Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the role of 3D models on positive surgical margin rate (PSM) rate in patients who underwent robot-assisted radical prostatectomy (RARP) compared to a no-3D control group. Secondarily, we evaluated the postoperative functional and oncological outcomes.

Methods

Prospective study enrolling patients with localized prostate cancer (PCa) undergoing RARP with mp-MRI-based 3D model reconstruction, displayed in a cognitive or augmented-reality fashion, at our Centre from 01/2016 to 01/2020. A control no-3D group was extracted from the last two years of our Institutional RARP database. PSMr between the two groups was evaluated and multivariable linear regression (MLR) models were applied. Finally, Kaplan–Meier estimator was used to calculate biochemical recurrence at 12 months after the intervention.

Results

160 patients were enrolled in the 3D Group, while 640 were selected for the Control Group. A more conservative NS approach was registered in the 3D Group (full NS 20.6% vs 12.7%; intermediate NS 38.1% vs 38.0%; standard NS 41.2% vs 49.2%; p = 0.02). 3D Group patients had lower PSM rates (25 vs. 35.1%, p = 0.01). At MLR models, the availability of 3D technology (p = 0.005) and the absence of extracapsular extension (ECE, p = 0.004) at mp-MRI were independent predictors of lower PSMr. Moreover, 3D model represented a significant protective factor for PSM in patients with ECE or pT3 disease.

Conclusion

The availability of 3D models during the intervention allows to modulate the NS approach, limiting the occurrence of PSM, especially in patients with ECE at mp-MRI or pT3 PCa.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Checcucci E, Amparore D, De Luca S, Autorino R, Fiori C, Porpiglia F (2019) Precision prostate cancer surgery: an overview of new technologies and techniques. Minerva Urol Nefrol 71(5):487–501. https://doi.org/10.23736/S0393-2249.19.03365-4

    Article  PubMed  Google Scholar 

  2. Lee S, Kim KB, Jo JK, Ho JN, Oh JJ, Jeong SJ, Hong SK, Byun SS, Choe G, Lee SE (2016) Prognostic value of focal positive surgical margins after radical prostatectomy. Clin Genitourin Cancer 14(4):e313–e319. https://doi.org/10.1016/j.clgc.2015.12.013

    Article  PubMed  Google Scholar 

  3. Dinneen E, Haider A, Grierson J, Freeman A, Oxley J, Briggs T, Nathan S, Williams NR, Brew-Graves C, Persad R, Aning J, Jamieson C, Ratynska M, Ben-Salha I, Ball R, Clow R, Allen C, Heffernan-Ho D, Kelly J, Shaw G (2020) NeuroSAFE frozen section during robot-assisted radical prostatectomy (RARP): Peri-operative and histopathological outcomes from the NeuroSAFE PROOF Feasibility Randomised Controlled Trial. BJU Int. https://doi.org/10.1111/bju.15256

    Article  PubMed  Google Scholar 

  4. Rocco B, Sighinolfi MC, Cimadamore A, ReggianiBonetti L, Bertoni L, Puliatti S, Eissa A, Spandri V, Azzoni P, Dinneen E, Shaw G, Nathan S, Micali S, Bianchi G, Maiorana A, Pellacani G, Montironi R (2020) Digital frozen section of the prostate surface during radical prostatectomy: a novel approach to evaluate surgical margins. BJU Int 126(3):336–338. https://doi.org/10.1111/bju.15108

    Article  PubMed  Google Scholar 

  5. Russo F, Manfredi M, Panebianco V, Armando E, De Luca S, Mazzetti S, Giannini V, Mele F, Bollito E, Appendino E, Regge D, Porpiglia F (2019) Radiological wheeler staging system: a retrospective cohort analysis to improve the local staging of prostate cancer with multiparametric MRI. Minerva Urol Nefrol 71(3):264–272. https://doi.org/10.23736/S0393-2249.19.03248-X

