3D Virtual Models as an Adjunct to Preoperative Surgical Planning

Kidney Neoplasms Clinical Trial

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03606044
• Other study ID #: 11605
• Status: Completed
• Start date: May 1, 2019
• Est. completion date: August 31, 2019

Study information

• Verified date: February 2020
• Source: Innersight Labs Ltd
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 24
• Est. completion date: August 31, 2019
• Est. primary completion date: July 31, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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;

Related Conditions & MeSH terms

• Kidney Neoplasms
• Surgical Oncology

Intervention

Device:
• 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

Locations

Country	Name	City	State
United Kingdom	Royal Free London NHS Foundation Trust	London

Sponsors (3)

Lead Sponsor	Collaborator
Dr Eoin R Hyde	National Institute for Health Research, United Kingdom, Royal Free Hospital NHS Foundation Trust

Country where clinical trial is conducted

United Kingdom, 

References & 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. — View 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. — View 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. — View 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. — View 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. — View 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. Review. — View 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. — View 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. Epub 2004 Jun 29. Review. — View 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. — View 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. — View 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. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Ratio of study participants willing to be randomized.	Determination of the willingness of eligible patients to be randomised (although this is a single-arm study and no randomisation will occur, this is an important outcome for future study design); Assessment: ratio of consenting patients that are favourable to randomisation to non-favourable	6 months
Secondary	Time spent by surgeons in pre-operative planning.	Determination of the time spent by surgeons in pre-operative planning using the 3D model building software.; Assessment: Recording of time spent planning	6 months
Secondary	Practicality of delivering the patient-specific 3D model to the Operating Room.	Determination of the practicality of delivering the patient-specific 3D model to the Operating Room visualisation device.; Assessment: Recording whether the 3D model was available for surgeon reference throughout the operation	6 months
Secondary	Surveying patient opinion on the usefulness of 3D models.	Determination of patient opinion on the usefulness of 3D models for improved understanding of the potential risks and benefits involved in their upcoming operation.; Assessment: The patient will be asked a single qualitative question to assess their opinion on the use of 3D models: "With regards to your understanding of the potential risks and benefits of your upcoming operation, do you feel that the additional use of 3D virtual models - decreased your understanding, made no difference to your understanding, or improved your understanding?"	6 months
Secondary	Feasibility of measuring of peri-operative operation time.	Measurability of peri-operative operation time from first incision to last suture.; Assessment: Ability to record the operation time in seconds.	6 months
Secondary	Feasibility of measuring of peri-operative acute haemorrhage events.	Measurability of peri-operative number of acute haemorrhage events.; Assessment: Ability to record the number of acute haemorrhage events.	6 months
Secondary	Feasibility of measuring of peri-operative blood loss.	Measurability of peri-operative blood loss.; Assessment: Ability to record the blood loss in millilitres.	6 months
Secondary	Feasibility of measuring of peri-operative number of transfusion events.	Measurability of peri-operative number of transfusion events.; Assessment: Ability to record the number of transfusion events.	6 months
Secondary	Feasibility of measuring of post-operative number of haemorrhage events.	Measurability of post-operative number of haemorrhage events.; Assessment: Ability to record the number of post-operative haemorrhage events (up to seven days post-operation).	6 months
Secondary	Feasibility of measuring of post-operative participant length-of-stay in hosital.	Measurability of post-operative participant length-of-stay in hosital.; Assessment: Ability to record the participant length-of-stay in hosital in days.	6 months
Secondary	Feasibility of measuring of post-operative number of surgical site infection events.	Measurability of post-operative number of surgical site infection events.; Assessment: Ability to record the number of surgical site infection events (up to seven days post-operation).	6 months

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