Virtual 3D Modelling for Improved Surgical Planning of Robotic-assisted Partial Nephrectomy

Kidney Cancer Clinical Trial

— VISP  

Official title:

Virtual 3D Modelling for Improved Surgical Planning of Robotic-assisted Partial Nephrectomy

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05109182
• Other study ID #: 295968
• Status: Not yet recruiting
• Phase: N/A
• Start date: January 1, 2022
• Est. completion date: August 1, 2023

Study information

• Verified date: October 2021
• Source: Innersight Labs Ltd
• Contact: Lorenz Berger, PhD
• Phone: 07979067365
• Email: lorenz[@]innersightlabs.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

To establish whether surgical planning using virtual 3D modelling (Innersight 3D) improves the outcome and cost-effectiveness of RAPN, allowing more patients to benefit from minimally-invasive procedures.

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 kidney cancer. Preoperative surgery planning decisions are made by radiologists and surgeons upon viewing CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) 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 a more challenging setting to rectify an unplanned complication than during open surgery (Byrn, et al. 2007). Therefore, better surgical planning tools are essential if we wish to improve patient outcome and reduce the cost of a 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. Once segmented, stereolithography files are generated, which can be used to visualise the anatomy and have the components 3D printed. It has previously been reported that such 3D printed models influence surgical decision-making (Wake, et al. 2017). However, 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 (Western, 2017).

Computational 3D surface-rendered virtual models have become a natural advancement from 3D printed models. In the literature, such models are referred to by a variety of names such as '3D-rendered images', (Zheng, et al. 2016), '3D reconstructions', (Isotani, et al. 2015), or 'virtual 3D models', (Wake, et al. 2017). In this protocol we will use the latter nomenclature.

Previous studies have already shown that surgeons benefit from virtual 3D models in the theatre (Hughes-Hallett, 2014; Fan, et al. 2018; Fotouhi, et al. 2018).

In a previous feasibility study (NIHR21460; IRAS 18/SW/0238), we used state-of-the-art CE marked software, called Innersight3D, to generate interactive virtual 3D models of the patient's unique anatomy from their received CT scans, to provide a detailed roadmap for the surgeon prior to the operation. We found that this approach had a positive influence on surgical decision-making.

RAPN is a rapidly developing surgical field, with robots in 70+ UK surgical centres. The main research question to be addressed in the present study is, whether surgical planning using virtual 3D modelling (Innersight 3D) in a randomised controlled trial, improves the outcome and cost-effectiveness of RAPN.

Patients will potentially benefit from this research for several reasons;

1. Due to higher quality surgery and a reduced chance of complications, patients might go home sooner (Shirk, et al. 2018).

2. Less likelihood of an unplanned conversion, which is when the surgeon has to abandon the minimally-invasive approach in favour of open surgery during the operation, due to unforeseen anatomical challenges.

3. Improved patient empowerment and improved consenting, resulting in better patient decision-making. Our previous feasibility study showed that patients strongly agreed that 3D models improved their understanding of the disease treatment decisions and surgical planning.

4. It could also reduce procedure time with less exposure to anesthetic. There are also operational benefits, as these models might improve prediction accuracy of operation complexity and operative time. Thus, surgery list scheduling and hospital-patient flow could be greatly improved. Waiting list could be reduced because of less operations overrun. In addition, surgical team cohesion could also be enhanced. A reduction in theatre time, length-of-stay, would have financial benefits for the health service.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 328
• Est. completion date: August 1, 2023
• Est. primary completion date: August 1, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

Aged 18-80 years; Agreement at Multidisciplinary team meeting that this patient could undergo robotic-assisted partial nephrectomy.

Willing and able to provide written informed consent. RENAL score (tumour complexity) >= 8. Received contrast enhanced abdominal preoperative CT scan. Ability to understand and speak English.

Exclusion Criteria:

Do not consent for robotic assisted partial nephrectomy; Chose to have treatment outside one of the NHS trial sites. Participation in other clinical studies that would potentially confound this study; Have a horseshoe, a solitary kidney or bilateral kidney tumours; Lack of willingness to allow personal medical imaging data to be used for generating a 3D model;

Related Conditions & MeSH terms

• Kidney Cancer
• Kidney Neoplasms

Intervention

Device:
• Innersight3D
Innersight3D generates a virtual interactive 3D model of the CT scan.

Locations

Country	Name	City	State
n/a

Sponsors (7)

Lead Sponsor	Collaborator
Innersight Labs Ltd	Frimley Park Hospital NHS Trust, Guy's and St Thomas' NHS Foundation Trust, King's College London, North Bristol NHS Trust, Royal Free Hospital NHS Foundation Trust, Sheffield Teaching Hospitals NHS Foundation Trust

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Total Console time	This is the time from the start of the robotic operation (arms start moving inside the abdomen) until the end of the robotic operation (arms have been taken out of the abdomen) and will be recorded using the robotic system	18 months
Secondary	Artery preparation time (mins)	Start time: From the point of dissection of gonadal vein. Stop time: As soon as arteries are isolated and ready for clamping.	18 months
Secondary	Tumour preparation time (mins)	Start time: From the point of defatting the kidney (to isolate tumour) Stop time: As soon as the tumour is ready for ultrasound.	18 months
Secondary	Tumour resection time (mins)	Start time: From the point of cutting of tumour Stop: Tumour is removed (excised)	18 months
Secondary	Hilar clamping technique	What clamping technique was used to control blood flow. Choose from [Global ischemia, Selective ischemia, Clampless]	18 months
Secondary	Extirpative technique	What technique was used to remove excise the tumour. Choose from [Enucleation, partial nephrectomyEnucleoresection (resection)] Choose from [Enucleation, partial nephrectomy]	18 months
Secondary	Opened collecting system [yes, no]	Was the collecting system cut open during tumour resection?	18 months
Secondary	Conversion to radical nephrectomy [yes/no]		18 months
Secondary	Clamp time (mins)	Time from when arteries are clamped to time until arteries are unclampsed are taken off. Also known as the warm ischemic time (WIT).	18 months
Secondary	Experience level of surgeon	What is the experience level of the surgeon who is operating? Also were any registrars involved?	18 months
Secondary	Blood loss (ml)		18 months
Secondary	Total Operative time (mins)	From the time that the patient enters the operating theatre to the point of exit, as recorded on the patient notes.	18 months
Secondary	Length of stay (days)	This will be available following hospital discharge. If the patient is not discharged after 4 weeks following the surgery. A maximum length of 28 days should be entered and this along with the reasons should be captured on the adverse events log.	18 months
Secondary	Margin status on histology [positive/negative]	The results from the histology report following the surgery should be recorded.	18 months
Secondary	Post-operative eGFR (ml/min)	Measured 4 weeks after surgery	18 months
Secondary	Post-operative Hemoglobin (g/dL)	Taken 1 day after surgery	18 months
Secondary	Clavien-Dindo Score	Choose option from [Grade I, Grade II, Grade IIIa, Grade IIIb, Grade IVa, Grade IVb]	18 months