    Article  PubMed  Google Scholar 

  6. Porpiglia F, Bertolo R, Checcucci E, Amparore D, Autorino R, Dasgupta P, Wiklund P, Tewari A, Liatsikos E, Fiori C, ESUT Research Group (2018) Development and validation of 3D printed virtual models for robot-assisted radical prostatectomy and partial nephrectomy: urologists’ and patients’ perception. World J Urol. https://doi.org/10.1007/s00345-017-2126-1

    Article  PubMed  Google Scholar 

  7. Porpiglia F, Checcucci E, Amparore D, Manfredi M, Massa F, Piazzolla P, Manfrin D, Piana A, Tota D, Bollito E, Fiori C (2019) Three-dimensional elastic augmented-reality robot-assisted radical prostatectomy using hyperaccuracy three-dimensional reconstruction technology: a step further in the identification of capsular involvement. Eur Urol 76(4):505–514. https://doi.org/10.1016/j.eururo.2019.03.037

    Article  PubMed  Google Scholar 

  8. Porpiglia F, Checcucci E, Amparore D, Autorino R, Piana A, Bellin A, Piazzolla P, Massa F, Bollito E, Gned D, De Pascale A, Fiori C (2019) Augmented-reality robot-assisted radical prostatectomy using hyper-accuracy three-dimensional reconstruction (HA3D™) technology: a radiological and pathological study. BJU Int 123(5):834–845. https://doi.org/10.1111/bju.14549 (Epub 2018 Oct 19 PMID: 30246936)

    Article  PubMed  Google Scholar 

  9. Amparore D, Pecoraro A, Checcucci E et al (2021) 3D imaging technologies in minimally-invasive kidney and prostate cancer surgery: which is the urologists’ perception? Minerva Urol Nefrol. https://doi.org/10.23736/S2724-6051.21.04131-X

    Article  Google Scholar 

  10. Russo F, Regge D, Armando E, Giannini V, Vignati A, Mazzetti S, Manfredi M, Bollito E, Correale L, Porpiglia F (2016) Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard. BJU Int 118(1):84–94. https://doi.org/10.1111/bju.13234

    Article  PubMed  Google Scholar 

  11. Porpiglia F, Bertolo R, Manfredi M, De Luca S, Checcucci E, Morra I, Passera R, Fiori C (2016) Total anatomical reconstruction during robot-assisted radical prostatectomy: implications on early recovery of urinary continence. Eur Urol 69(3):485–495. https://doi.org/10.1016/j.eururo.2015.08.005

    Article  PubMed  Google Scholar 

  12. Austin PC (2011) An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res 46(3):399–424. https://doi.org/10.1080/00273171.2011.568786

    Article  Google Scholar 

  13. Montorsi F, Wilson TG, Rosen RC, Ahlering TE, Artibani W, Carroll PR, Costello A, Eastham JA, Ficarra V, Guazzoni G, Menon M, Novara G, Patel VR, Stolzenburg JU, Van der Poel H, Van Poppel H, Mottrie A, Pasadena Consensus Panel (2012) Best practices in robot-assisted radical prostatectomy: recommendations of the Pasadena Consensus Panel. Eur Urol. https://doi.org/10.1016/j.eururo.2012.05.057

    Article  PubMed  PubMed Central  Google Scholar 

  14. Dindo D, Demartines N, Clavien PA (2004) Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg 240(2):205–213. https://doi.org/10.1097/01.sla.0000133083.54934.ae.PMID:15273542;PMCID:PMC1360123

    Article  PubMed  PubMed Central  Google Scholar 

  15. Martini A, Gandaglia G, Fossati N, Scuderi S, Bravi CA, Mazzone E, Stabile A, Scarcella S, Robesti D, Barletta F, Cucchiara V, Mirone V, Montorsi F, Briganti A (2019) Defining clinically meaningful positive surgical margins in patients undergoing radical prostatectomy for localised prostate cancer. Eur Urol Oncol. https://doi.org/10.1016/j.euo.2019.03.006

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rosen RC, Riley A, Wagner G, Osterloh IH, Kirkpatrick J, Mishra A (1997) The international index of erectile function (IIEF): a multidimensional scale for assessment of erectile dysfunction. Urology 49(6):822–830. https://doi.org/10.1016/s0090-4295(97)00238-0

