinical practice to support diagnostic and therapeutic imaging analysis to overcome these concerns. The following report details a protocol for a systematic review and meta-analysis investigating the accuracy of AI in predicting primary prostate cancer on mpMRI.

METHODS AND ANALYSIS

A systematic search will be performed using PubMed, MEDLINE, Embase and Cochrane databases. All relevant articles published between January 2016 and February 2023 will be eligible for inclusion. To be included, articles must use AI to study MRI prostate images to detect prostate cancer. All included articles will be in full-text, reporting original data and written in English. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. The QUADAS-2 score will assess the quality and risk of bias across selected studies.

ETHICS AND DISSEMINATION

Ethical approval will not be required for this systematic review. Findings will be disseminated through peer-reviewed publications and presentations at both national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42021293745.

Prediction of clinically significant prostate cancer using a novel 68Ga-PSMA PET-CT and multiparametric MRI-based model

Background

There are some limitations in the commonly used methods for the detection of prostate cancer. There is a lack of nomograms based on multiparametric magnetic resonance imaging (mpMRI) and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET-CT) for the prediction of prostate cancer. The study seeks to compare the performance of mpMRI and 68Ga-PSMA PET-CT, and design a novel predictive model capable of predicting clinically significant prostate cancer (csPCa) before biopsy based on a combination of 68Ga-PSMA PET-CT, mpMRI, and patient clinical parameters.

Methods

From September 2020 to June 2021, we prospectively enrolled 112 consecutive patients with no prior history of prostate cancer who underwent both 68Ga-PSMA PET-CT and mpMRI prior to biopsy at our clinical center. Univariate and multivariate regression analyses were used to identify predictors of csPCa, with a predictive model and its nomogram incorporating 68Ga-PSMA PET-CT, mpMRI, and the clinical predictors then being generated. The constructed model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, and further validated with the internal and external cohorts.

Results

The model incorporated prostate-specific antigen density (PSAd), Prostate Imaging Reporting and Data System (PI-RADS) category, and maximum standardized uptake value (SUVmax), and it exhibited excellent predictive efficacy when applying to evaluate both training and validation cohorts [area under the curve (AUC): 0.936 and 0.940, respectively]. Compared with SUVmax alone, the model demonstrated excellent diagnostic performance with improved specificity (0.910, 95% CI: 0.824-0.963) and positive predictive values (0.811, 95% CI: 0.648-0.920). Calibration curve and decision curve analysis further confirmed that the model exhibited a high degree of clinical net benefit and low error rate.

Conclusions

The constructed model in this study was capable of accurately predicting csPCa prior to biopsy with excellent discriminative ability. As such, this model has the potential to be an effective non-invasive approach for the diagnosis of csPCa.

Comparing the Detection Performance Between Multiparametric Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen PET/CT in Patients With Localized Prostate Cancer: A Systematic Review and Meta-analysis

PURPOSE

Multiparametric MRI (mpMRI) has been promoted as an auxiliary diagnostic tool for prostate biopsy. However, prostate-specific membrane antigen (PSMA) including 68 Ga-PSMA-11, 18 F-DCFPyL, and 18 F-PSMA-1007 applied PET/CT imaging was an emerging diagnostic tool in prostate cancer patients for staging or posttreatment follow-up, even early detecting. Many studies have used PSMA PET for comparison with mpMRI to test the diagnostic ability for early prostate cancer. Unfortunately, these studies have shown conflicting results. This meta-analysis aimed to compare the differences in diagnostic performance between PSMA PET and mpMRI for detecting and T staging localized prostatic tumors.

METHODS

This meta-analysis involved a systematic literature search of PubMed/MEDLINE and Cochrane Library databases. The pooling sensitivity and specificity of PSMA and mpMRI verified by pathological analysis were calculated and used to compare the differences between the 2 imaging tools.

RESULTS

Overall, 39 studies were included (3630 patients in total) from 2016 to 2022 in the current meta-analysis and found that the pooling sensitivity values for localized prostatic tumors and T staging T3a and T3b of PSMA PET were 0.84 (95% confidence interval [CI], 0.83-0.86), 0.61 (95% CI, 0.39-0.79), and 0.62 (95% CI, 0.46-0.76), respectively, whereas those of mpMRI were found to be 0.84 (95% 0.78-0.89), 0.67 (95% CI, 0.52-0.80), and 0.60 (95% CI, 0.45-0.73), respectively, without significant differences ( P > 0.05). However, in a subgroup analysis of radiotracer, the pooling sensitivity of 18 F-DCFPyL PET was higher than mpMRI (relative risk, 1.10; 95% CI, 1.03-1.17; P < 0.01).

CONCLUSIONS

This meta-analysis found that whereas 18 F-DCFPyL PET was superior to mpMRI at detecting localized prostatic tumors, the detection performance of PSMA PET for localized prostatic tumors and T staging was comparable to that of mpMRI.

Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation

BACKGROUND

Sarcopenia is a well know prognostic factor in oncology, influencing patients' quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes.

METHODS

We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints.

RESULTS

97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10-20% up to ~45-55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle.

CONCLUSIONS

A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.

Bridging the experience gap in prostate multiparametric magnetic resonance imaging using artificial intelligence: A prospective multi-reader comparison study on inter-reader agreement in PI-RADS v2.1, image quality and reporting time between novice and expert readers

PURPOSE

The aim of the study was to determine the impact of using a semi-automatic commercially available AI-assisted software (Quantib® Prostate) on inter-reader agreement in PI-RADS scoring at different PI-QUAL ratings and grades of reader confidence and on reporting times among novice readers in multiparametric prostate MRI.

METHODS

A prospective observational study, with a final cohort of 200 patients undergoing mpMRI scans, was performed at our institution. An expert fellowship-trained urogenital radiologist interpreted all 200 scans based on PI-RADS v2.1. The scans were divided into four equal batches of 50 patients. Four independent readers evaluated each batch with and without the use of AI-assisted software, blinded to expert and individual reports. Dedicated training sessions were held before and after each batch. Image quality rated according to PI-QUAL and reporting times were recorded. Readers' confidence was also evaluated. A final evaluation of the first batch was conducted at the end of the study to assess for any changes in performance.

RESULTS

The overall kappa coefficient differences in PI-RADS scoring agreement without and with Quantib® were 0.673 to 0.736 for Reader 1, 0.628 to 0.483 for Reader 2, 0.603 to 0.292 for Reader 3 and 0.586 to 0.613 for Reader 4. Using PI-RADS ≥ 4 as cut-off for biopsy, the AUCs with AI ranged from 0.799 (95 % CI: 0.743, 0.856) to 0.820 (95 % CI: 0.765, 0.874). Inter-reader agreements at different PI-QUAL scores were higher with the use of Quantib, particularly for readers 1 and 4, with Kappa coefficient values showing moderate to slight agreement.

CONCLUSION

Quantib® Prostate could potentially be useful in improving inter-reader agreement among less experienced to completely novice readers if used as a supplement to PACS.

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

OBJECTIVE

To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience.

METHODS

Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 'predefined' lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored 'additional' lesions if needed. Per-lesion analysis assessed the 'predefined' lesions, using targeted biopsy as reference; per-lobe analysis included 'predefined' and 'additional' lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement.

RESULTS

At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60-0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43-0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70-0.79) than experienced seniors (0.80; 95%CI 0.76-0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70-0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6-29), of which 2 (IQR: 1-3) were csPCa; it upgraded 4 lesions/reader (IQR: 2-7), of which 1 (IQR: 0-2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25-73) 'additional' lesions/reader, yielded similar results.

CONCLUSIONS

Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.

Detection of prostate cancer using diffusion-relaxation correlation spectrum imaging with support vector machine model - a feasibility study

BACKGROUND

To evaluate the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) with support vector machine (SVM) in detecting prostate cancer (PCa).

METHODS

In total, 114 patients (mean age, 66 years, range, 48-87 years) who received a prostate MRI and underwent biopsy were enrolled in three stages. Thirty-nine were assigned for the exploration stage to establish the model, 18 for the validation stage to choose the appropriate scale for mapping and 57 for the test stage to compare the diagnostic performance of the DR-CSI and PI-RADS.

RESULTS

In the exploration stage, the DR-CSI model was established and performed better than the ADC and T₂ values (both P < 0.001). The validation result shows that at least 2 pixels were required for both the long-axis and short-axis in the mapping procedure. In the test stage, DR-CSI had higher accuracy than PI-RADS ≥ 3 as a positive finding based on patient (84.2% vs. 63.2%, P = 0.004) and lesion (78.8% vs. 57.6%, P = 0.001) as well as PI-RADS ≥ 4 on lesion (76.5% vs. 64.7%, P = 0.029), while there was no significant difference between DR-CSI and PI-RADS ≥ 4 based on patient (P = 0.508). For clinically significant PCa, DR-CSI had higher accuracy than PI-RADS ≥ 3 based on patients (84.2% vs. 63.2%, P = 0.004) and lesions (62.4% vs. 48.2%, P = 0.036). There was no significant difference between DR-CSI and PI-RADS ≥ 4 (P = 1.000 and 0.845 for the patient and lesion levels, respectively).

CONCLUSIONS

DR-CSI combined with the SVM model may improve the diagnostic accuracy of PCa.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of our institute (Approval No. KY2018-213). Written informed consent was obtained from all participants.

Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

PURPOSE

The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting.

RESULTS

Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings.

CONCLUSION

Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.

Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer

Background

Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods.

Methods

The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores.

Results

The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation.

Conclusion

The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring.

The contribution of the 1H-MRS lipid signal to cervical cancer prognosis: a preliminary study

Background

The aim of this study was to investigate the role of the lipid peak derived from ¹H magnetic resonance (MR) spectroscopy in assessing cervical cancer prognosis, particularly in assessing response to neoadjuvant chemotherapy (NACT) of locally advanced cervical cancer (LACC).

Methods

We enrolled 17 patients with histologically proven cervical cancer who underwent 3-T MR imaging at baseline. In addition to conventional imaging sequences for pelvic assessment, the protocol included a single-voxel point-resolved spectroscopy (PRESS) sequence, with repetition time of 1,500 ms and echo times of 28 and 144 ms. Spectra were analysed using the LCModel fitting routine, thus extracting multiple metabolites, including lipids (Lip) and total choline (tCho). Patients with LACC were treated with NACT and reassessed by MRI at term. Based on tumour volume reduction, patients were classified as good responder (GR; tumour volume reduction > 50%) and poor responder or nonresponder (PR-or-NR; tumour volume reduction ≤ 50%).

Results

Of 17 patients, 11 were LACC. Of these 11, only 6 had both completed NACT and had good-quality ¹H-MR spectra; 3 GR and 3 PR-or-NR. A significant difference in lipid values was observed in the two groups of patients, particularly with higher Lip values and higher Lip/tCho ratio in PR-NR patients (p =0.040). A significant difference was also observed in choline distribution (tCho), with higher values in GR patients (p = 0.040).

Conclusions

Assessment of lipid peak at ¹H-MR spectroscopy could be an additional quantitative parameter in predicting the response to NACT in patients with LACC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-022-00300-1.

PI-RADS: multiparametric MRI in prostate cancer

Multiparametric MRI of the prostate gland has become the initial evaluation of biopsy naïve men with a clinical suspicion for prostate cancer. PI-RADS 2.1 is a joint initiative and framework for prostate MRI acquisition and reporting, which aims to standardize technique and interpretation across centers. Building upon experience accrued following the introduction of PI-RADS 2.0, version 2.1 provides key updates and important clarifications, although it is intended to be an active document, which continues to be updated. Continued advances in our understanding of prostate cancer and progress in imaging technology will undoubtedly shape future iterations of the reporting system.

Quantitative imaging parameters to predict the local staging of prostate cancer in intermediate- to high-risk patients

Objectives

PSMA PET/MRI showed the potential to increase the sensitivity for extraprostatic disease (EPD) assessment over mpMRI; however, the interreader variability for EPD is still high. Therefore, we aimed to assess whether quantitative PSMA and mpMRI imaging parameters could yield a more robust EPD prediction.

Methods

We retrospectively evaluated PCa patients who underwent staging mpMRI and [68Ga]PSMA-PET, followed by radical prostatectomy at our institution between 01.02.2016 and 31.07.2019. Fifty-eight cases with PET/MRI and 15 cases with PET/CT were identified. EPD was determined on histopathology and correlated with quantitative PSMA and mpMRI parameters assessed by two readers: ADC (mm2/1000 s), longest capsular contact (LCC, mm), tumor volume (cm3), PSMA-SUVmax and volume-based parameters using a fixed threshold at SUV > 4 to delineate PSMAtotal (g/ml) and PSMAvol (cm3). The t test was used to compare means, Pearson’s test for categorical correlation, and ROC curve to determine the best cutoff. Interclass correlation (ICC) was performed for interreader agreement (95% CI).

