Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

Open Access 01-04-2019 | Nephrectomy | Original Article

Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume-rendered images

Authors: E. R. Hyde, L. U. Berger, N. Ramachandran, A. Hughes-Hallett, N. P. Pavithran, M. G. B. Tran, S. Ourselin, A. Bex, F. H. Mumtaz

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2019

Abstract

Purpose

To determine whether the interactive visualisation of patient-specific virtual 3D models of the renal anatomy influences the pre-operative decision-making process of urological surgeons for complex renal cancer operations.

Methods

Five historic renal cancer patient pre-operative computed tomography (CT) datasets were retrospectively selected based on RENAL nephrectomy score and variety of anatomy. Interactive virtual 3D models were generated for each dataset using image segmentation software and were made available for online visualisation and manipulation. Consultant urologists were invited to participate in the survey which consisted of CT and volume-rendered images (VRI) for the control arm, and CT with segmentation overlay and the virtual 3D model for the intervention arm. A questionnaire regarding anatomical structures, surgical approach, and confidence was administered.

Results

Twenty-five participants were recruited (54% response rate), with 19/25 having > 5 years of renal surgery experience. The median anatomical clarity score increased from 3 for the control to 5 for the intervention arm. A change in planned surgical approach was reported in 19% of cases. Virtual 3D models increased surgeon confidence in the surgical decisions in 4/5 patient datasets. There was a statistically significant improvement in surgeon opinion of the potential utility for decision-making purposes of virtual 3D models as compared to VRI at the multidisciplinary team meeting, theatre planning, and intra-operative stages.

Conclusion

The use of pre-operative interactive virtual 3D models for surgery planning influences surgical decision-making. Further studies are needed to investigate if the use of these models changes renal cancer surgery outcomes.

Available only for authorised users

Liu J-J, Leppert JT, Maxwell BG, Panousis P, Chung BI (2014) Trends and perioperative outcomes for laparoscopic and robotic nephrectomy using the National Surgical Quality Improvement Program (NSQIP) database. Urol Oncol Semin Orig Investig 32:473–479. https://doi.org/10.1016/j.urolonc.2013.09.012 CrossRef

Patel HD, Mullins JK, Pierorazio PM, Jayram G, Cohen JE, Matlaga BR, Allaf ME (2013) Trends in renal surgery: robotic technology is associated with increased use of partial nephrectomy. J Urol 189:1229–1235. https://doi.org/10.1016/j.juro.2012.10.024 CrossRefPubMed

Rassweiler J, Goezen AS (2018) Laparoscopic partial nephrectomy in the era of robotic surgery: there is a role! Minerva Urol Nefrol 70:6–8. https://doi.org/10.23736/S0393-2249.17.03039-9 CrossRefPubMed

Bertolo RG, Fiori C, Piramide F, Amparore D, Barrera M, Sardo D, Veltri A, Porpiglia F (2018) Assessment of the relationship between renal volume and renal function after minimally-invasive partial nephrectomy: the role of computed tomography and nuclear renal scan. Minerva Urol Nefrol 70:509–517. https://doi.org/10.23736/S0393-2249.18.03140-5 CrossRefPubMed

Bertolo RG, Zargar H, Autorino R, Fiori C, Kaouk JH, Russo P, Thompson RH, Porpiglia F (2017) Estimated glomerular filtration rate, renal scan and volumetric assessment of the kidney before and after partial nephrectomy: a review of the current literature. Minerva Urol Nefrol 69:539–547. https://doi.org/10.23736/S0393-2249.17.02865-X CrossRefPubMed

Hughes-Hallett A, Mayer EK, Pratt P, Mottrie A, Darzi A, Vale J (2015) The current and future use of imaging in urological robotic surgery: a survey of the European Association of Robotic Urological Surgeons. Int J Med Robot Comput Assist Surg MRCAS 11:8–14. https://doi.org/10.1002/rcs.1596 CrossRef

Rosset A, Spadola L, Ratib O (2004) OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 17:205–216. https://doi.org/10.1007/s10278-004-1014-6 CrossRefPubMedPubMedCentral

Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001 CrossRefPubMedPubMedCentral

Yushkevich PA, Gao Y, Gerig G (2016) ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: 2016 38th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 3342–3345

10.

Wake N, Rude T, Kang SK, Stifelman MD, Borin JF, Sodickson DK, Huang WC, Chandarana H (2017) 3D printed renal cancer models derived from MRI data: application in pre-surgical planning. Abdom Radiol 42:1501–1509. https://doi.org/10.1007/s00261-016-1022-2 CrossRef

11.

Porpiglia F, Bertolo R, Checcucci E, Amparore D, Autorino R, Dasgupta P, Wiklund P, Tewari A, Liatsikos E, Fiori C (2017) Development and validation of 3D printed virtual models for robot-assisted radical prostatectomy and partial nephrectomy: urologists’ and patients’ perception. World J Urol 36:1–7. https://doi.org/10.1007/s00345-017-2126-1 CrossRef

12.

Weston MJ (2017) Virtual special issue: renal masses. Clin Radiol 72:826–827. https://doi.org/10.1016/j.crad.2017.06.011 CrossRefPubMed

13.

Hughes-Hallett A, Pratt P, Mayer E, Martin S, Darzi A, Vale J (2014) Image guidance for all—TilePro display of 3-dimensionally reconstructed images in robotic partial nephrectomy. Urology 84:237–243. https://doi.org/10.1016/j.urology.2014.02.051 CrossRefPubMed

14.

Porpiglia F, Fiori C, Checcucci E, Amparore D, Bertolo R (2018) Hyperaccuracy three-dimensional reconstruction is able to maximize the efficacy of selective clamping during robot-assisted partial nephrectomy for complex renal masses. Eur Urol. https://doi.org/10.1016/j.eururo.2017.12.027 CrossRefPubMed

15.