    Article  CAS  PubMed  Google Scholar 

  17. Wang S, Frisbie J, Keepers Z, Bolten Z, Hevaganinge A, Boctor E, Leonard S, Tokuda J, Krieger A, Siddiqui MM (2020) The Use of three-dimensional visualization techniques for prostate procedures: a systematic review. Eur Urol Focus. https://doi.org/10.1016/j.euf.2020.08.002

    Article  PubMed  Google Scholar 

  18. Kratiras Z, Gavazzi A, Belba A, Willis B, Chew S, Allen C, Amoroso P, Dasgupta P (2019) Phase I study of a new tablet-based image guided surgical system in robot-assisted radical prostatectomy. Minerva Urol Nefrol 71(1):92–95. https://doi.org/10.23736/S0393-2249.18.03250-2

    Article  PubMed  Google Scholar 

  19. Ukimura O, Gill IS (2008) Imaging-assisted endoscopic surgery: Cleveland Clinic experience. J Endourol 22(4):803–810. https://doi.org/10.1089/end.2007.9823

    Article  PubMed  Google Scholar 

  20. Teber D, Simpfendörfer T, Guven S, Baumhauer M, Gözen AS, Rassweiler J (2010) In-vitro evaluation of a soft-tissue navigation system for laparoscopic prostatectomy. J Endourol 24(9):1487–1491. https://doi.org/10.1089/end.2009.0289

    Article  PubMed  Google Scholar 

  21. Simpfendörfer T, Baumhauer M, Müller M, Gutt CN, Meinzer HP, Rassweiler JJ, Guven S, Teber D (2011) Augmented reality visualization during laparoscopic radical prostatectomy. J Endourol 25(12):1841–1845. https://doi.org/10.1089/end.2010.0724

    Article  PubMed  Google Scholar 

  22. Lanchon C, Custillon G, Moreau-Gaudry A, Descotes JL, Long JA, Fiard G, Voros S (2016) Augmented reality using transurethral ultrasound for laparoscopic radical prostatectomy: preclinical evaluation. J Urol 196(1):244–250. https://doi.org/10.1016/j.juro.2016.01.094

    Article  PubMed  Google Scholar 

  23. Amparore D, Checcucci E, Fiori C, Porpiglia F, Francesco P, Enrico C, Daniele A et al (2020) Three-dimensional Augmented Reality Robot-assisted Partial Nephrectomy in Case of Complex Tumours (PADUA ≥ 10): A New Intraoperative Tool Overcoming the Ultrasound Guidance. Eur Urol. In press. Eur Urol 77(6):e163–e164. https://doi.org/10.1016/j.eururo.2020.03.038

    Article  PubMed  Google Scholar 

  24. Porpiglia F, Checcucci E, Amparore D, Piazzolla P, Manfredi M, Pecoraro A, De Cillis S, Piana A, Volpi G, Piramide F, Alessio P, Granato S, Vezzetti E, Fiori C (2021) P1100-Artificial intelligence guided 3D automatic augmented-reality images allow to identify the extracapsular extension on neurovascular bundles during robotic prostatectomy. Eur Urol 79(Supplement):1. https://doi.org/10.1016/S0302-2838(21)01471-8

    Article  Google Scholar 

  25. Bianchi L, Chessa F, Angiolini A, Cercenelli L, Lodi S, Bortolani B, Molinaroli E, Casablanca C, Droghetti M, Gaudiano C, Mottaran A, Porreca A, Golfieri R, Romagnoli D, Giunchi F, Fiorentino M, Piazza P, Puliatti S, Diciotti S, Marcelli E, Mottrie A, Schiavina R (2021) The use of augmented reality to guide the intraoperative frozen section during robot-assisted radical prostatectomy. Eur Urol. https://doi.org/10.1016/j.eururo.2021.06.020