Results

Seventy-three patients were included (64.5 ± 6.0 years; PSA 14.4 ± 17.1 ng/ml), and 31 had EPD (42.5%). From mpMRI, only LCC reached significance (p = 0.005), while both volume-based PET parameters PSMAtotal and PSMAvol were significantly associated with EPD (p = 0.008 and p = 0.004, respectively). On ROC analysis, LCC, PSMAtotal, and PSMAvol reached an AUC of 0.712 (p = 0.002), 0.709 (p = 0.002), and 0.718 (p = 0.002), respectively. ICC was moderate–good for LCC 0.727 (0.565–0.828) and excellent for PSMAtotal and PSMAvol with 0.944 (0.990–0.996) and 0.985 (0.976–0.991), respectively.

Conclusions

Quantitative PSMA parameters have a similar potential as mpMRI LCC to predict EPD of PCa, with a significantly higher interreader agreement.

Safety and Diagnostic Yield of 68Ga Prostate-specific Membrane Antigen PET/CT Guided Robotic-assisted Transgluteal Prostatic Biopsy

Background Transrectal US-guided biopsy with or without MRI fusion is performed for diagnosing prostate cancer (PCa) but has limitations. Gallium 68 (⁶⁸Ga) prostate-specific membrane antigen (PSMA) PET/CT-guided targeted biopsy has the potential to improve diagnostic yield of PCa. Purpose To evaluate the safety and diagnostic yield of ⁶⁸Ga PSMA PET/CT-guided, robotic-arm assisted transgluteal prostatic biopsy. Materials and Methods In this single-center nonrandomized prospective trial, participants with a clinical suspicion of PCa (serum prostate-specific antigen level > 4 ng/mL) were recruited from January 2019 to September 2020. After whole-body ⁶⁸Ga PSMA PET/CT, participants with PSMA-avid intraprostatic lesions underwent PET-guided transgluteal biopsy by using an automated robotic arm. To assess safety and diagnostic yield, procedure-related complications and histopathologic results were documented. Pain during the procedure was scored by a visual analog scale. Descriptive statistics were applied; qualitative variables were reported in percentages. Results Seventy-eight participants (mean age, 66 years ± 7 [standard deviation]; 36 participants [46%] with prior negative results at transrectal US-guided biopsy) were enrolled. Fifty-six (72%) participants had PSMA-avid lesions (prior negative results at transrectal US-guided biopsy in 22 of 56 [39%]) and underwent targeted biopsy. PCa was confirmed in 54 of 56 (96%) participants, and clinically significant PCa (Gleason score ≥ 7) was confirmed in 24 of 54 (44%). Two participants had nonrepresentative samples that required rebiopsy. All participants experienced pain during the procedure, mild (median visual analog scale score, 1; interquartile range, 1-2) in 36 of 56 (64%) and moderate (median visual analog scale score, 5; interquartile range, 5-6) in 20 of 56 (36%). Postprocedure complications were noted in five of 56 (9%) participants and were minor (hematuria, four participants; hematospermia, one participant; and gluteal pain, two participants). No participant developed a postprocedural infection. Conclusion Transgluteal prostate-specific membrane antigen (PSMA) PET/CT-guided, robotic-targeted prostatic biopsy is safe with a high diagnostic yield of prostate cancer for PSMA-avid lesions. Clinical trial registration no. NCT05022576 © RSNA, 2022.

Establishment of an Individualized Predictive Model to Reduce the Core Number for Systematic Prostate Biopsy: A Dual Center Study Based on Stratification of the Disease Risk Score

Purpose

To establish an individualized prostate biopsy model that reduces unnecessary biopsy cores based on multiparameter MRI (mpMRI).

Materials and Methods

This retrospective, non-inferiority dual-center study retrospectively included 609 patients from the Changhai Hospital from June 2017 to November 2020 and 431 patients from the Fujian Union Hospital between 2014 and 2019. Clinical, radiological, and pathological data were analyzed. Data from the Changhai Hospital were used for modeling by calculating the patients’ disease risk scores. Data from the Fujian Union Hospital were used for external verification.

Results

Based on the data of 609 patients from the Changhai Hospital, we divided the patients evenly into five layers according to the disease risk score. The area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) was analyzed. Twelve-core systemic biopsy (12-SBx) was used as the reference standard. The SBx cores from each layer were reduced to 9, 6, 5, 4, and 4. The data of 279 patients with benign pathological results from the Fujian Union Hospital were incorporated into the model. No patients were in the first layer. The accuracies of the models for the other layers were 88, 96.43, 94.87, and 94.59%. The accuracy of each layer would be increased to 96, 100, 100, and 97.30% if the diagnosis of non-clinically significant prostate cancer was excluded.

Conclusions

In this study, we established an individualized biopsy model using data from a dual center. The results showed great accuracy of the model, indicating its future clinical application.

Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study)

INTRODUCTION

Prostate multiparametric MRI (mpMRI) has shown good sensitivity in detecting cancers with an International Society of Urological Pathology (ISUP) grade of ≥2. However, it lacks specificity, and its inter-reader reproducibility remains moderate. Biomarkers, such as the Prostate Health Index (PHI), may help select patients for prostate biopsy. Computer-aided diagnosis/detection (CAD) systems may also improve mpMRI interpretation. Different prototypes of CAD systems are currently developed under the Recherche Hospitalo-Universitaire en Santé / Personalized Focused Ultrasound Surgery of Localized Prostate Cancer (RHU PERFUSE) research programme, tackling challenging issues such as robustness across imaging protocols and magnetic resonance (MR) vendors, and ability to characterise cancer aggressiveness. The study primary objective is to evaluate the non-inferiority of the area under the receiver operating characteristic curve of the final CAD system as compared with the Prostate Imaging-Reporting and Data System V.2.1 (PI-RADS V.2.1) in predicting the presence of ISUP ≥2 prostate cancer in patients undergoing prostate biopsy.

METHODS

This prospective, multicentre, non-inferiority trial will include 420 men with suspected prostate cancer, a prostate-specific antigen level of ≤30 ng/mL and a clinical stage ≤T2 c. Included men will undergo prostate mpMRI that will be interpreted using the PI-RADS V.2.1 score. Then, they will undergo systematic and targeted biopsy. PHI will be assessed before biopsy. At the end of patient inclusion, MR images will be assessed by the final version of the CAD system developed under the RHU PERFUSE programme. Key secondary outcomes include the prediction of ISUP grade ≥2 prostate cancer during a 3-year follow-up, and the number of biopsy procedures saved and ISUP grade ≥2 cancers missed by several diagnostic pathways combining PHI and MRI findings.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the Comité de Protection des Personnes Nord Ouest III (ID-RCB: 2020-A02785-34). After publication of the results, access to MR images will be possible for testing other CAD systems.

TRIAL REGISTRATION NUMBER

NCT04732156.

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

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

Prostate cancer detection by targeted prostate biopsy using the 3D Navigo system: a prospective study

PURPOSE

The 3D Navigo™ system is a magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion device for prostate targeted biopsies (TB). Our aim was to evaluate the clinically significant prostate cancer (CSC) detection rate of TB using the 3D Navigo™ system.

METHODS

Patients who underwent TB with the 3D Navigo™ system in our center between June 2014 and May 2018 were prospectively included, excluding those who have previously received treatment for prostate cancer. A 3-Tesla MRI imaging was performed before biopsies; findings were reported according to the Prostate Imaging Reporting and Data System version 2 (PIRADS). CSC was defined by an ISUP score ≥ 2.

RESULTS

304 patients underwent TB. Median age was 66 years (51-84). Median PSA was 7.75 ng/ml (0.6-70.0). Median prostate volume was 45.0 ml (15.9-221.7). PCa and CSC were found in 70.4% (214/304) and 47.7% (145/304) of the patients, respectively. The proportion of patients diagnosed with CSC among those with PCa was 67.8% (145/214). There was a significant risk of having a CSC in case of PIRADS score ≥ 4 and 5 (OR 5.0, 95% CI [2.7-9.2], P < 0.001; OR 3.2, 95% CI [1.8-5.5], P < 0.001). PIRADS score was an independent risk factor of having a CSC (OR 4.19, 95% CI [2.49-7.05], P < 0.001). There was no significant difference between pathological outcomes of TB and RP in paired analysis (P = 0.892). There was a correlation between TB and RP specimens for PCa detection (r = 0.60, P < 0.001).

CONCLUSION

Detecting CSC with MRI-TRUS fusion targeted biopsies using the 3D Navigo™ system is feasible and safe. We found a positive correlation between TB and RP for ISUP scores.

Use of TDI during MRI/US fusion-guided biopsy for suspected prostate cancer

BACKGROUND

Tissue Doppler imaging (TDI) uses the Doppler principle to quantify the movement of biological tissues.

OBJECTIVE

To investigate the contribution of TDI parameters derived during magnetic resonance imaging and ultrasound (MRI/US) fusion-guided biopsy for prostate cancer (PCa) discrimination.

METHODS

From March 2016 to Dec. 2018, 75 men with suspected PCa prospectively underwent fusion-guided prostate biopsy. TDI overlaid on predefined target lesion were compared to the confirmed contralateral tumor-free area of the prostate gland (using Image J). Diagnostic value of TDI parameters was assessed using histopathology as standard of reference.

RESULTS

Thirty-seven patients were diagnosed with PCa (49.3%), among them 27 with clinically significant PCa (Gleason score >  3 + 3 = 6 (ISUP 1). The LES/REF ratio was lower in confirmed PCa patients compared to patients without PCa (0.42, IQR, 0.22-0.59 vs. 0.52, IQR, 0.40-0.72, p = 0.017). TDI parameters allowed differentiation of low-risk from high-to-intermediate-risk PCa (ISUP 2 versus ISUP 3) based on lower pixel counts within the target ROI (1340, IQR 596-2430 vs. 2687, IQR 2453-3216, p = 0.004), lower pixel percentage (16.4 IQR 11.4-29.5 vs. 27.3, IQR 22.1-39.5; p = 0.005), and lower LES/REF ratios (0.29, IQR 0.19-0.51 vs. 0.52, IQR 0.47-0.74, p = 0.001).

CONCLUSION

TDI of prostate lesions prelocated by MRI discriminates between cancerous and noncancerous lesions and further seems to enable characterization of PCa aggressiveness. This widely available US technique may improve confidence in target lesion localization for tissue sampling.

Active Surveillance Strategies for Low-Grade Prostate Cancer: Comparative Benefits and Cost-effectiveness

Background Active surveillance (AS) is the recommended treatment option for low-risk prostate cancer (PC). Surveillance varies in MRI, frequency of follow-up, and the Prostate Imaging Reporting and Data System (PI-RADS) score that would repeat biopsy. Purpose To compare the effectiveness and cost-effectiveness of AS strategies for low-risk PC with versus without MRI. Materials and Methods This study developed a mathematical model to evaluate the cost-effectiveness of surveillance strategies in a simulation of men with a diagnosis of low-risk PC. The following strategies were compared: watchful waiting, prostate-specific antigen (PSA) and annual biopsy without MRI, and PSA testing and MRI with varied PI-RADS thresholds for biopsy. MRI strategies differed regarding scheduling and use of PI-RADS score of at least 3, or a PI-RADS score of at least 4 to indicate the need for biopsy. Life-years, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios were calculated by using microsimulation. Sensitivity analysis was used to assess the impact of varying parameter values on results. Results For the base case of 60-year-old men, all strategies incorporating prostate MRI extended QALYs and life-years compared with watchful waiting and non-MRI strategies. Annual MRI strategies yielded 16.19 QALYs, annual biopsy with no MRI yielded 16.14 QALYs, and watchful waiting yielded 15.94 QALYs. Annual MRI with PI-RADS score of at least 3 or of at least 4 as the biopsy threshold and annual MRI with biopsy even after MRI with negative findings offered similar QALYs and the same unadjusted life expectancy: 23.05 life-years. However, a PI-RADS score of at least 4 yielded 42% fewer lifetime biopsies. With a cost-effectiveness threshold of $100 000 per QALY, annual MRI with biopsy for lesions with PI-RADS scores of 4 or greater was most cost-effective (incremental cost-effectiveness ratio, $67 221 per QALY). Age, treatment type, risk of initial grade misclassification, and quality-of-life impact of procedural complications affected results. Conclusion The use of active surveillance (AS) with biopsy decisions guided by findings from annual MRI reduces the number of biopsies while preserving life expectancy and quality of life. Biopsy in lesions with PI-RADS scores of 4 or greater is likely the most cost-effective AS strategy for men with low-risk prostate cancer who are younger than 70 years. © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on July 13, 2021.

In-bore MRI-guided prostate biopsy in a patient group with PI-RADS 4 and 5 targets: A single center experience

PURPOSE

To determine the diagnostic yield of magnetic resonance imaging (MRI) guided in-bore biopsy in patients with high likelihood multiparametric MRI (mpMRI) findings, regarding overall and clinically significant prostate cancer (csPCa) detection rates and concordance of biopsy and radical prostatectomy (RP) Gleason scores (GS).

