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ObjectivesThis study aims to define consensus-based criteria for acquiring and reporting prostate MRI and establishing prerequisites for image quality.MethodsA total of 44 leading urologists and urogenital radiologists who are experts in prostate cancer imaging from the European Society of Urogenital Radiology (ESUR) and EAU Section of Urologic Imaging (ESUI) participated in a Delphi consensus process. Panellists completed two rounds of questionnaires with 55 items under three headings: image quality assessment, interpretation and reporting, and radiologists’ experience plus training centres. Of 55 questions, 31 were rated for agreement on a 9-point scale, and 24 were multiple-choice or open. For agreement items, there was consensus agreement with an agreement ≥ 70% (score 7–9) and disagreement of ≤ 15% of the panellists. For the other questions, a consensus was considered with ≥ 50% of votes.ResultsTwenty-four out of 31 of agreement items and 11/16 of other questions reached consensus. Agreement statements were (1) reporting of image quality should be performed and implemented into clinical practice; (2) for interpretation performance, radiologists should use self-performance tests with histopathology feedback, compare their interpretation with expert-reading and use external performance assessments; and (3) radiologists must attend theoretical and hands-on courses before interpreting prostate MRI. Limitations are that the results are expert opinions and not based on systematic reviews or meta-analyses. There was no consensus on outcomes statements of prostate MRI assessment as quality marker.ConclusionsAn ESUR and ESUI expert panel showed high agreement (74%) on issues improving prostate MRI quality. Checking and reporting of image quality are mandatory. Prostate radiologists should attend theoretical and hands-on courses, followed by supervised education, and must perform regular performance assessments.Key Points• Multi-parametric MRI in the diagnostic pathway of prostate cancer has a well-established upfront role in the recently updated European Association of Urology guideline and American Urological Association recommendations.• Suboptimal image acquisition and reporting at an individual level will result in clinicians losing confidence in the technique and returning to the (non-MRI) systematic biopsy pathway. Therefore, it is crucial to establish quality criteria for the acquisition and reporting of mpMRI.• To ensure high-quality prostate MRI, experts consider checking and reporting of image quality mandatory. Prostate radiologists must attend theoretical and hands-on courses, followed by supervised education, and must perform regular self- and external performance assessments.

PurposeProstate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) is used for (re)staging prostate cancer (PCa) and as a biomarker for evaluating response to therapy, but lacks established response criteria. A panel of PCa experts in nuclear medicine, radiology, and/or urology met on February 21, 2020, in Amsterdam, The Netherlands, to formulate criteria for PSMA PET/CT-based response in patients treated for metastatic PCa and optimal timing to use it.MethodsPanelists received thematic topics and relevant literature prior to the meeting. Statements on how to interpret response and progression on therapy in PCa with PSMA PET/CT and when to use it were developed. Panelists voted anonymously on a nine-point scale, ranging from strongly disagree (1) to strongly agree (9). Median scores described agreement and consensus.ResultsPSMA PET/CT consensus statements concerned utility, best timing for performing, criteria for evaluation of response, patients who could benefit, and handling of radiolabeled PSMA PET tracers. Consensus was reached on all statements. PSMA PET/CT can be used before and after any local and systemic treatment in patients with metastatic disease to evaluate response to treatment. Ideally, PSMA PET/CT imaging criteria should categorize patients as responders, patients with stable disease, partial response, and complete response, or as non-responders. Specific clinical scenarios such as oligometastatic or polymetastatic disease deserve special consideration.ConclusionsAdoption of PSMA PET/CT should be supported by indication for appropriate use and precise criteria for interpretation. PSMA PET/CT criteria should categorize patients as responders or non-responders. Specific clinical scenarios deserve special consideration.

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

BackgroundMultiparametric MRI (mpMRI) became recognised in investigating those with suspected prostate cancer between 2010 and 2012; in the USA, the preventative task force moratorium on PSA screening was a strong catalyst. In a few short years, it has been adopted into daily urological and oncological practice. The pace of clinical uptake, born along by countless papers proclaiming high accuracy in detecting clinically significant prostate cancer, has sparked much debate about the timing of mpMRI within the traditional biopsy-driven clinical pathways. There are strongly held opposing views on using mpMRI as a triage test regarding the need for biopsy and/or guiding the biopsy pattern.ObjectiveTo review the evidence base and present a position paper on the role of mpMRI in the diagnosis and management of prostate cancer.MethodsA subgroup of experts from the ESUR Prostate MRI Working Group conducted literature review and face to face and electronic exchanges to draw up a position statement.ResultsThis paper considers diagnostic strategies for clinically significant prostate cancer; current national and international guidance; the impact of pre-biopsy mpMRI in detection of clinically significant and clinically insignificant neoplasms; the impact of pre-biopsy mpMRI on biopsy strategies and targeting; the notion of mpMRI within a wider risk evaluation on a patient by patient basis; the problems that beset mpMRI including inter-observer variability.ConclusionsThe paper concludes with a set of suggestions for using mpMRI to influence who to biopsy and who not to biopsy at diagnosis.Key Points• Adopt mpMRI as the first, and primary, investigation in the workup of men with suspected prostate cancer.• PI-RADS assessment categories 1 and 2 have a high negative predictive value in excluding significant disease, and systematic biopsy may be postponed, especially in men with low-risk of disease following additional risk stratification.• PI-RADS assessment category lesions 4 and 5 should be targeted; PI-RADS assessment category lesion 3 may be biopsied as a target, as part of systematic biopsies or may be observed depending on risk stratification.

