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Background: 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., Methods: 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., Findings: 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)., Interpretation: 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., Funding: Health~Holland and EU Horizon 2020., Competing Interests: Declaration of interests NAO provides statistical consultation to Siemens Healthineers, Takeda, and Qure, and serves as a committee member of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network, the Tomosynthesis Mammographic Imaging Screening Trial, and the National Cancer Institute's Clinical Imaging Steering Committee (Bethesda, MD, USA). AB has been a consultant and advisor for Astellas and Bayer; board membership, officer, and trustee for Glactone Pharma, and LIDDS Pharma; has received lecture honoraria for Accord, Astellas, AstraZeneca, Bayer, Ipsen, Janssen, and Merck; has participated in trials run by Astellas, Ferring, and Janssen; and holds stock in Glactone Pharma, LIDDS Pharma, Noviga, and WntResearch. BvG holds stocks in and is a founder of Thirona. JKC has received research funding from GE Healthcare and Genentech and is the co-inventor of software that has been licensed to Siloam Vision. JKC has equity ownership in Siloam Vision. GS has been an advisory board member of Exact Imaging and Angiogenesis and has received lecture honorarium from Hitachi. OR has received funding for travel expenses from Philips Medical Systems. ARP has received research funding from Siemens Healthineers, holds stocks in Lucida Medical, and has received lecture honoraria for Siemens Healthineers and Bayer. HH has received research funding from Siemens Healthineers and Canon Medical Systems. GV has been a clinical advisory board member of AGFA Healthcare. VK has received lecture honoraria on prostate cancer diagnosis from the European Association of Urology and Singapore Urology Association and has received research funding from Prostate Cancer UK and the John Black Charitable Foundation. DB has received lecture honorarium from Bayer Vital and holds stocks in NVIDIA, Microsoft, and MSCI-World ETF. RvdB has been an advisory board member for Janssen; has received lecture honoraria from Amgen, Astellas, Ipsen, Janssen, and MSD; has received research support from Astellas and Janssen; and has participated in trials run by Janssen. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

At the so-called in-bore, MRI-guided prostate biopsy, the radiologist in the MRI suite manually directs a rectal biopsy needle guide at an abnormality confirmed by a previous prostate MRI. This manual technique of taking a biopsy is time-consuming and thus rather expensive, as the patient has to be moved in and out of the MRI several times. Since 2015, a remote-controlled manipulator robot (RCM) has been available. Using this apparatus the radiologist is able to position the needle guide remotely. This technique is simple to learn and less time-consuming than the in-bore biopsy without the RCM. In this article we discuss the findings from the first 201 patients in the Netherlands from whom robot-guided prostate biopsies have been taken.

Background: Accurate pretreatment assessment of prostate cancer (PCa) aggressiveness is important in decision making. Gleason grade is a critical predictor of the aggressiveness of PCa. Transrectal ultrasound-guided biopsies (TRUSBxs) show substantial undergrading of Gleason grades found after radical prostatectomy (RP). Diffusion-weighted magnetic resonance imaging (MRI) has been shown to be a biomarker of tumour aggressiveness., Objective: To improve pretreatment assessment of PCa aggressiveness, this study prospectively evaluated MRI-guided prostate biopsies (MR-GBs) of abnormalities determined on diffusion-weighted imaging (DWI) apparent diffusion coefficient (ADC) maps. The results were compared with a 10-core TRUSBx cohort. RP findings served as the gold standard., Design, Setting, and Participants: A 10-core TRUSBx (n=64) or MR-GB (n=34) was used for PCa diagnosis before RP in 98 patients., Measurements: Using multiparametric 3-T MRI: T2-weighted, dynamic contrast-enhanced imaging, and DWI were performed to identify tumour-suspicious regions in patients with a negative TRUSBx. The regions with the highest restriction on ADC maps within the suspicions regions were used to direct MR-GB. A 10-core TRUSBx was used in a matched cohort. Following RP, the highest Gleason grades (HGGs) in biopsies and RP specimens were identified. Biopsy and RP Gleason grade results were evaluated using chi-square analysis., Results and Limitations: No significant differences on RP were observed for proportions of patients having a HGG of 3 (35% vs 28%; p=0.50), 4 (32% vs 41%; p=0.51), and 5 (32% vs 31%; p=0.61) for the MR-GB and TRUSBx cohort, respectively. MR-GB showed an exact performance with RP for overall HGG: 88% (30 of 34); for TRUS-GB it was 55% (35 of 64; p=0.001). In the MR-GB cohort, an exact performance with HGG 3 was 100% (12 of 12); for HGG 4, 91% (10 of 11); and for HGG 5, 73% (8 of 11). The corresponding performance rates for TRUSBx were 94% (17 of 18; p=0.41), 46% (12 of 26; p=0.02), and 30% (6 of 20; p=0.01), respectively., Conclusions: This study shows prospectively that DWI-directed MR-GBs significantly improve pretreatment risk stratification by obtaining biopsies that are representative of true Gleason grade., (Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Re-excision rates after breast conserving surgery(BCS) of invasive lobular carcinoma (ILC) are high.Preoperative breast MRI has the potential to reduce re-excision rates, but may lead to an increased rate of mastectomies. Hence, we assessed the influence of preoperative breast MRI on the re-excision rate and the rate of mastectomies. We performed a retrospective cohort study of a consecutive series of patients with ILC who presented in one of two dedicated tertiary cancer centers between 1993 and 2005. We assessed the initial type of surgery(BCS or mastectomy), the re-excision rate and the final type of surgery. Patients were stratified into two groups:those who received preoperative MRI (MR? group) and those who did not (MR- group). In the MR- group, 27%of the patients underwent a re-excision after initial BCS. In the MR? group, this rate was significantly lower at 9%.The odds ratio was 3.64 (95% CI: 1.30-10.20, P = 0.010).There was a trend towards a lower final mastectomy rate in the MR? group compared to the MR- group (48 vs. 59%,P = 0.098). In conclusion, preoperative MRI in patients with ILC can reduce re-excision rates without increasing the rate of mastectomies.

MRI with its excellent contrast resolution and direct multiplanar imaging has become a valuable medical tool in diagnostic imaging. Due to physiological motion artifacts, the role of MRI in the abdomen is still under discussion. The use of ultrafast sequences and the development of oral contrast agents, however, offers new promises for abdominal MRI. In the area of the retroperitoneum and pelvis, MRI produces excellent images as motion-artifacts are absent. Up to the end of 1989 clinical MRI in The Netherlands was performed in only four University hospitals; nevertheless these centers proved able to compete with international standards. The rapidly increasing number of MR units that recently became available in The Netherlands could result in a surpassing of CT in many pelvic and abdominal pathological conditions.