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Objective: The aim of this study was to assess the precision and safety of targeted microwave ablation (TMA) using organ-based tracking (OBT) fusion, in patients with intermediate risk prostate cancer., Patients and Method: We conducted a prospective, multicentric trial. Eligible patients had a prostate-specific antigen (PSA) < 20 ng/mL, a magnetic resonance imaging (MRI)-visible index tumour of Gleason score 3 + 4, with largest axis ≤15 mm and distant of at least 5 mm from the rectum and apex. TMA was performed with microwave needle applicator using OBT fusion, with a transperineal or a transrectal approach. In this interim analysis, we evaluated precision, safety, urinary and sexual outcomes, and PSA density kinetics., Results: At this point, 37 patients were treated in five centres. Median (interquartile range) age is 68 (63-72) years. Baseline median prostate volume and PSA are of 45 (34-57) mL and 8 (6.2-10.8) ng/mL, respectively. Median largest tumour axis on T2W MRI is of 11 mm (10-13). Patients were treated under general anaesthesia or conscious IV sedation in an outpatient setting. Anaesthesia had a median duration of 78 (66-90) min. A median number of 3 (2-4) 12-W ablations of 2 to 5 min were performed per patient. After a median follow-up of 6 (2.4-10) months, we observed 58 adverse events (AE) in 22 patients. These were of Common Terminology Criteria for Adverse Events (CTCAE) grade 1, 2 and 3 in 43 (74%), 13 (22%) and 2 (4%) cases. Six (15%) patients had an acute urinary retention, five of which considered as severe AE because of rehospitalisation. We did not observe any significant difference in International Prostate Symptom Score (IPSS), Male Sexual Health Questionnaire-ejaculatory dysfunction (MSHQ-EjD) and International Index of Erectile Function (IIEF5) from baseline to last follow-up. Median PSA density evolved from 0.2 (0.1-0.3) at baseline to 0.1 (0.07-0.16) at 12 months., Conclusions: These preliminary results suggest that TMA using OBT fusion is precise and safe in patients with intermediate risk localised prostate cancer. Further inclusions and follow-up are needed to assess oncological outcome., Competing Interests: Nicolas Barry Delongchamps and Julien Anract: proctoring activity for Koelis. The other authors have no potential conflicts of interest to declare., (© 2024 The Author(s). BJUI Compass published by John Wiley & Sons Ltd on behalf of BJU International Company.)

Background and Objective: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI., Methods: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively., Key Findings and Limitations: After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment., Conclusions and Clinical Implications: The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy., Patient Summary: An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Despite the significant advances in the management of advanced prostate cancer (PCa), metastatic PCa is currently considered incurable. For further investigations in precision treatment, the development of preclinical models representing the complex prostate tumor heterogeneity are mandatory. Accordingly, we aimed to establish a resource of patient-derived xenograft (PDX) models that exemplify each phase of this multistage disease for accurate and rapid evaluation of candidate therapies., Methods: Fresh tumor samples along with normal corresponding tissues were obtained directly from patients at surgery. To ensure that the established models reproduce the main features of patient's tumor, both PDX tumors at multiple passages and patient's primary tumors, were processed for histological characteristics. STR profile analyses were also performed to confirm patient identity. Finally, the responses of the PDX models to androgen deprivation, PARP inhibitors and chemotherapy were also evaluated., Results: In this study, we described the development and characterization of 5 new PDX models of PCa. Within this collection, hormone-naïve, androgen-sensitive and castration-resistant (CRPC) primary tumors as well as prostate carcinoma with neuroendocrine differentiation (CRPC-NE) were represented. Interestingly, the comprehensive genomic characterization of the models identified recurrent cancer driver alterations in androgen signaling, DNA repair and PI3K, among others. Results were supported by expression patterns highlighting new potential targets among gene drivers and the metabolic pathway. In addition, in vivo results showed heterogeneity of response to androgen deprivation and chemotherapy, like the responses of patients to these treatments. Importantly, the neuroendocrine model has been shown to be responsive to PARP inhibitor., Conclusion: We have developed a biobank of 5 PDX models from hormone-naïve, androgen-sensitive to CRPC primary tumors and CRPC-NE. Increased copy-number alterations and accumulation of mutations within cancer driver genes as well as the metabolism shift are consistent with the increased resistance mechanisms to treatment. The pharmacological characterization suggested that the CRPC-NE could benefit from the PARP inhibitor treatment. Given the difficulties in developing such models, this relevant panel of PDX models of PCa will provide the scientific community with an additional resource for the further development of PDAC research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Béraud, Bidan, Lassalle, Lang, Lindner, Krucker, Masliah-Planchon, Potiron, Lluel, Massfelder, Allory and Misseri.)