Han K, Bohnen JD, Peponis T, Martinez M, Nandan AR, Yeh DD, Lee J, Demoya M, Velmahos G, Kaafarani HMA (2017) The surgeon as the second victim? Results of the Boston Intraoperative Adverse Events Surgeons’ Attitude (BISA) study. J Am Coll Surg 224:1048–1056. https://doi.org/10.1016/j.jamcollsurg.2016.12.039 CrossRefPubMed

16.

Berger L, Hyde E, Cardoso J, Ourselin S (2017) A self-aware sampling scheme to efficiently train fully convolutional networks for semantic segmentation. arXiv Prepr 1:1–12

17.

Lasser MS, Doscher M, Keehn A, Chernyak V, Garfein E, Ghavamian R (2012) Virtual surgical planning: a novel aid to robot-assisted laparoscopic partial nephrectomy. J Endourol 26:1372–1379. https://doi.org/10.1089/end.2012.0093 CrossRefPubMed

18.

Wake N, Chandarana H, Huang WC, Taneja SS, Rosenkrantz AB (2016) Application of anatomically accurate, patient-specific 3D printed models from MRI data in urological oncology. Clin Radiol 71:610–614. https://doi.org/10.1016/j.crad.2016.02.012 CrossRefPubMed

19.

Raman A, Kuusk T, Hyde ER, Berger LU, Bex A, Mumtaz F (2017) Robotic-assisted laparoscopic partial nephrectomy in a horseshoe kidney: a case report and review of the literature. Urology. https://doi.org/10.1016/j.urology.2017.12.003 CrossRefPubMed

20.

Jones DB, Sung R, Weinberg C, Korelitz T, Andrews R (2016) Three-dimensional modeling may improve surgical education and clinical practice. Surg Innov 23:189–195. https://doi.org/10.1177/1553350615607641 CrossRefPubMed

21.

von Rundstedt FC, Scovell JM, Agrawal S, Zaneveld J, Link RE (2017) Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy. BJU Int 119:598–604. https://doi.org/10.1111/bju.13712 CrossRef

22.

Thompson S, Totz J, Song Y, Johnsen S, Stoyanov D, Ourselin S, Gurusamy K, Schneider C, Davidson B, Hawkes D, Clarkson MJ (2015) Accuracy validation of an image guided laparoscopy system for liver resection. 941509. https://doi.org/10.1117/12.2080974

23.

Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78. https://doi.org/10.1016/j.media.2016.10.004 CrossRefPubMed

24.

Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Med Image Comput Comput Interv. https://doi.org/10.1007/978-3-319-24574-4_28 CrossRef

25.

Wolz R, Chu C, Misawa K, Mori K, Rueckert D (2012) Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases. Med Image Comput Comput Assist Interv 15:10–17. https://doi.org/10.1007/978-3-642-33415-3_2 CrossRefPubMed

26.

Wang G, Li W, Ourselin S, Vercauteren T (2017) Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: International MICCAI Brainlesion workshop. Springer, Cham, pp 178–190. https://doi.org/10.1007/978-3-319-75238-9_16 CrossRef

27.

Schneider M, Hirsch S, Weber B, Székely G, Menze BH (2015) Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters. Med Image Anal 19:220–249. https://doi.org/10.1016/j.media.2014.09.007 CrossRefPubMed

28.

Zuluaga MA, Rodionov R, Nowell M, Achhala S, Zombori G, Mendelson AF, Cardoso MJ, Miserocchi A, McEvoy AW, Duncan JS, Ourselin S (2015) Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning. Int J Comput Assist Radiol Surg 10:1227–1237. https://doi.org/10.1007/s11548-015-1174-5 CrossRefPubMedPubMedCentral

29.

Wang G, Zuluaga MA, Pratt R, Aertsen M, Doel T, Klusmann M, David AL, Deprest J, Vercauteren T, Ourselin S (2016) Dynamically balanced online random forests for interactive scribble-based segmentation. Springer, BerlinCrossRef

30.

Seymour NE, Gallagher AG, Roman SA, O’Brien MK, Bansal VK, Andersen DK, Satava RM (2002) Virtual reality training improves operating room performance: results of a randomized, double-blinded study. Ann Surg 236:458–463. https://doi.org/10.1097/01.sla.0000028969.51489.b4 (discussion 463-4) CrossRefPubMedPubMedCentral

31.

Sullivan GM, Artino AR (2013) Analyzing and interpreting data from Likert-type scales. J Grad Med Educ 5:541–542. https://doi.org/10.4300/JGME-5-4-18 CrossRefPubMedPubMedCentral

Title: Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume-rendered images
Authors: E. R. Hyde
L. U. Berger
N. Ramachandran
A. Hughes-Hallett
N. P. Pavithran
M. G. B. Tran
S. Ourselin
A. Bex
F. H. Mumtaz
Publication date: 01-04-2019
Publisher: Springer International Publishing
Keywords: Nephrectomy
Computed Tomography
Computed Tomography
Nephrectomy
Nephrectomy
Renal Cancer
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01913-5

At a glance: The STEP trials

Springer Medicine

Interactive virtual 3D models of renal cancer patient anatomies alter partial nephrectomy surgical planning decisions and increase surgeon confidence compared to volume-rendered images

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 4/2019

Automatic and near real-time stylistic behavior assessment in robotic surgery

A review on lung boundary detection in chest X-rays

Temporal enhancement of 2D color Doppler echocardiography sequences by fragment-based frame reordering and refinement

An analytical approach for the simulation of realistic low-dose fluoroscopic images

A computer vision technique for automated assessment of surgical performance using surgeons’ console-feed videos

Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches