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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.

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.

ObjectivesThe aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.MethodsThis study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.ResultsThe multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.ConclusionsIn this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.Key Points• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible.• Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities.• Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

ObjectiveTo compare the detection rate of clinically significant cancer (CSCa) by magnetic resonance imaging-targeted biopsy (MRI-TB) with that by standard systematic biopsy (SB) and to evaluate the role of MRI-TB as a replacement from SB in men at clinical risk of prostate cancer.MethodsThe non-systematic literature was searched for peer-reviewed English-language articles using PubMed, including the prospective paired studies, where the index test was MRI-TB and the comparator text was SB. Also the randomized clinical trials (RCTs) are included if one arm was MRI-TB and another arm was SB.ResultsEighteen prospective studies used both MRI-TB and TRUS-SB, and eight RCT received one of the tests for prostate cancer detection. In most prospective trials to compare MRI-TB vs. SB, there was no significant difference in any cancer detection rate; however, MRI-TB detected more men with CSCa and fewer men with CISCa than SB.ConclusionMRI-TB is superior to SB in detection of CSCa. Since some significant cancer was detected by SB only, a combination of SB with the TB technique would avoid the underdiagnosis of CSCa.

BackgroundWith growing interest in focal therapy (FT) of prostate cancer (PCa) there is an increasing armamentarium of treatment modalities including high-intensity focused ultrasound (HIFU), cryotherapy, focal laser ablation (FLA), irreversible electroporation (IRE), vascular targeted photodynamic therapy (VTP), focal brachytherapy (FBT) and stereotactic ablative radiotherapy (SABR). Currently there are no clear recommendations as to which of these technologies are appropriate for individual patient characteristics. Our intention was to review the literature for special aspects of the different technologies that might be of advantage depending on individual patient and tumour characteristics.MethodsThe current literature on FT was screened for the following factors: morbidity, repeatability, tumour risk category, tumour location, tumour size and prostate volume and anatomical issues. The ESUT expert panel arrived at consensus regarding a position statement on a structured pathway for available FT technologies based on a combination of the literature and expert opinion.ResultsSide effects were low across different studies and FT modalities with urinary continence rates of 90–100% and erectile dysfunction between 5 and 52%. Short to medium cancer control based on post-treatment biopsies were variable between ablative modalities. Expert consensus suggested that posterior lesions are better amenable to FT using HIFU. Cryotherapy provides best possible outcomes for anterior tumours. Apical lesions, when treated with FBT, may yield the least urethral morbidity.ConclusionsFurther prospective trials are required to assess medium to long term disease control of different ablative modalities for FT. Amongst different available FT modalities our ESUT expert consensus suggests that some may be better for diffe`rent tumour locations. Tumour risk, tumour size, tumour location, and prostate volume are all important factors to consider and might aid in designing future FT trials.

ContextThe value of Histoscanning™ (HS) in prostate cancer (PCa) imaging is much debated, although it has been used in clinical practice for more than 10 years now.ObjectiveTo summarize the data on HS from various PCa diagnostic perspectives to determine its potential.Materials and methodsWe performed a systematic search using 2 databases (Medline and Scopus) on the query “Histoscan*”. The primary endpoint was HS accuracy. The secondary endpoints were: correlation of lesion volume by HS and histology, ability of HS to predict extracapsular extension or seminal vesicle invasion.ResultsHS improved cancer detection rate “per core”, OR = 16.37 (95% CI 13.2; 20.3), p < 0.0001, I2 = 98% and “per patient”, OR = 1.83 (95% CI 1.51; 2.21), p < 0.0001, I2 = 95%. The pooled accuracy was markedly low: sensitivity − 0.2 (95% CI 0.19–0.21), specificity − 0.12 (0.11–0.13), AUC 0.12. 8 of 10 studiers showed no additional value for HS. The pooled accuracy with histology after RP was relatively better, yet still very low: sensitivity − 0.56 (95% CI 0.5–0.63), specificity − 0.23 (0.18–0.28), AUC 0.4. 9 of 12 studies did not show any benefit of HS.ConclusionThis meta-analysis does not see the incremental value in comparing prostate Histoscanning with conventional TRUS in prostate cancer screening and targeted biopsy. HS proved to be slightly more accurate in predicting extracapsular extension on RP, but the available data does not allow us to draw any conclusions on its effectiveness in practice.Patient summaryHistoscanning is a modification of ultrasound for prostate cancer visualization. The available data suggest its low accuracy in screening and detecting of prostate cancer.