METHODS

This retrospective study consisted of 277 Prostate Imaging Reporting and Data System (PI-RADS) assessment category 4 and 5 targets in 246 patients (mean age, 65.7 years; median prostate specific antigen value, 7.75 ng/mL) who had undergone in-bore biopsy at our institution between 2012 and 2020. Eighty-one patients who underwent RP were eligible for the concordance analysis of biopsy and RP specimen GS.

RESULTS

Overall PCa detection rates were 80.5 % per patient (198/246) and 78 % per target (216/277) and 83.5 % and 67.4 % in primary (biopsy naive) and secondary (at least one negative prior biopsy) settings. csPCa was found in 63 % overall, 66 % of patients (132/200) in the primary, and 50 % of patients (23/46) in the secondary biopsy settings (p < 0.001). The prostate cancer detection rate was 68 % and 92 % in PI-RADS 4 and 5, respectively (p < 0.001). In the radical prostatectomy subcohort, 27.2 % of patients were upgraded, 8.6 % of patients were downgraded from needle biopsy. Significant complications occurred in 1.2 % of patients.

CONCLUSIONS

MRI-guided in-bore prostate biopsy has a high detection rate of csPCa in primary and secondary biopsy cohorts. Biopsy results were satisfactory in terms of the number of positive cores, cancer percentage in positive cores, and concordance of GS in needle biopsy and RP specimen.

Multiparametric MRI in Active Surveillance of Prostate Cancer: An Overview and a Practical Approach

MRI has become important for the detection of prostate cancer. MRI-guided biopsy is superior to conventional systematic biopsy in patients suspected with prostate cancer. MRI is also increasingly used for monitoring patients with low-risk prostate cancer during active surveillance. It improves patient selection for active surveillance at diagnosis, although its role during follow-up is unclear. We aim to review existing evidence and propose a practical approach for incorporating MRI into active surveillance protocols.

Diagnostic Accuracy of 68Ga-PSMA PET/CT for Initial Detection in Patients With Suspected Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND. Early diagnosis is important in the overall management of prostate cancer (PCa). Gallium-68-labeled prostate-specific membrane antigen (PSMA) PET/CT has an established role in the detection of recurrent disease and staging of patients with intermediate- to high-risk PCa. However, only a small number of studies have evaluated its role in the initial diagnosis of PCa. OBJECTIVE. This systematic review was conducted to evaluate the diagnostic performance of 68Ga-PSMA PET/CT in the initial detection of PCa in patients with clinical or biochemical findings suspicious for PCa. EVIDENCE ACQUISITION. This systematic review followed PRISMA guidelines. Searches in PubMed, Scopus, and Embase were conducted using relevant keywords, and articles published through April 30, 2020, were included. Using histopathology results as the reference standard, the numbers of true- and false-positives and true- and false-negatives were extracted. Pooled estimates of diagnostic test accuracy-including sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and summary ROC (SROC) curve-were generated using bivariate random-effects meta-analysis. EVIDENCE SYNTHESIS. Seven studies comprising 389 patients were included in the systematic review and meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio for the initial diagnosis of PCa using 68Ga-PSMA PET/CT were 0.97 (95% CI, 0.90-0.99), 0.66 (95% CI, 0.52-0.78), 2.86 (95% CI, 1.95-4.20), and 0.05 (95% CI, 0.01-0.15), respectively. The test had high accuracy; the area under the SROC curve was 0.91 (95% CI, 0.88-0.93). CONCLUSION. Gallium-68-labeled PSMA PET/CT had excellent sensitivity and negative likelihood ratio in the initial diagnosis of PCa in patients with clinical or biochemical findings suspicious for PCa. CLINICAL IMPACT. Gallium-68-labeled PSMA PET/CT had high diagnostic accuracy for the initial detection of PCa in patients with clinical or biochemical findings suspicious for PCa and has potential utility as a rule-out test for these patients.

Identifying prostate cancer in men with non-suspicious multi-parametric magnetic resonance imaging of the prostate

BACKGROUND

To formulate clinical pathways for identifying clinically significant prostate cancer (csPC) and avoiding insignificant prostate cancer (isPC) in those without suspicious regions of interest on multi-parametric magnetic resonance imaging (mpMRI) of the prostate.

METHODS

A retrospective review identified patients with negative mpMRI who underwent subsequent transperineal prostate biopsy across two centres. Patient characteristics and association with biopsy results were evaluated using univariate and multivariate regression analyses.

RESULTS

A total of 144 patients were identified as having negative mpMRI and undergoing subsequent transperineal prostate biopsy; 18% (25/144) of the cohort were found to have csPC. Logistic regression analysis failed to identify statistically significant predictive factors. In this cohort, if all patients with prostate-specific antigen > 3.0 were biopsied the least amount of csPC is missed, at 20% (5/25) however all isPC would be diagnosed. The least amount of isPC is diagnosed with a biopsy threshold of >15% from the European Randomized Study of Screening for Prostate Cancer calculator with 20% (5/25) of isPC diagnoses made however only 10.5% (2/19) csPC would be diagnosed. A biopsy threshold of >5% risk reduces the number of csPC missed to 37% (7/19) however increases isPC diagnoses to 54% (13/24) of the population.

CONCLUSION

False-negative rates of prostate MRI for csPC are significant within our cohort at 18%. The decision to biopsy should be made in conjunction with a risk profile acceptable by the patient and clinician. The current study demonstrates that there is a need to balance the risk of missing csPC and harm of diagnosing isPC.

Diagnostic pathway of the biopsy-naïve patient suspected for prostate cancer: Real-life scenario when multiparametric Magnetic Resonance Imaging is not centralized

INTRODUCTION

We aimed to compare the pathway including multi-parametric Magnetic Resonance Imaging (mpMRI) versus the one without mpMRI in detection of prostate cancer (PCa) when mpMRI is not centralized.

MATERIALS

January 2019-March 2020: prospective data collection of trans-perineal prostate biopsies. Group A: biopsy-naïve patients who underwent mpMRI (at any institution) versus Group B: patients who did not. Within Group A, patients were stratified into those with negative mpMRI (mpMRI-, PIRADS v2.1=1-3, with PSA density <0.15 if PIRADS 3) who underwent standard biopsy (SB), versus those with positive mpMRI (mpMRI+, when PIRADS 3-5, with PSA density>0.15 if PIRADS 3) who underwent cognitive fusion biopsy.