Key Points • Before a prostate biopsy, the likely benefits and the harms emanating from true and false test MRI results need to be balanced. Prioritizing patients’ preferences and their tolerance to potential harms are essential to assess. • The decision curve analysis method is an analytical framework where the net clinical benefit is plotted against a range of risk thresholds of having important cancers, helping patients and their physicians to decide between cancer averse (important cancers being detected) and biopsy averse (biopsies avoided) strategies. • The decision curve analysis method showed that the incorporation of clinical risk factors with MRI findings optimizes biopsy outcomes over a range of clinically relevant risk thresholds, compared to other biopsy strategies.

Objectives To assess factors influencing prostate cancer detection on multiparametric (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced) MRI. Methods One hundred and seventy-five patients who underwent radical prostatectomy were included. Pre-operative MRI performed at 1.5 T ( n  = 71) or 3 T ( n  = 104), with ( n  = 58) or without ( n  = 117) an endorectal coil were independently interpreted by two radiologists. A five-point subjective suspicion score (SSS) was assigned to all focal abnormalities (FAs). MR findings were then compared with whole-mount sections. Results Readers identified 192–214/362 cancers, with 130–155 false positives. Detection rates for tumours of <0.5 cc (cm 3 ), 0.5–2 cc and >2 cc were 33–45/155 (21–29 %), 15–19/35 (43–54 %) and 8–9/12 (67–75 %) for Gleason ≤6, 17/27 (63 %), 42–45/51 (82–88 %) and 34/35 (97 %) for Gleason 7 and 4/5 (80 %), 13/14 (93 %) and 28/28 (100 %) for Gleason ≥8 cancers respectively. At multivariate analysis, detection rates were influenced by tumour Gleason score, histological volume, histological architecture and location ( P  < 0.0001), but neither by field strength nor coils used for imaging. The SSS was a significant predictor of both malignancy of FAs ( P  < 0.005) and aggressiveness of tumours ( P  < 0.00001). Conclusions Detection rates were significantly influenced by tumour characteristics, but neither by field strength nor coils used for imaging. The SSS significantly stratified the risk of malignancy of FAs and aggressiveness of detected tumours. Key Points • Prostate cancer volume, Gleason score, architecture and location are MRI predictors of detection. • Field strength and coils used do not influence the tumour detection rate. • Multiparametric MRI is accurate for detecting aggressive tumours. • A subjective suspicion score can stratify the risk of malignancy and tumour aggressiveness.

Active surveillance (AS) is the recommended treatment option for low-risk and favourable intermediate-risk prostate cancer management, preserving oncological and functional outcomes. However, active monitoring using relevant parameters in addition to the usual clinical, biological and pathological considerations is necessary to compensate for initial undergrading of the tumour or to detect early progression without missing the opportunity to provide curative therapy. Indeed, several studies have raised concerns about inadequate biopsy sampling at diagnosis. However, the implementation of baseline MRI and targeted biopsy have led to improved initial stratification of low-risk disease; baseline MRI correlates well with disease characteristics and AS outcomes. The use of follow-up MRI during the surveillance phase also raises the question of the requirement for serial biopsies in the absence of radiological progression and the possibility of using completely MRI-based surveillance, with triggers for biopsies based solely on MRI findings. This concept of a tailored-risk, imaging-based monitoring strategy is aimed at reducing invasive procedures. However, the abandonment of serial biopsies in the absence of MRI progression can probably not yet be recommended in routine practice, as the data from real-life cohorts are heterogeneous and inconclusive. Thus, the evolution towards a routine, fully MRI-guided AS pathway has to be preceded by ensuring quality programme assessment for MRI reading and by demonstrating its safety in prospective trials.Active surveillance is recommended for low-risk and favourable intermediate-risk prostate cancer management, but active monitoring using imaging or biopsy is necessary to compensate for initial undergrading of the tumour or to detect early progression without missing the opportunity to provide curative therapy. In this Review, the authors discuss the potential for MRI-based active monitoring for active surveillance and consider how this approach might affect patient care.

Objectives To measure benign and malignant prostate tissue stiffness using shear-wave elastography (SWE). Methods Thirty consecutive patients underwent transrectal SWE in the axial and sagittal planes before prostatectomy. After reviewing prostatectomy specimens, two radiologists measured stiffness in regions corresponding to cancers, lateral and median benign peripheral zone (PZ) and benign transition zone (TZ). Results Cancers were stiffer than benign PZ and TZ. All tissue classes were stiffer on sagittal than on axial imaging, in TZ than in PZ, and in median PZ than in lateral PZ. At multivariate analysis, the nature of tissue (benign or malignant; P  < 0.00001), the imaging plane (axial or sagittal; P  < 0.00001) and the location within the prostate (TZ, median PZ or lateral PZ; P  = 0.0065) significantly and independently influenced tissue stiffness. On axial images, the thresholds maximising the Youden index in TZ, lateral PZ and median PZ were respectively 62 kPa, 33 kPa and 49 kPa. On sagittal images, the thresholds were 76 kPa, 50 kPa and 72 kPa, respectively. Conclusions SWE can distinguish prostate malignant and benign tissues. Tissue stiffness is influenced by the imaging plane and the location within the gland. Key Points • Prostate cancers were stiffer than the benign peripheral zone • All tissue classes were stiffer on sagittal than on axial imaging • All tissue classes were stiffer in the transition zone than in the peripheral zone • All tissue classes were stiffer in the median than in the lateral peripheral zone • Taking into account imaging plane and zonal anatomy can improve cancer detection

Objective To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation. Methods The algorithm, combining model-based and deep learning–based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm’s mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression. Results Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm’s median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists’ delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation. Conclusions The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging. Key Points • Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma) . • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks . • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists .