Literature DB >> 30979636

Three-dimensional Elastic Augmented-reality Robot-assisted Radical Prostatectomy Using Hyperaccuracy Three-dimensional Reconstruction Technology: A Step Further in the Identification of Capsular Involvement.

Francesco Porpiglia1, Enrico Checcucci2, Daniele Amparore2, Matteo Manfredi2, Federica Massa3, Pietro Piazzolla2, Diego Manfrin2, Alberto Piana2, Daniele Tota3, Enrico Bollito3, Cristian Fiori2.   

Abstract

BACKGROUND: In prostate cancer (PCa) surgical procedures, in order to maximize potency recovery, a nerve-sparing (NS) procedure is preferred. However, cancer abutting or focally extending beyond the prostate capsule increases the risk of a positive surgical margin.
OBJECTIVE: To evaluate the accuracy of our new three-dimensional (3D) elastic augmented-reality (AR) system in identifying capsular involvement (CI) location of PCa during the NS phase of robot-assisted radical prostatectomy (RARP). Secondarily, the accuracy of this technology was compared with two-dimensional (2D)-based cognitive procedures. DESIGN, SETTING, AND PARTICIPANTS: A prospective study, enrolling 40 patients with PCa undergoing RARP at our center, from May to October 2018. SURGICAL PROCEDURE: Patients underwent 3D AR RARP or, in case of unavailability of this technology, 2D cognitive RARP. In all patients, total anatomical reconstruction was used. MEASUREMENTS: Clinical data were collected. In order to compare the two groups, nonparametric Mann-Whitney and chi-square tests were performed. A metallic clip was placed at the level of suspicious CI on the basis of images given by the 3D AR or magnetic resonance imaging (MRI) report. The pathological analysis evaluated the presence of tumor at the level of the clip. RESULTS AND LIMITATIONS: Twenty patients were enrolled in each group. Focusing on the 3D AR group at macroscopic evaluation, the metallic clip was placed at the tumor and capsular bulging in all cases. At microscopic assessment, cancer presence was confirmed in the suspicious area in 95.4% of the cases. Moreover, CI was correctly identified in 100.0% of the cases, thanks to the 3D image overlap. These results were compared with the 2D MRI cognitive group, showing, at microscopic analysis, statistically significant superiority of the 3D AR group in CI detection during the NS phase (100% vs 47.0%; p<0.05). The main limitation of this technique is that the segmentation and overlapping of the images are performed manually.
CONCLUSIONS: Our findings suggest that, with the introduction of the elastic 3D virtual models, prostate deformation is correctly simulated during surgery and lesion location is correctly identified, even in dynamic reality with a subsequent potential reduction of positive surgical margin rate and, in the meantime, maximization of functional outcomes. PATIENT
SUMMARY: On the basis of our findings, the three-dimensional elastic augmented-reality technology seems to help the surgeon in lesion location identification even in a dynamic phase of the intervention, optimizing the oncological outcomes.
Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Augmented reality; Hyperaccuracy three-dimensional reconstruction; Image-guided surgery; Prostatectomy; Robotics; Three-dimensional reconstruction

Mesh:

Year:  2019        PMID: 30979636     DOI: 10.1016/j.eururo.2019.03.037

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  13 in total

1.  Virtual reality of three-dimensional surgical field for surgical planning and intraoperative management.

Authors:  Atsuko Fujihara; Osamu Ukimura
Journal:  World J Urol       Date:  2021-11-17       Impact factor: 4.226

2.  Development and clinical applicability of MRI-based 3D prostate models in the planning of nerve-sparing robot-assisted radical prostatectomy.

Authors:  Hans Veerman; Thierry N Boellaard; Jari A van der Eijk; Judith H Sluijter; Ton A Roeleveld; Tim M van der Sluis; Jakko A Nieuwenhuijzen; Esther Wit; Maarten J A van Alphen; Robert L P van Veen; André N Vis; Henk G van der Poel; Pim J van Leeuwen
Journal:  J Robot Surg       Date:  2022-07-12

3.  The impact of 3D models on positive surgical margins after robot-assisted radical prostatectomy.

Authors:  Cristian Fiori; Francesco Porpiglia; Enrico Checcucci; Angela Pecoraro; Daniele Amparore; Sabrina De Cillis; Stefano Granato; Gabriele Volpi; Michele Sica; Paolo Verri; Alberto Piana; Pietro Piazzolla; Matteo Manfredi; Enrico Vezzetti; Michele Di Dio
Journal:  World J Urol       Date:  2022-07-05       Impact factor: 3.661

Review 4.  The evolution of image guidance in robotic-assisted laparoscopic prostatectomy (RALP): a glimpse into the future.

Authors:  Joshua Makary; Danielle C van Diepen; Ranjan Arianayagam; George McClintock; Jeremy Fallot; Scott Leslie; Ruban Thanigasalam
Journal:  J Robot Surg       Date:  2021-09-04

Review 5.  Autonomous surgery in the era of robotic urology: friend or foe of the future surgeon?

Authors:  Martin J Connor; Prokar Dasgupta; Hashim U Ahmed; Asif Raza
Journal:  Nat Rev Urol       Date:  2020-09-23       Impact factor: 14.432

6.  A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery.

Authors:  Erica Padovan; Giorgia Marullo; Leonardo Tanzi; Pietro Piazzolla; Sandro Moos; Francesco Porpiglia; Enrico Vezzetti
Journal:  Int J Med Robot       Date:  2022-03-13       Impact factor: 2.483

Review 7.  Machine learning in the optimization of robotics in the operative field.

Authors:  Runzhuo Ma; Erik B Vanstrum; Ryan Lee; Jian Chen; Andrew J Hung
Journal:  Curr Opin Urol       Date:  2020-11       Impact factor: 2.808

Review 8.  Techniques of robotic radical prostatectomy for the management of prostate cancer: which one, when and why.

Authors:  Shuo Liu; Ashok Hemal
Journal:  Transl Androl Urol       Date:  2020-04

9.  Real-time navigation by three-dimensional virtual reconstruction models in robot-assisted laparoscopic pyeloplasty for ureteropelvic junction obstruction: our initial experience.

Authors:  Sida Cheng; Xinfei Li; Weijie Zhu; Wanqiang Li; Jie Wang; Jian Yang; Jingyun Wu; He Wang; Lei Zhang; Xuesong Li; Liqun Zhou
Journal:  Transl Androl Urol       Date:  2021-01

10.  Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists.

Authors:  Michael Y Chen; Maria A Woodruff; Prokar Dasgupta; Nicholas J Rukin
Journal:  Cancer Med       Date:  2020-08-18       Impact factor: 4.452

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