Search

Copyright of Proges en Urologie is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Proges en Urologie is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Proges en Urologie is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Proges en Urologie is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Proges en Urologie is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: The aim of this study was to perform a cross-cultural adaptation of the Objective Structured assessment of Technical Skill (OSATS) tool into Chinese and to determine its reproducibility and validity in China., Methods: A Chinese version of OSATS was created through a process of translation, back-translation, expert panel evaluation, pilot testing and then its validation. 59 candidates were included in the study in an international Chinese simulation center including medical students (5th year of medical studies), surgical fellow, attending surgeons, attending professors and professors of surgery. Two exercises performed on synthetic protheses (latex balloons) were designed and filmed: separate suture (3 points) and a circular anastomosis. Each candidate technical competencies were established according to the score by 3 experts. The results of the different scores were correlated against the theoretical background (TB) of the candidates (1 to 5), for each exercise., Results: A Chinese version of OSATS was produced and validated through a back-translation process. The average professors' residents' and students' scores were 35 (± 0.2), 28 (± 0.4) and 18 (± 0.3) respectively. For the objective score cnOSATS on simple sutures, the mean increases from one TB level to the next was 1.63 (0.90-2.37), p < 0.001. For the objective score cnOSATS on running sutures, the mean increases from one TB level to the next was 1.99 (1.22 - 2.77), p < 0.001., Conclusion: This study suggests that the Chinese version of OSATS can reliably and validly assess surgical skills in China., (Copyright © 2024 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Background: In 2021, the EAU Guidelines implemented a novel, expert opinion-based follow-up scheme, with a three-risk-category system for clear cell (cc) and non-cc renal cell carcinoma (non-ccRCC) after surgery with curative intent. We aimed to validate the novel follow-up scheme and provide data-driven recurrence estimates according to risk groups, to confirm or implement the oncologic surveillance strategy., Methods: We identified 5,320 patients from a prospectively maintained database involving 28 French referral centers. The risk of recurrence, as either loco-regional or distant, was evaluated with the Kaplan-Meier method for each group (low- intermediate- or high-risk) according to ccRCC or non-ccRCC histology. The noncumulative distribution of recurrences was graphically investigated through the LOWESS smoother., Results: Two thousand two hundred ninety-three (58%), 926 (23%), and 738 (19%) had low-, intermediate, and high-risk ccRCC, and 683 (50%), 297 (22%), and 383 (28%) had low-, intermediate, and high-risk non-ccRCC, respectively. Median follow-up for survivors was 46 months. Overall, 661 patients experienced recurrence. Over time, the noncumulative risk of recurrence was approximately 10% for low-risk cc-RCC, non-ccRCC, and intermediate-risk non-ccRCC, with non-significant difference among the three recurrence functions (P=0.9). At 5-year, time point after which imaging should be de-intensified to biennial, the noncumulative risks of recurrence were: for intermediate risk ccRCC and non-ccRCC: 15% and 11%, respectively; for high-risk ccRCC and non-ccRCC: 24% and 8%, respectively. Among high-risk non-ccRCC patients there were 9 recurrences at 3-month. There was no significant difference between the recurrence function of high-risk non-ccRCC patients with negative imaging at 3-month and the one of intermediate-risk ccRCC (P=0.3)., Conclusions: Given the relatively low recurrence risk of patients with intermediate-risk non-ccRCC, those individuals could be followed up with a similar strategy to the low-risk category. Similarly, patients with high-risk non-ccRCC with negative imaging at 3-month, could be followed up similarly to intermediate-risk ccRCC after the 3-month time point.

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.)