3D Virtual Models as an Adjunct to Preoperative Surgical Planning
NCT ID: NCT03606044
Last Updated: 2020-02-12
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
24 participants
OBSERVATIONAL
2019-05-01
2019-08-31
Brief Summary
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This study is a single-site, single-arm, unblinded, prospective, feasibility study aiming to recruit 24 participants from the Royal Free Hospital that are scheduled for robotic-assisted partial nephrectomy. Consenting participants will be recruited over a 6-month period, and interactive 3D virtual models of their anatomy will be generated. These models will be used to aid surgeon-patient communications and to plan for the operation. This study will determine whether a definitive RCT of virtual 3D models as an adjunct to surgery planning is feasible with respect to: recruitment of local authorities and patients; ensuring staff can be adequately trained to deliver programmes within specified timeframes; and assessment of the measurability of key surgical outcomes.
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Detailed Description
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To overcome the limitations of current surgery planning in a soft-tissue oncology setting, dedicated software packages and service providers have provided the capability of classifying the scan voxels into their anatomical components in a process known as image segmentation (see Section 6.1 for more information). Once segmented, stereolithography files are generated which can be used to visualise the anatomy and have the components 3D printed. It has previously been shown that such 3D printed models influence surgical decision-making. However, the relevance of a physical model to plan for a minimally invasive surgical approach is debatable, and the financial and administrative costs of obtaining accurate 3D printed models for routine surgery planning has been speculated to be holding back 3D printed models from breaking into regular clinical usage.
As a necessary precursor to 3D printed models, computational 3D surface-rendered virtual models could be used by the urologist to assist with clinical decision-making. In the literature, such models are referred to by a variety of names such as '3D-rendered images', '3D reconstructions', or 'virtual 3D models'. In this protocol, the investigators will use the latter nomenclature. Virtual 3D models provide many of the advantages of their physical 3D printed counterpart without the challenge of the printing process, they can be easily viewed on standard digital devices such as laptops or smartphones and can be simultaneously viewed and interacted with from anywhere in the world, which could help with collaborative surgery planning between centres. Note that this study's use of virtual 3D models is not to be confused with Virtual-Reality visualisation, which is an immersive environment and currently requires specialist equipment. In support of this study, previous pioneering studies have already shown that surgeons benefit from computational 3D models in the theatre. However, in addition to the available 2D medical images (CT, MRI, volume-rendered images), it has not been shown that virtual 3D models, constructed from the same existing medical scan data, would influence the surgical decision-making process or alter surgeon confidence in their decisions. Crucially, it also remains to be shown that such 3D models can be built reliably and at scale to facilitate their widespread adoption.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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MIS-PN
Participants approved for elective robot-assisted partial nephrectomy with T1a or T1b renal tumours.
3D-models
The study radiologist will generate a patient-specific virtual 3D model of the participant's body from their pre-operational medical scans (CT, and MRI if available) using regulated commercial medical image analysis software, specifically Osirix MD 9.0 (Pixmeo, Geneva, Switzerland).(Rosset et al. 2004)
The CRFw checks that the medical scan segmentation is accurate and validates the virtual 3D model.
The surgeon checks that the medical scan segmentation is accurate and validates the virtual 3D model.
The surgeon uses all available medical scan data, and the virtual 3D model as an adjunct, to assess the patient anatomy and plan the operation accordingly
Interventions
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3D-models
The study radiologist will generate a patient-specific virtual 3D model of the participant's body from their pre-operational medical scans (CT, and MRI if available) using regulated commercial medical image analysis software, specifically Osirix MD 9.0 (Pixmeo, Geneva, Switzerland).(Rosset et al. 2004)
The CRFw checks that the medical scan segmentation is accurate and validates the virtual 3D model.
The surgeon checks that the medical scan segmentation is accurate and validates the virtual 3D model.
The surgeon uses all available medical scan data, and the virtual 3D model as an adjunct, to assess the patient anatomy and plan the operation accordingly
Eligibility Criteria
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Inclusion Criteria
2. Male and female;
3. Diagnosed with T1a, or T1b renal tumours;
4. Suitable for elective robot-assisted partial nephrectomy;
5. Willing and able to provide written informed consent.
Exclusion Criteria
2. have had prior abdominal surgery;
3. have had pre-operative imaging that is not adherent to the study protocol;
4. contraindicated for biopsy;
5. do not consent to have biopsy;
6. have a body mass index (BMI) ≥35 kg/m\^2;
7. have a bleeding disorder;
8. have baseline chronic kidney disease (CKD);
9. not fit or do not consent for surgery;
10. chose to have treatment outside the Royal Free Hospital;
11. participation in other clinical studies that would potentially confound this study;
12. unable to understand English;
13. unable to provide consent themselves;
18 Years
80 Years
ALL
No
Sponsors
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National Institute for Health Research, United Kingdom
OTHER_GOV
Royal Free Hospital NHS Foundation Trust
OTHER
Dr Eoin R Hyde
INDUSTRY
Responsible Party
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Dr Eoin R Hyde
Co-Investigator
Principal Investigators
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Faiz H Mumtaz, MBBS, MD
Role: PRINCIPAL_INVESTIGATOR
Royal Free Hospital NHS Foundation Trust
Locations
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Royal Free London NHS Foundation Trust
London, , United Kingdom
Countries
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References
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Byrn JC, Schluender S, Divino CM, Conrad J, Gurland B, Shlasko E, Szold A. Three-dimensional imaging improves surgical performance for both novice and experienced operators using the da Vinci Robot System. Am J Surg. 2007 Apr;193(4):519-22. doi: 10.1016/j.amjsurg.2006.06.042.
Fan G, Li J, Li M, Ye M, Pei X, Li F, Zhu S, Weiqin H, Zhou X, Xie Y. Three-Dimensional Physical Model-Assisted Planning and Navigation for Laparoscopic Partial Nephrectomy in Patients with Endophytic Renal Tumors. Sci Rep. 2018 Jan 12;8(1):582. doi: 10.1038/s41598-017-19056-5.
Fotouhi J, Alexander CP, Unberath M, Taylor G, Lee SC, Fuerst B, Johnson A, Osgood G, Taylor RH, Khanuja H, Armand M, Navab N. Plan in 2-D, execute in 3-D: an augmented reality solution for cup placement in total hip arthroplasty. J Med Imaging (Bellingham). 2018 Apr;5(2):021205. doi: 10.1117/1.JMI.5.2.021205. Epub 2018 Jan 4.
Hughes-Hallett A, Pratt P, Mayer E, Martin S, Darzi A, Vale J. Image guidance for all--TilePro display of 3-dimensionally reconstructed images in robotic partial nephrectomy. Urology. 2014 Jul;84(1):237-42. doi: 10.1016/j.urology.2014.02.051. Epub 2014 May 22.
Isotani S, Shimoyama H, Yokota I, China T, Hisasue S, Ide H, Muto S, Yamaguchi R, Ukimura O, Horie S. Feasibility and accuracy of computational robot-assisted partial nephrectomy planning by virtual partial nephrectomy analysis. Int J Urol. 2015 May;22(5):439-46. doi: 10.1111/iju.12714. Epub 2015 Mar 17.
Khor WS, Baker B, Amin K, Chan A, Patel K, Wong J. Augmented and virtual reality in surgery-the digital surgical environment: applications, limitations and legal pitfalls. Ann Transl Med. 2016 Dec;4(23):454. doi: 10.21037/atm.2016.12.23.
Pulijala Y, Ma M, Pears M, Peebles D, Ayoub A. Effectiveness of Immersive Virtual Reality in Surgical Training-A Randomized Control Trial. J Oral Maxillofac Surg. 2018 May;76(5):1065-1072. doi: 10.1016/j.joms.2017.10.002. Epub 2017 Oct 13.
Rosset A, Spadola L, Ratib O. OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging. 2004 Sep;17(3):205-16. doi: 10.1007/s10278-004-1014-6. Epub 2004 Jun 29.
Wake N, Rude T, Kang SK, Stifelman MD, Borin JF, Sodickson DK, Huang WC, Chandarana H. 3D printed renal cancer models derived from MRI data: application in pre-surgical planning. Abdom Radiol (NY). 2017 May;42(5):1501-1509. doi: 10.1007/s00261-016-1022-2.
Weston MJ. Virtual special issue: renal masses. Clin Radiol. 2017 Oct;72(10):826-827. doi: 10.1016/j.crad.2017.06.011. Epub 2017 Jul 14. No abstract available.
Zheng YX, Yu DF, Zhao JG, Wu YL, Zheng B. 3D Printout Models vs. 3D-Rendered Images: Which Is Better for Preoperative Planning? J Surg Educ. 2016 May-Jun;73(3):518-23. doi: 10.1016/j.jsurg.2016.01.003. Epub 2016 Feb 6.
Related Links
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The world famous medical images viewer
Other Identifiers
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11605
Identifier Type: -
Identifier Source: org_study_id
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