3D Virtual Models as an Adjunct to Preoperative Surgical Planning

NCT03606044 | Completed

• 1 other identifier: 11605
• Study Type: observational
• Target: 24
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to determine the feasibility of undertaking a future definitive RCT to evaluate the clinical effectiveness of complementing existing medical scans with a patient-specific interactive 3D virtual model of the patient's body to assist the surgeon with planning for the operation in the best way possible. Renal cancer patients receive a tri-phasic CT scan as routine practice, thus if the standard imaging protocols are followed, there should be ample imaging data available for 3D model creation. 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.

Conditions

Kidney Neoplasms; Surgical Oncology

Outcome Measures

Primary Outcomes (1)

• Study participant recruitment rate as assessed by number of participants divided by the total number of invited eligible patients.

  Determination of participant recruitment rates of eligible patients to this study. Assessment: ratio of consenting patients to eligible patients

  6 months

Secondary Outcomes (11)

• Ratio of study participants willing to be randomized.

  6 months

• Time spent by surgeons in pre-operative planning.

  6 months

• Practicality of delivering the patient-specific 3D model to the Operating Room.

  6 months

• Surveying patient opinion on the usefulness of 3D models.

  6 months

• Feasibility of measuring of peri-operative operation time.

  6 months

Study Arms (1)

MIS-PN

Participants approved for elective robot-assisted partial nephrectomy with T1a or T1b renal tumours.

Device: 3D-models

Interventions

3D-models DEVICE

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

MIS-PN

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study concerns patients that have been selected for minimally invasive renal cancer surgery at the Royal Free Hospital.

You may qualify if:

• Aged between 18 - 80 years, inclusive;
• Male and female;
• Diagnosed with T1a, or T1b renal tumours;
• Suitable for elective robot-assisted partial nephrectomy;
• Willing and able to provide written informed consent.

You may not qualify if:

• aged \<18 or \>80 years;
• have had prior abdominal surgery;
• have had pre-operative imaging that is not adherent to the study protocol;
• contraindicated for biopsy;
• do not consent to have biopsy;
• have a body mass index (BMI) ≥35 kg/m\^2;
• have a bleeding disorder;
• have baseline chronic kidney disease (CKD);
• not fit or do not consent for surgery;
• chose to have treatment outside the Royal Free Hospital;
• participation in other clinical studies that would potentially confound this study;
• unable to understand English;
• unable to provide consent themselves;

Sponsors & Collaborators

• Dr Eoin R Hyde: lead
• National Institute for Health Research, United Kingdom: collaborator
• Royal Free Hospital NHS Foundation Trust: collaborator

Study Sites (1)

Royal Free London NHS Foundation Trust

London, NW3 2QG, United Kingdom

Location

Related Publications (11)

• 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.

  PMID: 17368303 BACKGROUND

• 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.

  PMID: 29330499 BACKGROUND

• 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.

  PMID: 29322072 BACKGROUND

• 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.

  PMID: 24857271 BACKGROUND

• 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.

  PMID: 25783817 BACKGROUND

• 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.

  PMID: 28090510 BACKGROUND

• 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.

  PMID: 29104028 BACKGROUND

• 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.

  PMID: 15534753 BACKGROUND

• 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.

  PMID: 28062895 BACKGROUND

• 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.

  PMID: 28716212 BACKGROUND

• 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.

  PMID: 26861582 BACKGROUND

Related Links

• The world famous medical images viewer

MeSH Terms

Conditions

Kidney Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Study Officials

• Faiz H Mumtaz, MBBS, MD

  Royal Free Hospital NHS Foundation Trust

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Co-Investigator

Study Record Dates

• First Submitted: July 10, 2018
• First Posted: July 30, 2018
• Study Start: May 1, 2019
• Primary Completion: July 31, 2019
• Study Completion: August 31, 2019
• Last Updated: February 12, 2020

Record last verified: 2020-02

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)