3-Dimensional Virtual Reality Modelling With Intravascular Indocyanine Green Fluorescence Mapping for Targeted Pulmonary Segmental Resection Trial
- Conditions
- Non Small Cell Lung Cancer
- Registration Number
- NCT06638125
- Lead Sponsor
- St. Joseph's Healthcare Hamilton
- Brief Summary
With the advent of CT screening for lung cancer, an increasing number of NSCLCs are being detected at very early stages, and the demand for pulmonary segmentectomy is rising rapidly. As such, there is a need to develop new surgical techniques to facilitate minimally invasive pulmonary segmentectomy, as segmentectomy may provide a number of significant advantages over lobectomy for patients presenting with early-stage lung cancer, or for patients unable to undergo a full lobectomy due to existing comorbidities. This study will provide the first case series using preoperative 3D virtual reality (VR) anatomical planning (Elucis) added to ICG and NIF-guided robotic segmentectomy to date and will be the first reported use of Elucis-guided targeted pulmonary segmental resection in Canada. As lung cancer is the most frequently fatal cancer in North America, many thousands of patients will be able to benefit from this operation every year.
If successful, this project will establish a novel operation that has the potential of increasing the rates of success for segmental resection. This will allow for further research that will externally validate this technique and ensure that it is reproducible in other centres by other surgeons. As segmental resection is the new standard of care for surgical management of early-stage NSCLC, and because lung cancer is the most frequently fatal cancer in North America, many thousands of patients will be able to benefit from this operation every year.
Equally importantly, the investigators believe that this method will enable them to develop a new way of teaching lung resections, in a manner that is more effective for learners. Further research on the role of VR in teaching lung cancer surgery will very likely be a downstream effect of developing this surgical method.
- Detailed Description
Segmental resection is the new standard of care for early-stage non-small cell lung cancer (NSCLC). However, minimally invasive segmental resection is very difficult to perform, due to high inconsistency and variation in segmental anatomy, and the lack of clearly visible tissue planes between segments (intersegmental planes). The investigators have demonstrated that the rate of successful completion of a segmental resection is only 60%. As such, segmental resections are unlikely to become widely adopted by surgeons outside of centres of high volume expertise, unless an adjunct to facilitate and improve the success rate of this operation is developed. In this trial, the investigators propose to use 3-dimensional (3D) virtual reality (VR) modelling to plan, simulate, and execute segmental resections. The investigators believe that this adjunct will improve the rate of successful completion of this operation.
This submission proposes a novel operation for segmental resections of the lung. Segmental resections are extremely difficult to perform because of the high rate of anatomical variations in segmental anatomy, and the lack of visible tissue planes between segments. In the largest prospective series on segmental resection, the investigators demonstrated that the rate of completion was only 60%. The investigators therefore conducted a subsequent trial utilizing 3D preoperative anatomical planning (Synapse 3D) in conjunction with intraoperative NIF-mapping using intravascular ICG in order to increase the rate of successful completion of a segmental resection. However, planned interim analyses has shown that there is no difference in the rate of successful completion of a segmental resection using Synapse 3D or NIF. This is likely the case because the investigators are using 2D models to plan a 3D operation. In this submission, the investigators hypothesize that adding 3D VR preoperative anatomical planning (Elucis) to NIF-guided segmental resection can greatly increase the rate of success of segmental resections. In this Phase I trial, the investigators propose to describe the technical details of this novel operation, and to evaluate it for safety, feasibility, and learning curve. If successful, this would be the first trial to do so, and would allow for further Phase II and III comparative trials to evaluate this operation.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 89
- Age >/= 18 years
- Tumour size < 3 cm
- Clinical Stage 1 Non-Small Cell Lung Cancer (NSCLC)
- CT-imaging confirming that the tumour is confined to one broncho-pulmonary segment, rendering the patient a candidate for segmental resection.
- Hypersensitivity or allergy to ICG, sodium iodide, or iodine
- Women who are currently pregnant or breastfeeding; or women of childbearing potential who are not currently taking adequate birth control.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Phase I: Feasibility of the operation Through study completion, an average of 2 years Based on the proportion of completed minimally invasive segmental resections. A rate of 60%, similar to the established current standard, will be considered adequate.
Phase I: Safety of the operation 30-days from date of surgery Based on rates of perioperative complications within 30-days of surgery, as defined by the Ottawa Thoracic Morbidity \& Mortality (TMNM) System.
Phase I: Surgeon's learning curve Through study completion, an average of 2 years Evaluated using CUSUM analysis of operative time and successful segmentectomy completion rate over time.
Phase II: Rate of conversion to robotic lobectomy in each arm Through study completion, an average of 2 years Evaluated based on the proportion of minimally invasive segmental resections converted to robotic lobectomy in each arm.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
St. Joseph's Healthcare Hamilton
🇨🇦Hamilton, Ontario, Canada