Background The diagnostic pathway for prostate cancer (PCa) is advancing towards an imaging-driven approach. Multiparametric magnetic resonance imaging, although increasingly used, has not shown sufficient accuracy to replace biopsy for now. The introduction of new ultrasound (US) modalities, such as quantitative contrast-enhanced US (CEUS) and shear wave elastography (SWE), shows promise but is not evidenced by sufficient high quality studies, especially for the combination of different US modalities. The primary objective of this study is to determine the individual and complementary diagnostic performance of greyscale US (GS), SWE, CEUS and their combination, multiparametric ultrasound (mpUS), for the detection and localization of PCa by comparison with corresponding histopathology. Methods/design In this prospective clinical trial, US imaging consisting of GS, SWE and CEUS with quantitative mapping on 3 prostate imaging planes (base, mid and apex) will be performed in 50 patients with biopsy-proven PCa before planned radical prostatectomy using a clinical ultrasound scanner. All US imaging will be evaluated by US readers, scoring the four quadrants of each imaging plane for the likelihood of significant PCa based on a 1 to 5 Likert Scale. Following resection, PCa tumour foci will be identified, graded and attributed to the imaging-derived quadrants in each prostate plane for all prostatectomy specimens. Primary outcome measure will be the sensitivity, specificity, negative predictive value and positive predictive value of each US modality and mpUS to detect and localize significant PCa evaluated for different Likert Scale thresholds using receiver operating characteristics curve analyses. Discussion In the evaluation of new PCa imaging modalities, a structured comparison with gold standard radical prostatectomy specimens is essential as first step. This trial is the first to combine the most promising ultrasound modalities into mpUS. It complies with the IDEAL stage 2b recommendations and will be an important step towards the evaluation of mpUS as a possible option for accurate detection and localization of PCa. Trial registration The study protocol for multiparametric ultrasound was prospectively registered on Clinicaltrials.gov on 14 March 2017 with the registry name ‘Multiparametric Ultrasound-Study for the Detection of Prostate Cancer’ and trial registration number NCT03091231

Lysophosphatidylcholine acyltransferase 1 (LPCAT1) has been suggested to play a role in cancer. To assess its role in prostate cancer, LPCAT1 expression was analyzed on a tissue microarray containing samples from 11,152 prostate cancer patients. In benign prostate glands, LPCAT1 immunostaining was absent or weak. In prostate cancer, LPCAT1 positivity was found in 73.8% of 8786 interpretable tumors including 29.2% with strong expression. Increased LPCAT1 expression was associated with advanced tumor stage (pT3b/T4) (p < 0.0001), high Gleason score (≥4 + 4) (p < 0.0001), positive nodal involvement (p = 0.0002), positive surgical margin (p = 0.0005), and early PSA recurrence (p < 0.0001). High LPCAT1 expression was strongly linked to ERG-fusion type prostate cancer. Strong LPCAT1 staining was detected in 45.3% of ERG positive but in only 16.7% of ERG negative tumors (p < 0.0001). Within ERG negative cancers, LPCAT1 staining was strongly increased within the subgroup of PTEN deleted cancers (p < 0.0001). Further subgroup analyses revealed that associations of high LPCAT1 expression with PSA recurrence and unfavorable tumor phenotype were largely driven by ERG negative cancers (p < 0.0001) while these effects were substantially mitigated in ERG positive cancers (p = 0.0073). The prognostic impact of LPCAT1 expression was independent of histological and clinical parameters. It is concluded, that LPCAT1 measurement, either alone or in combination, may be utilized for better clinical decision-making. These data also highlight the potentially important role of lipid metabolism in prostate cancer biology. •Increased LPCAT1 expression was associated with unfavorable tumor phenotype and early PSA recurrence in prostate cancers.•High LPCAT1 expression was strongly linked to ERG-fusion type prostate cancer.•Within ERG negative cancers, LPCAT1 staining was strongly increased within the subgroup of PTEN deleted cancers.•LPCAT1 measurement, either alone or in combination, may be utilized for better clinical decision-making.

Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging—Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5–10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4–6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341. Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87–0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83–0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6–63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3–92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3–72·4] vs 69·0% [65·5–72·5]) at the same sensitivity (96·1%, 94·0–98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (−0·04) was greater than the non-inferiority margin (−0·05) and a p value below the significance threshold was reached (p<0·001). An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system. Health~Holland and EU Horizon 2020.

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. Clinical relevance statement High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. Key Points High-quality images are essential for prostate cancer diagnosis and management using MRI . PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium . PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI .