Clinical trial NCT05109182: Virtual 3D Modelling for Improved Surgical Planning of Robotic-assisted Partial Nephrectomy
Official title: Virtual 3D Modelling for Improved Surgical Planning of Robotic-assisted Partial Nephrectomy
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Kidney Cancer
Lead sponsor: Innersight Labs Ltd
Sponsor class: INDUSTRY
Collaborators: name: Royal Free Hospital NHS Foundation Trust
name: Frimley Park Hospital NHS Trust
name: Sheffield Teaching Hospitals NHS Foundation Trust
name: North Bristol NHS Trust
name: Guy's and St Thomas' NHS Foundation Trust
name: King's College London
First posted: 2021-11-05
Enrollment: 328
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: This RCT, is a multi-centre main trial and will recruit patients selected for minimally-invasive robotic-assisted renal cancer surgery during the enrolment period. The study will compare current surgical planning method to planning with the addition of virtual 3D models in randomised patient groups.

There are no clinical treatment changes/interventions in addition to the standard-of-care procedures. Participants will have 3D models of their anatomy built, clinical team and participant feedback will be obtained in the form of a survey, and the measurability of the key trial outcomes will be assessed as outlined below.

Patient participation in the trial is expected to take no longer than 9 weeks, including a 4-week follow-up, from the initial participant information meeting. Methods used to assess outcomes will employ medical data analysis, participant opinion and observational measurements.
Primary purpose: OTHER

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.

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;

Primary outcomes:
measure: Total Console time
timeFrame: 18 months
Secondary outcomes:
measure: Artery preparation time (mins)
timeFrame: 18 months
measure: Tumour preparation time (mins)
timeFrame: 18 months
measure: Tumour resection time (mins)
timeFrame: 18 months
measure: Hilar clamping technique
timeFrame: 18 months
measure: Extirpative technique
timeFrame: 18 months
measure: Opened collecting system [yes, no]
timeFrame: 18 months
measure: Conversion to radical nephrectomy [yes/no]
timeFrame: 18 months
measure: Clamp time (mins)
timeFrame: 18 months
measure: Experience level of surgeon
timeFrame: 18 months
measure: Blood loss (ml)
timeFrame: 18 months
measure: Total Operative time (mins)
timeFrame: 18 months
measure: Length of stay (days)
timeFrame: 18 months
measure: Margin status on histology [positive/negative]
timeFrame: 18 months
measure: Post-operative eGFR (ml/min)
timeFrame: 18 months
measure: Post-operative Hemoglobin (g/dL)
timeFrame: 18 months
measure: Clavien-Dindo Score
timeFrame: 18 months
Other outcomes:
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