Clinical trial NCT03606044: 3D Virtual Models as an Adjunct to Preoperative Surgical Planning
Official title: Single-site Single-arm Feasibility Study of Patient-specific Interactive 3D Anatomical Models Aimed at Improving Surgery Planning Processes for Complex Renal Cancer Patients
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Kidney Neoplasms
Surgical Oncology
Lead sponsor: Innersight Labs Ltd
Sponsor class: INDUSTRY
Collaborators: name: National Institute for Health Research, United Kingdom
name: Royal Free Hospital NHS Foundation Trust
First posted: 2018-07-30
Enrollment: 24
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

Detailed description: Surgery is the mainstay treatment for abdominal cancer, resulting in over 50,000 surgeries annually in the UK, with 10% of those being for renal cancer. Preoperative surgery planning decisions are made by radiologists and surgeons upon viewing CT and MRI scans. The challenge is to mentally reconstruct the patient's 3D anatomy from these 2D image slices, including tumour location and its relationship to nearby structures such as critical vessels. This process is time consuming and difficult, often resulting in human error and suboptimal decision-making. It is even more important to have a good surgical plan when the operation is to be performed in a minimally-invasive fashion, as it is more challenging setting to rectify an unplanned complication than during open surgery. Therefore, better surgical planning tools are essential if one is to improve patient outcome and reduce the cost of surgical misadventure.

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.

Eligibility criteria: Inclusion Criteria:

1. Aged between 18 - 80 years, inclusive;
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:

1. aged \<18 or \>80 years;
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;

Primary outcomes:
measure: Study participant recruitment rate as assessed by number of participants divided by the total number of invited eligible patients.
timeFrame: 6 months
Secondary outcomes:
measure: Ratio of study participants willing to be randomized.
timeFrame: 6 months
measure: Time spent by surgeons in pre-operative planning.
timeFrame: 6 months
measure: Practicality of delivering the patient-specific 3D model to the Operating Room.
timeFrame: 6 months
measure: Surveying patient opinion on the usefulness of 3D models.
timeFrame: 6 months
measure: Feasibility of measuring of peri-operative operation time.
timeFrame: 6 months
measure: Feasibility of measuring of peri-operative acute haemorrhage events.
timeFrame: 6 months
measure: Feasibility of measuring of peri-operative blood loss.
timeFrame: 6 months
measure: Feasibility of measuring of peri-operative number of transfusion events.
timeFrame: 6 months
measure: Feasibility of measuring of post-operative number of haemorrhage events.
timeFrame: 6 months
measure: Feasibility of measuring of post-operative participant length-of-stay in hosital.
timeFrame: 6 months
measure: Feasibility of measuring of post-operative number of surgical site infection events.
timeFrame: 6 months
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Royal Free London NHS Foundation Trust
Study officials:
affiliation: Royal Free Hospital NHS Foundation Trust
name: Faiz H Mumtaz, MBBS, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.