RESULTS

Two hundred and eighty one biopsies were analyzed. 153 patients underwent mpMRI (Group A). 98 mpMRI+ underwent fusion biopsy; 55 mpMRI- underwent SB. 128 Group B patients underwent SB. Overall PCa detection rate was 52.3% vs. 48.4% (Group A vs. B, P=0.5). Non-clinically-significant PCa was detected in 7.8 vs. 13.3% (Group A vs. B, P=0.1). Among the 98 mpMRI+ Group A patients only 2 had non clinically-significant disease. In 55 mpMRI- patients who underwent SB, 10 (18.2%) had clinically-significant PCa. Prostate volume predicted detection of PCa. In Group B, age and PSA predicted PCa. Sensitivity of mpMRI was 75.0% for all PCa, 85.3% for clinically-significant PCa.

CONCLUSION

Higher detection of PCa and lower detection of non-clinically-significant PCa favored mpMRI pathway. A consistent number of clinically-significant PCa was diagnosed after a mpMRI-. Thus, in real-life scenario, mpMRI- does not obviate indication to biopsy when mpMRI is not centralized.

LEVEL OF EVIDENCE

3.

Characterisation of Prostate Lesions Using Transrectal Shear Wave Elastography (SWE) Ultrasound Imaging: A Systematic Review

BACKGROUND

ultrasound-based shear wave elastography (SWE) can non-invasively assess prostate tissue stiffness. This systematic review aims to evaluate SWE for the detection of prostate cancer (PCa) and compare diagnostic estimates between studies reporting the detection of all PCa and clinically significant PCa (csPCa).

METHODS

a literature search was performed using the MEDLINE, EMBASE, Cochrane Library, ClinicalTrials.gov, and CINAHL databases. Studies evaluating SWE for the detection of PCa using histopathology as reference standard were included.

RESULTS

16 studies including 2277 patients were included for review. Nine studies evaluated SWE for the detection of PCa using systematic biopsy as a reference standard at the per-sample level, with a pooled sensitivity and specificity of 0.85 (95% CI = 0.74-0.92) and 0.85 (95% CI = 0.75-0.91), respectively. Five studies evaluated SWE for the detection of PCa using histopathology of radical prostatectomy (RP) specimens as the reference standard, with a pooled sensitivity and specificity of 0.71 (95% CI = 0.55-0.83) and 0.74 (95% CI = 0.42-0.92), respectively. Sub-group analysis revealed a higher pooled sensitivity (0.77 vs. 0.62) and specificity (0.84 vs. 0.53) for detection of csPCa compared to all PCa among studies using RP specimens as the reference standard.

CONCLUSION

SWE is an attractive imaging modality for the detection of PCa.

Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)-do we still need contrast media?

Objectives

To assess the visibility of clinically significant prostate cancer (PCA) lesions on the sequences multiparametric MRI of the prostate (mpMRI) and to evaluate whether the addition of dynamic contrast–enhanced imaging (DCE) improves the overall visibility.

Methods

We retrospectively evaluated multiparametric MRI images of 119 lesions in 111 patients with biopsy-proven clinically significant PCA. Three readers assigned visual grading scores for visibility on each sequence, and a visual grading characteristic analysis was performed. Linear regression was used to explore which factors contributed to visibility in individual sequences.

Results

The visibility of lesions was significantly better with mpMRI when compared to biparametric MRI in visual grading characteristic (VGC) analysis, with an AUC_VGC of 0.62 (95% CI 0.55–0.69; p < 0.001). This benefit was seen across all readers. Multivariable linear regression revealed that a location in the peripheral zone was associated with better visibility on T2-weighted imaging (T2w). A higher Prostate Imaging-Reporting and Data System (PI-RADS) score was associated with better visibility on both diffusion-weighted imaging (DWI) and DCE. Increased lesion size was associated with better visibility on all sequences.

Conclusions

Visibility of clinically significant PCA is improved by using mpMRI. DCE and DWI images independently improve lesion visibility compared to T2w images alone. Further research into the potential of DCE to impact on clinical decision-making is suggested.

Key Points

• DCE and DWI images independently improve clinically significant prostate cancer lesion visibility compared to T2w images alone.

• Multiparametric MRI (DCE, DWI, T2w) achieved significantly higher visibility scores than biparametric MRI (DWI, T2w).

• Location in the transition zone is associated with poor visibility on T2w, while it did not affect visibility on DWI or DCE.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07494-1.

Rectal gas-induced susceptibility artefacts on prostate diffusion-weighted MRI with epi read-out at 3.0 T: does a preparatory micro-enema improve image quality?

Purpose

To assess whether the application of a preparatory micro-enema reduces gas-induced susceptibility artefacts on diffusion-weighted MRI of the prostate.

Methods

114 consecutive patients who received multiparametric 3 T MRI of the prostate at our institution were retrospectively enrolled. 63 patients self-administered a preparatory micro-enema prior to imaging, and 51 patients underwent MRI without bowel preparation. Two blinded readers independently reviewed the diffusion-weighted sequences regarding gas-induced artefacts. The presence/severity of artefacts was scored ranging from 0 (no artefact) to 3 (severe artefact). A score ≥ 2 was considered a clinically relevant artefact. Maximum rectal width at the level of the prostate was correlated with the administration of a micro-enema. Scores were compared between the scans performed with and without bowel preparation using univariable and multivariable logistic regression, taking into account potential confounding factors (age and prostate volume).

Results

Significantly less artefacts were found on diffusion-weighted sequences after the administration of a micro-enema shortly prior to MR imaging. Clinically relevant artefacts were found in 10% in the patient group after enema, in 41% without enema. If present, artefacts were also significantly less severe. Mean severity score was 0.3 (enema administered) and 1.2 (no enema), and odds ratio was 0.137 (p < 0.0001) in univariable ordinal logistic regression. Inter-observer agreement was excellent (κ 0.801).

Conclusion

The use of a preparatory micro-enema prior to 3 T multiparametric prostate MRI significantly reduces both the incidence and severity of gas-induced artefacts on diffusion-weighted sequences and thus improves image quality.

Electronic supplementary material

The online version of this article (10.1007/s00261-020-02600-9) contains supplementary material, which is available to authorized users.

EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

OBJECTIVE

To present a summary of the 2020 version of the European Association of Urology (EAU)-European Association of Nuclear Medicine (EANM)-European Society for Radiotherapy and Oncology (ESTRO)-European Society of Urogenital Radiology (ESUR)-International Society of Geriatric Oncology (SIOG) guidelines on screening, diagnosis, and local treatment of clinically localised prostate cancer (PCa).

EVIDENCE ACQUISITION

The panel performed a literature review of new data, covering the time frame between 2016 and 2020. The guidelines were updated and a strength rating for each recommendation was added based on a systematic review of the evidence.

EVIDENCE SYNTHESIS

A risk-adapted strategy for identifying men who may develop PCa is advised, generally commencing at 50 yr of age and based on individualised life expectancy. Risk-adapted screening should be offered to men at increased risk from the age of 45 yr and to breast cancer susceptibility gene (BRCA) mutation carriers, who have been confirmed to be at risk of early and aggressive disease (mainly BRAC2), from around 40 yr of age. The use of multiparametric magnetic resonance imaging in order to avoid unnecessary biopsies is recommended. When a biopsy is performed, a combination of targeted and systematic biopsies must be offered. There is currently no place for the routine use of tissue-based biomarkers. Whilst prostate-specific membrane antigen positron emission tomography computed tomography is the most sensitive staging procedure, the lack of outcome benefit remains a major limitation. Active surveillance (AS) should always be discussed with low-risk patients, as well as with selected intermediate-risk patients with favourable International Society of Urological Pathology (ISUP) 2 lesions. Local therapies are addressed, as well as the AS journey and the management of persistent prostate-specific antigen after surgery. A strong recommendation to consider moderate hypofractionation in intermediate-risk patients is provided. Patients with cN1 PCa should be offered a local treatment combined with long-term hormonal treatment.

CONCLUSIONS

The evidence in the field of diagnosis, staging, and treatment of localised PCa is evolving rapidly. The 2020 EAU-EANM-ESTRO-ESUR-SIOG guidelines on PCa summarise the most recent findings and advice for their use in clinical practice. These PCa guidelines reflect the multidisciplinary nature of PCa management.

PATIENT SUMMARY

Updated prostate cancer guidelines are presented, addressing screening, diagnosis, and local treatment with curative intent. These guidelines rely on the available scientific evidence, and new insights will need to be considered and included on a regular basis. In some cases, the supporting evidence for new treatment options is not yet strong enough to provide a recommendation, which is why continuous updating is important. Patients must be fully informed of all relevant options and, together with their treating physicians, decide on the most optimal management for them.

Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in the Detection of Clinically Significant Prostate Cancer in the Prostate Imaging Reporting and Data System Era: A Systematic Review and Meta-analysis

CONTEXT

Prebiopsy multiparametric magnetic resonance imaging (mpMRI) is increasingly used in prostate cancer diagnosis. The reported negative predictive value (NPV) of mpMRI is used by some clinicians to aid in decision making about whether or not to proceed to biopsy.

OBJECTIVE

We aim to perform a contemporary systematic review that reflects the latest literature on optimal mpMRI techniques and scoring systems to update the NPV of mpMRI for clinically significant prostate cancer (csPCa).

EVIDENCE ACQUISITION

We conducted a systematic literature search and included studies from 2016 to September 4, 2019, which assessed the NPV of mpMRI for csPCa, using biopsy or clinical follow-up as the reference standard. To ensure that studies included in this analysis reflect contemporary practice, we only included studies in which mpMRI findings were interpreted according to the Prostate Imaging Reporting and Data System (PIRADS) or similar Likert grading system. We define negative mpMRI as either (1) PIRADS/Likert 1-2 or (2) PIRADS/Likert 1-3; csPCa was defined as either (1) Gleason grade group ≥2 or (2) Gleason grade group ≥3. We calculated NPV separately for each combination of negative mpMRI and csPCa.

EVIDENCE SYNTHESIS

A total of 42 studies with 7321 patients met our inclusion criteria and were included for analysis. Using definition (1) for negative mpMRI and csPCa, the pooled NPV for biopsy-naïve men was 90.8% (95% confidence interval [CI] 88.1-93.1%). When defining csPCa using definition (2), the NPV for csPCa was 97.1% (95% CI 94.9-98.7%). Calculation of the pooled NPV using definition (2) for negative mpMRI and definition (1) for csPCa yielded the following: 86.8% (95% CI 80.1-92.4%). Using definition (2) for both negative mpMRI and csPCa, the pooled NPV from two studies was 96.1% (95% CI 93.4-98.2%).

CONCLUSIONS

Multiparametric MRI of the prostate is generally an accurate test for ruling out csPCa. However, we observed heterogeneity in the NPV estimates, and local institutional data should form the basis of decision making if available.

PATIENT SUMMARY

The negative predictive values should assist in decision making for clinicians considering not proceeding to biopsy in men with elevated age-specific prostate-specific antigen and multiparametric magnetic resonance imaging reported as negative (or equivocal) on Prostate Imaging Reporting and Data System/Likert scoring. Some 7-10% of men, depending on the setting, will miss a diagnosis of clinically significant cancer if they do not proceed to biopsy. Given the institutional variation in results, it is of upmost importance to base decision making on local data if available.

Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study

PURPOSE

To investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN).

MATERIALS AND METHODS

Raw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy images using two averages and reference images using all 16 averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DW image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb DW images. A cumulative link mixed regression model was used to compare the readers' scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland-Altman analysis.

RESULTS

Compared with the original DnCNN, the guided DnCNN produced denoised hb DW images with higher peak signal-to-noise ratio (32.79 ± 3.64 [standard deviation] vs 33.74 ± 3.64), higher structural similarity index (0.92 ± 0.05 vs 0.93 ± 0.04), and lower normalized mean square error (3.9% ± 10 vs 1.6% ± 1.5) (P < .001 for all). Compared with the reference images, the denoised images received higher image quality scores from the readers (P < .0001). The ADC values based on the denoised hb DW images were in good agreement with the reference ADC values (mean ADC difference ranged from -0.04 to 0.02 × 10^-3 mm²/sec).

CONCLUSION

Accelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible. Supplemental material is available for this article. © RSNA, 2020.

Prostate cancer screening research can benefit from network medicine: an emerging awareness

Up to date, screening for prostate cancer (PCa) remains one of the most appealing but also a very controversial topics in the urological community. PCa is the second most common cancer in men worldwide and it is universally acknowledged as a complex disease, with a multi-factorial etiology. The pathway of PCa diagnosis has changed dramatically in the last few years, with the multiparametric magnetic resonance (mpMRI) playing a starring role with the introduction of the “MRI Pathway”. In this scenario the basic tenet of network medicine (NM) that sees the disease as perturbation of a network of interconnected molecules and pathways, seems to fit perfectly with the challenges that PCa early detection must face to advance towards a more reliable technique. Integration of tests on body fluids, tissue samples, grading/staging classification, physiological parameters, MR multiparametric imaging and molecular profiling technologies must be integrated in a broader vision of “disease” and its complexity with a focus on early signs. PCa screening research can greatly benefit from NM vision since it provides a sound interpretation of data and a common language, facilitating exchange of ideas between clinicians and data analysts for exploring new research pathways in a rational, highly reliable, and reproducible way.

Advanced ultrasound in the diagnosis of prostate cancer

The diagnosis of prostate cancer (PCa) can be challenging due to the limited performance of current diagnostic tests, including PSA, digital rectal examination and transrectal conventional US. Multiparametric MRI has improved PCa diagnosis and is recommended prior to biopsy; however, mp-MRI does miss a substantial number of PCa. Advanced US modalities include transrectal prostate elastography and contrast-enhanced US, as well as improved B-mode, micro-US and micro-Doppler techniques. These techniques can be combined to define a novel US approach, multiparametric US (mp-US). Mp-US improves PCa diagnosis but is not sufficiently accurate to obviate the utility of mp-MRI. Mp-US using advanced techniques and mp-MRI provide complementary information which will become even more important in the era of focal therapy, where precise identification of PCa location is needed.

Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer

BACKGROUND

Multiparametric magnetic resonance imaging (MRI), with or without MRI-targeted biopsy, is an alternative test to systematic transrectal ultrasonography-guided biopsy in men suspected of having prostate cancer. At present, evidence on which test to use is insufficient to inform detailed evidence-based decision-making.

OBJECTIVES

To determine the diagnostic accuracy of the index tests MRI only, MRI-targeted biopsy, the MRI pathway (MRI with or without MRI-targeted biopsy) and systematic biopsy as compared to template-guided biopsy as the reference standard in detecting clinically significant prostate cancer as the target condition, defined as International Society of Urological Pathology (ISUP) grade 2 or higher. Secondary target conditions were the detection of grade 1 and grade 3 or higher-grade prostate cancer, and a potential change in the number of biopsy procedures.

SEARCH METHODS

We performed a comprehensive systematic literature search up to 31 July 2018. We searched CENTRAL, MEDLINE, Embase, eight other databases and one trials register.

SELECTION CRITERIA

We considered for inclusion any cross-sectional study if it investigated one or more index tests verified by the reference standard, or if it investigated the agreement between the MRI pathway and systematic biopsy, both performed in the same men. We included only studies on men who were biopsy naïve or who previously had a negative biopsy (or a mix of both). Studies involving MRI had to report on both MRI-positive and MRI-negative men. All studies had to report on the primary target condition.

DATA COLLECTION AND ANALYSIS

Two reviewers independently extracted data and assessed the risk of bias using the QUADAS-2 tool. To estimate test accuracy, we calculated sensitivity and specificity using the bivariate model. To estimate agreement between the MRI pathway and systematic biopsy, we synthesised detection ratios by performing random-effects meta-analyses. To estimate the proportions of participants with prostate cancer detected by only one of the index tests, we used random-effects multinomial or binary logistic regression models. For the main comparisions, we assessed the certainty of evidence using GRADE.

MAIN RESULTS

The test accuracy analyses included 18 studies overall.MRI compared to template-guided biopsy: Based on a pooled sensitivity of 0.91 (95% confidence interval (CI): 0.83 to 0.95; 12 studies; low certainty of evidence) and a pooled specificity of 0.37 (95% CI: 0.29 to 0.46; 12 studies; low certainty of evidence) using a baseline prevalence of 30%, MRI may result in 273 (95% CI: 249 to 285) true positives, 441 false positives (95% CI: 378 to 497), 259 true negatives (95% CI: 203 to 322) and 27 (95% CI: 15 to 51) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations and inconsistency.MRI-targeted biopsy compared to template-guided biopsy: Based on a pooled sensitivity of 0.80 (95% CI: 0.69 to 0.87; 8 studies; low certainty of evidence) and a pooled specificity of 0.94 (95% CI: 0.90 to 0.97; 8 studies; low certainty of evidence) using a baseline prevalence of 30%, MRI-targeted biopsy may result in 240 (95% CI: 207 to 261) true positives, 42 (95% CI: 21 to 70) false positives, 658 (95% CI: 630 to 679) true negatives and 60 (95% CI: 39 to 93) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations and inconsistency.The MRI pathway compared to template-guided biopsy: Based on a pooled sensitivity of 0.72 (95% CI: 0.60 to 0.82; 8 studies; low certainty of evidence) and a pooled specificity of 0.96 (95% CI: 0.94 to 0.98; 8 studies; low certainty of evidence) using a baseline prevalence of 30%, the MRI pathway may result in 216 (95% CI: 180 to 246) true positives, 28 (95% CI: 14 to 42) false positives, 672 (95% CI: 658 to 686) true negatives and 84 (95% CI: 54 to 120) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations, inconsistency and imprecision.Systemic biopsy compared to template-guided biopsy: Based on a pooled sensitivity of 0.63 (95% CI: 0.19 to 0.93; 4 studies; low certainty of evidence) and a pooled specificity of 1.00 (95% CI: 0.91 to 1.00; 4 studies; low certainty of evidence) using a baseline prevalence of 30%, systematic biopsy may result in 189 (95% CI: 57 to 279) true positives, 0 (95% CI: 0 to 63) false positives, 700 (95% CI: 637 to 700) true negatives and 111 (95% CI: 21 to 243) false negatives per 1000 men. We downgraded the certainty of evidence for study limitations and inconsistency.Agreement analyses: In a mixed population of both biopsy-naïve and prior-negative biopsy men comparing the MRI pathway to systematic biopsy, we found a pooled detection ratio of 1.12 (95% CI: 1.02 to 1.23; 25 studies). We found pooled detection ratios of 1.44 (95% CI 1.19 to 1.75; 10 studies) in prior-negative biopsy men and 1.05 (95% CI: 0.95 to 1.16; 20 studies) in biopsy-naïve men.

AUTHORS' CONCLUSIONS

Among the diagnostic strategies considered, the MRI pathway has the most favourable diagnostic accuracy in clinically significant prostate cancer detection. Compared to systematic biopsy, it increases the number of significant cancer detected while reducing the number of insignificant cancer diagnosed. The certainty in our findings was reduced by study limitations, specifically issues surrounding selection bias, as well as inconsistency. Based on these findings, further improvement of prostate cancer diagnostic pathways should be pursued.