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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., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Purpose: The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data., Method: A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets., Results: The model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types., Conclusions: The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: G.L.B. Stock in Siemens. C.A.H. Seed funding from University Medicine Greifswald; payment for lectures from Bracco Imaging Deutschland. F.B. Research project Sondierungsprojekt zu KI in der Pflege (SOKIP), funded by German Federal Ministry of Research and Education; AI Lighthouse Project Green Consumption Assistant with ecosia and TU Berlin, funded by German Federal Ministry for the Environment; Research grant on Prozessentwicklung und -begleitung zum KI-Einsatz in der Pflege (ProKIP), funded by the German Federal Ministry of Research; Research grant on KI in der Pflege-Sturz / Delir / Medikation (KIP-SDM), funded by the German Federal Ministry of Research; Research grant on Reduction of the Impact of untreated Waste Water on the Environment in case of torrential Rain (RIWWER), funded by the German Federal Ministry for Economic Affairs and Climate Action; Research grant on Citizen-based Monitoring for Peace & Security in the Era of Synthetic Media and Deepfakes, funded by the German Foundation for Peace Research. T.P. Berlin Institute of Health (Clinician Scientist Grant, Platform Grant), Ministry of Education and Research (BMBF, 01KX2021, 01KX2121, 68GX21001A), German Research Foundation (DFG, SFB 1340/2), Horizon 2020 (952172); AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Dohme Corp, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, MedImmune, Merck Sharp, Millennium Pharmaceuticals, Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Roche, Siemens Healthineers, and TESARO; royalties from Elsevier; patent pending., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model., Methods: We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates., Results: The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model., Conclusion: The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates., (© 2022. The Author(s).)

Objective: To examine the performance of a primary magnetic resonance imaging (MRI)/ultrasonography (US) fusion-guided targeted biopsy (TB), and in combination with an added systematic biopsy (SB)., Patients and Methods: Analysis of 318 consecutive biopsy-naïve men with suspicious multiparametric MRI (mpMRI; Prostate Imaging Reporting and Data System [PI-RADS] score ≥3) undergoing transrectal TB and 10-core SB between January 2012 and December 2016. The indication for performing mpMRI was based on clinical parameters and decided by the treating urologist before admission. TB was performed with a sensor-based MRI/US fusion-guided platform. Clinically significant prostate cancer was defined as Gleason score ≥4 + 3 = 7 (International Society of Urological Pathology Grade [ISUP] grade 3) or maximum cancer core length of ≥6 mm., Results: A median (interquartile range) of 14 (13-14) biopsies per case were taken. The overall cancer detection rate (CDR) was 77% (245/318). The TB alone detected 67% of prostate cancers and the SB alone detected 70%. The PI-RADS dependent CDR for the combination of TB/SB were 38% (21/55), 78% (120/154) and 95% (104/109) for PI-RADS scores of 3/4/5, respectively. Clinically significant prostate cancer was diagnosed by the combination of TB and SB in 195 men (61%) and by TB alone in 163 cases (51%). The number of missed or underestimated prostate cancers with a Gleason score ≥8 for TB alone was 31 (10%, P < 0.001) and 21 (7%, P < 0.001) for SB alone in comparison with the results of the combination of TB and SB. The rate of insignificant prostate cancer was comparable for the combination of TB and SB and TB alone (50/318, 16% vs 50/318, 16%)., Conclusions: Pre-biopsy mpMRI is of incremental value in increasing the detection of clinically significant prostate cancer in biopsy-naïve patients with suspicion of prostate cancer. Combining TB with SB further improved the diagnostic accuracy without increasing the rate of insignificant prostate cancer., (© 2018 The Authors BJU International © 2018 BJU International Published by John Wiley & Sons Ltd.)

Introduction: Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) analyse tissue vascularization. We evaluated if CEUS can provide comparable information as DCE-MRI for the detection of prostate cancer (PCa) and prediction of its aggressiveness., Material and Methods: A post-hoc evaluation of 92 patients was performed. In each patient CEUS and DCE-MRI parameters of the most suspicious lesion identified on MRI were analysed. The predictive values for discrimination between benign lesions, low-/intermediate- and high-grade PCa were evaluated. Results of targeted biopsy served as reference standard (benign lesions, n=51; low- and intermediate-grade PCa [Gleason grade group 1 and 2], n=22; high-grade PCa [≥ Gleason grade group 3], n=19)., Results: In peripheral zone lesions of all tested CEUS parameters only time to peak (TTP CEUS ) showed significant differences between benign lesions and PCa (AUC 0.65). Of all tested DCE-MRI parameters, rate constant (K ep ) was the best discriminator of high-grade PCa in the whole prostate (AUC 0.83) and in peripheral zone lesions (AUC 0.89)., Conclusion: DCE-MRI showed a superior performance for detection of PCa and prediction of its aggressiveness. CEUS and DCE-MRI performed better in peripheral zone lesions than in transition zone lesions., Key Points: • DCE-MRI gathers information about vascularization and capillary permeability characteristics of tissues. • DCE-MRI can detect PCa and predict its aggressiveness. • CEUS also gathers information about vascularization of tissues. • For detection of PCa and prediction of aggressiveness DCE-MRI performed superiorly. • Both imaging techniques performed better in peripheral zone lesions.

Objectives: To evaluate the performance of T2 mapping in discriminating prostate cancer from normal prostate tissue in the peripheral zone using a practical reduced field-of-view MRI sequence requiring less than 3 minutes of scan time., Materials and Methods: Thirty-six patients with biopsy-proven peripheral zone prostate cancer without prior treatment underwent routine multiparametric MRI at 3.0T with an endorectal coil. An Inner-Volume Carr-Purcell-Meiboom-Gill imaging sequence that required 2.8 minutes to obtain data for quantitative T2 mapping covering the entire prostate gland was added to the routine multiparametric protocol. Suspected cancer (SC) and suspected healthy (SH) tissue in the peripheral zone were identified in consensus by three radiologists and were correlated with available biopsy results. Differences in mean T2 values in SC and SH regions-of-interest (ROIs) were tested for significance using unpaired Student's two-tailed t-test. The area under the receiver operating characteristic curve was used to assess the optimal threshold T2 value for cancer discrimination., Results: ROI analyses revealed significantly (p<0.0001) shorter T2 values in SC (85.4±12.3ms) compared to SH (169.6±38.7ms). An estimated T2 threshold of 99ms yielded a sensitivity of 92% and a specificity of 97% for prostate cancer discrimination., Conclusions: Quantitative values derived from this clinically practical T2-mapping sequence allow high precision discrimination between healthy and cancerous peripheral zone in the prostate., (Copyright © 2015 Elsevier Inc. All rights reserved.)