Three-dimensional Augmented Reality Robot-assisted Partial Nephrectomy in Case of Complex Tumours (PADUA ≥10): A New Intraoperative Tool Overcoming the Ultrasound Guidance.


Journal

European urology
ISSN: 1873-7560
Titre abrégé: Eur Urol
Pays: Switzerland
ID NLM: 7512719

Informations de publication

Date de publication:
08 2020
Historique:
received: 18 08 2019
accepted: 29 11 2019
pubmed: 4 1 2020
medline: 25 6 2021
entrez: 4 1 2020
Statut: ppublish

Résumé

Despite technical improvements introduced with robotic surgery, management of complex tumours (PADUA score ≥10) is still a matter of debate within the field of transperitoneal robot-assisted partial nephrectomy (RAPN). To evaluate the accuracy of our three-dimensional (3D) static and elastic augmented reality (AR) systems based on hyperaccuracy models (HA3D) in identifying tumours and intrarenal structures during transperitoneal RAPN (AR-RAPN), compared with standard ultrasound (US). A retrospective study was conducted, including 91 patients who underwent RAPN for complex renal tumours, 48 with 3D AR guidance and 43 with 2D US guidance, from July 2017 to May 2019. In patients who underwent 3D AR-RAPN, virtual image overlapping guided the surgeon during resection and suture phases. In the 2D US group, interventions were driven by US only. Patient characteristics were tested using the Fisher's exact test for categorical variables and the Mann-Whitney test for continuous ones. Intraoperative, postoperative, and surgical outcomes were collected. All results for continuous variables were expressed as medians (range), and frequencies and proportions were reported as percentages. The use of 3D AR guidance makes it possible to correctly identify the lesion and intraparenchymal structures with a more accurate 3D perception of the location and the nature of the different structures relative to the standard 2D US guidance. This translates to a lower rate of global ischaemia (45.8% in the 3D group vs 69.7% in the US group; p = 0.03), higher rate of enucleation (62.5% vs 37.5% in the 3D and US groups, respectively; p = 0.02), and lower rate of collecting system violation (10.4% vs 45.5%; p = 0.003). Postoperatively, 3D AR guidance use correlates to a low risk of surgery-related complications in 3D AR groups and a lower drop in estimated renal plasma flow at renal scan at 3 mo of follow-up (-12.38 in the 3D group vs -18.14 in the US group; p = 0.01). The main limitations of this study are short follow-up time and small sample size. HA3D models that overlap in vivo anatomy during AR-RAPN for complex tumours can be useful for identifying the lesion and intraparenchymal structures that are difficult to visualise with US only. This translates to a potential improvement in the quality of the resection phase and a reduction in postoperative complications, with better functional recovery. Based on our findings, three-dimensional augmented reality robot-assisted partial nephrectomy seems to help surgeons in the management of complex renal tumours, with potential early postoperative benefits.

Sections du résumé

BACKGROUND
Despite technical improvements introduced with robotic surgery, management of complex tumours (PADUA score ≥10) is still a matter of debate within the field of transperitoneal robot-assisted partial nephrectomy (RAPN).
OBJECTIVE
To evaluate the accuracy of our three-dimensional (3D) static and elastic augmented reality (AR) systems based on hyperaccuracy models (HA3D) in identifying tumours and intrarenal structures during transperitoneal RAPN (AR-RAPN), compared with standard ultrasound (US).
DESIGN, SETTING, AND PARTICIPANTS
A retrospective study was conducted, including 91 patients who underwent RAPN for complex renal tumours, 48 with 3D AR guidance and 43 with 2D US guidance, from July 2017 to May 2019.
SURGICAL PROCEDURE
In patients who underwent 3D AR-RAPN, virtual image overlapping guided the surgeon during resection and suture phases. In the 2D US group, interventions were driven by US only.
MEASUREMENTS
Patient characteristics were tested using the Fisher's exact test for categorical variables and the Mann-Whitney test for continuous ones. Intraoperative, postoperative, and surgical outcomes were collected. All results for continuous variables were expressed as medians (range), and frequencies and proportions were reported as percentages.
RESULTS AND LIMITATIONS
The use of 3D AR guidance makes it possible to correctly identify the lesion and intraparenchymal structures with a more accurate 3D perception of the location and the nature of the different structures relative to the standard 2D US guidance. This translates to a lower rate of global ischaemia (45.8% in the 3D group vs 69.7% in the US group; p = 0.03), higher rate of enucleation (62.5% vs 37.5% in the 3D and US groups, respectively; p = 0.02), and lower rate of collecting system violation (10.4% vs 45.5%; p = 0.003). Postoperatively, 3D AR guidance use correlates to a low risk of surgery-related complications in 3D AR groups and a lower drop in estimated renal plasma flow at renal scan at 3 mo of follow-up (-12.38 in the 3D group vs -18.14 in the US group; p = 0.01). The main limitations of this study are short follow-up time and small sample size.
CONCLUSIONS
HA3D models that overlap in vivo anatomy during AR-RAPN for complex tumours can be useful for identifying the lesion and intraparenchymal structures that are difficult to visualise with US only. This translates to a potential improvement in the quality of the resection phase and a reduction in postoperative complications, with better functional recovery.
PATIENT SUMMARY
Based on our findings, three-dimensional augmented reality robot-assisted partial nephrectomy seems to help surgeons in the management of complex renal tumours, with potential early postoperative benefits.

Identifiants

pubmed: 31898992
pii: S0302-2838(19)30897-8
doi: 10.1016/j.eururo.2019.11.024
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

229-238

Commentaires et corrections

Type : CommentIn
Type : CommentIn
Type : CommentIn

Informations de copyright

Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Francesco Porpiglia (F)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy. Electronic address: porpiglia@libero.it.

Enrico Checcucci (E)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Daniele Amparore (D)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Federico Piramide (F)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Gabriele Volpi (G)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Stefano Granato (S)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Paolo Verri (P)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Matteo Manfredi (M)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Andrea Bellin (A)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Pietro Piazzolla (P)

Department of Management and Production Engineer, Polytechnic University of Turin, Italy.

Riccardo Autorino (R)

Division of Urology, VCU Health, Richmond, VA, USA.

Ivano Morra (I)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Cristian Fiori (C)

Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano, Turin, Italy.

Alex Mottrie (A)

Onze-Lieve-Vrouw Hospital, Aalst, Belgium; OLV Robotic Surgery Institute Academy, Melle, Belgium.

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