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3D Virtual Models as an Adjunct to Preoperative Surgical Planning

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ClinicalTrials.gov Identifier: NCT03606044
Recruitment Status : Not yet recruiting
First Posted : July 30, 2018
Last Update Posted : July 30, 2018
Sponsor:
Collaborators:
National Institute for Health Research, United Kingdom
Royal Free Hospital NHS Foundation Trust
Information provided by (Responsible Party):
Dr Eoin R Hyde, Innersight Labs Ltd

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.


Condition or disease Intervention/treatment
Kidney Neoplasms Surgical Oncology Device: 3D-models

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.


Study Type : Observational
Estimated Enrollment : 24 participants
Observational Model: Cohort
Time Perspective: Prospective
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
Estimated Study Start Date : August 2018
Estimated Primary Completion Date : December 31, 2018
Estimated Study Completion Date : January 31, 2019

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Kidney Cancer

Group/Cohort Intervention/treatment
MIS-PN
Participants approved for elective robot-assisted partial nephrectomy with T1a or T1b renal tumours.
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





Primary Outcome Measures :
  1. Study participant recruitment rate as assessed by number of participants divided by the total number of invited eligible patients. [ Time Frame: 6 months ]

    Determination of participant recruitment rates of eligible patients to this study.

    Assessment: ratio of consenting patients to eligible patients



Secondary Outcome Measures :
  1. Ratio of study participants willing to be randomized. [ Time Frame: 6 months ]
    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

  2. Time spent by surgeons in pre-operative planning. [ Time Frame: 6 months ]

    Determination of the time spent by surgeons in pre-operative planning using the 3D model building software.

    Assessment: Recording of time spent planning


  3. Practicality of delivering the patient-specific 3D model to the Operating Room. [ Time Frame: 6 months ]

    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


  4. Surveying patient opinion on the usefulness of 3D models. [ Time Frame: 6 months ]

    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?"


  5. Feasibility of measuring of peri-operative operation time. [ Time Frame: 6 months ]

    Measurability of peri-operative operation time from first incision to last suture.

    Assessment: Ability to record the operation time in seconds.


  6. Feasibility of measuring of peri-operative acute haemorrhage events. [ Time Frame: 6 months ]

    Measurability of peri-operative number of acute haemorrhage events.

    Assessment: Ability to record the number of acute haemorrhage events.


  7. Feasibility of measuring of peri-operative blood loss. [ Time Frame: 6 months ]

    Measurability of peri-operative blood loss.

    Assessment: Ability to record the blood loss in millilitres.


  8. Feasibility of measuring of peri-operative number of transfusion events. [ Time Frame: 6 months ]

    Measurability of peri-operative number of transfusion events.

    Assessment: Ability to record the number of transfusion events.


  9. Feasibility of measuring of post-operative number of haemorrhage events. [ Time Frame: 6 months ]

    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).


  10. Feasibility of measuring of post-operative participant length-of-stay in hosital. [ Time Frame: 6 months ]

    Measurability of post-operative participant length-of-stay in hosital.

    Assessment: Ability to record the participant length-of-stay in hosital in days.


  11. Feasibility of measuring of post-operative number of surgical site infection events. [ Time Frame: 6 months ]

    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).




Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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.
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;

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03606044


Contacts
Contact: Neil Hubbard 00 44 20 7794 0500 rf.randd@nhs.net
Contact: Rachel Fay 00 44 20 7794 0500 rf.randd@nhs.net

Sponsors and Collaborators
Dr Eoin R Hyde
National Institute for Health Research, United Kingdom
Royal Free Hospital NHS Foundation Trust
Investigators
Principal Investigator: Faiz H Mumtaz, MBBS, MD Royal Free Hospital NHS Foundation Trust

Additional Information:
Publications:

Responsible Party: Dr Eoin R Hyde, Co-Investigator, Innersight Labs Ltd
ClinicalTrials.gov Identifier: NCT03606044     History of Changes
Other Study ID Numbers: 11605
First Posted: July 30, 2018    Key Record Dates
Last Update Posted: July 30, 2018
Last Verified: July 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
Kidney Neoplasms
Urologic Neoplasms
Urogenital Neoplasms
Neoplasms by Site
Neoplasms
Kidney Diseases
Urologic Diseases