    Article  PubMed  Google Scholar 

  26. Schiavina R, Bianchi L, Lodi S, Cercenelli L, Chessa F, Bortolani B, Gaudiano C, Casablanca C, Droghetti M, Porreca A, Romagnoli D, Golfieri R, Giunchi F, Fiorentino M, Marcelli E, Diciotti S, Brunocilla E (2020) Real-time augmented reality three-dimensional guided robotic radical prostatectomy: preliminary experience and evaluation of the impact on surgical planning. Eur Urol Focus. https://doi.org/10.1016/j.euf.2020.08.004

    Article  PubMed  Google Scholar 

  27. Morozov A, Barret E, Veneziano D, Grigoryan V, Salomon G, Fokin I, Taratkin M, Poddubskaya E, Gomez Rivas J, Puliatti S, Okhunov Z, Cacciamani GE, Checcucci E, Marenco Jiménez JL, Enikeev D, In collaboration with ESUT-YAUWP Group (2021) A systematic review of nerve-sparing surgery for high-risk prostate cancer. Minerva Urol Nefrol. https://doi.org/10.23736/S0393-2249.20.04178-8

    Article  Google Scholar 

  28. Checcucci E, Autorino R, Cacciamani GE, Amparore D, De Cillis S, Piana A, Piazzolla P, Vezzetti E, Fiori C, Veneziano D, Tewari A, Dasgupta P, Hung A, Gill I, Porpiglia F, Uro-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology (2020) Artificial intelligence and neural networks in urology: current clinical applications. Minerva Urol Nefrol. https://doi.org/10.23736/S0393-2249.19.03613-0

    Article  PubMed  Google Scholar 

  29. Checcucci E, De Cillis S, Granato S, Chang P, Afyouni AS, Okhunov Z, o-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology (2020) Applications of neural networks in urology: a systematic review. Curr Opin Urol. https://doi.org/10.1097/MOU.0000000000000814 (PMID: 32881726)

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Cristian Fiori and Francesco Porpiglia contributed equally to senior authorship.

Authors and Affiliations

  1. Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142, km 3, 95 10060, Candiolo, Turin, Italy

    Enrico Checcucci

  2. Uro-Technology and SoMe Working Group of the Young Academic Urologists (YAU) Working Party of the European Association of Urology (EAU), Arnhem, The Netherlands

    Enrico Checcucci

  3. Division of Urology, Department of Oncology, University of Turin San Luigi Gonzaga Hospital, Orbassano, Turin, Italy

    Enrico Checcucci, Angela Pecoraro, Daniele Amparore, Sabrina De Cillis, Stefano Granato, Gabriele Volpi, Michele Sica, Paolo Verri, Alberto Piana, Matteo Manfredi, Cristian Fiori & Francesco Porpiglia

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

    Pietro Piazzolla & Enrico Vezzetti

  5. Division of Urology, Department of Surgery, SS Annunziata Hospital, Cosenza, Italy

    Michele Di Dio

Authors
  1. Enrico Checcucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angela Pecoraro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniele Amparore
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sabrina De Cillis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefano Granato
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gabriele Volpi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michele Sica
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Paolo Verri
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alberto Piana
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pietro Piazzolla
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Matteo Manfredi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Enrico Vezzetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Michele Di Dio
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Cristian Fiori
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Francesco Porpiglia
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

The Uro-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology

Contributions

Protocol/project development: EC, DA, AP, FP. Data collection or management: SDC, SG, MS, AP, PV. Data analysis: AP. Manuscript writing/editing: EC, GV, AP. Supervision: CF, MDD, MM, PP, EV, FP.

Corresponding author

Correspondence to Enrico Checcucci.

Ethics declarations

Conflict of interest

All the authors have nothing to disclose.

Research involving human participants and/or animals

None.

Informed consent

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 98 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Checcucci, E., Pecoraro, A., Amparore, D. et al. The impact of 3D models on positive surgical margins after robot-assisted radical prostatectomy. World J Urol 40, 2221–2229 (2022). https://doi.org/10.1007/s00345-022-04038-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-022-04038-8

Keywords

Search

Navigation