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

NCT ID: NCT05109182

Last Updated: 2021-11-05

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 328 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-01-01
• Study Completion Date: 2023-08-01

Brief 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.

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 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.

Conditions

Kidney Cancer

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: SINGLE

Study Groups

Intervention (3D model + CT for surgical planning)

Patients in this arm will receive a 3D model which will be used in addition to the CT scan for surgical planning.

Group Type: EXPERIMENTAL

Innersight3D

Intervention Type: DEVICE

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

Control (CT for surgical planning)

Patients in this arm will only have the a CT scan used for surgical planning.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Innersight3D

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

Intervention Type: DEVICE

Eligibility Criteria

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;

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Royal Free Hospital NHS Foundation Trust

OTHER

Sponsor Role: collaborator

Frimley Park Hospital NHS Trust

OTHER

Sponsor Role: collaborator

Sheffield Teaching Hospitals NHS Foundation Trust

OTHER

Sponsor Role: collaborator

North Bristol NHS Trust

OTHER

Sponsor Role: collaborator

Guy's and St Thomas' NHS Foundation Trust

OTHER

Sponsor Role: collaborator

King's College London

OTHER

Sponsor Role: collaborator

Innersight Labs Ltd

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Central Contacts

Lorenz Berger, PhD

Role: CONTACT

Phone: 07979067365

Email: lorenz@innersightlabs.com

Other Identifiers

295968

Identifier Type: -

Identifier Source: org_study_id