3D VR Modelling for Non-Small Cell Lung Cancer

Overseen By Yogita S Patel

Age: 18+

Sex: Any

Trial Phase: Phase 1 & 2

Sponsor: St. Joseph's Healthcare Hamilton

Disqualifiers: Hypersensitivity to ICG, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

89 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the treatment 3-Dimensional Virtual Reality Modelling with Intravascular Indocyanine Green Fluorescence Mapping for Non-Small Cell Lung Cancer?

The research on 3D reconstruction of lung cancer using CT scanning data suggests that virtual surgery can help visualize lung and blood vessel structures, which may improve surgical planning and outcomes. Additionally, studies on tumor blood volume using advanced imaging techniques show that whole tumor assessments can better predict changes after therapy, indicating that detailed imaging and modeling could enhance treatment effectiveness. ^{1 2 3 4 5}

How is the 3D VR Modelling with Intravascular Indocyanine Green Fluorescence Mapping treatment different from other treatments for non-small cell lung cancer?

This treatment is unique because it uses 3D virtual reality to create a detailed model of the lung and its blood vessels, combined with a special dye (indocyanine green) that helps map blood flow, potentially allowing for more precise surgical planning and tumor removal. ^{1 4 6 7 8}

What is the purpose of this trial?

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.

Research Team

Waël C Hanna, MDCM MBA FRCSC

Principal Investigator

St. Joseph's Healthcare Hamilton / McMaster University

Eligibility Criteria

This trial is for individuals with early-stage Non-Small Cell Lung Cancer who may benefit from a less invasive surgical technique called segmentectomy, which removes part of the lung. It's especially aimed at those who can't have a full lobectomy due to other health issues.

Inclusion Criteria

My tumor is smaller than 3 cm.

My condition is stage 1 non-small cell lung cancer.

My CT scan shows my lung tumor is small enough for a specific type of surgery.

Exclusion Criteria

Hypersensitivity or allergy to ICG, sodium iodide, or iodine

I am not pregnant, breastfeeding, and if of childbearing potential, I am using effective birth control.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Preoperative Planning

3D VR anatomical planning using Elucis platform for targeted pulmonary segmental resection

4 weeks

Surgical Intervention

Execution of segmental resections using 3D VR modelling and NIF-guided techniques

1 day (surgery)

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after surgery, including perioperative complications

30 days

Treatment Details

Interventions

• 3-Dimensional Virtual Reality Modelling with Intravascular Indocyanine Green Fluorescence Mapping

Trial Overview The study tests two types of preoperative planning: one using Elucis 3D VR modeling and another using Synapse 3D, both combined with Intravascular Indocyanine Green Fluorescence Mapping to guide robotic segmental resection surgery.

Participant Groups

Group I: 3D VR Modelling (Elucis) with Intravascular Indocyanine Green Fluorescence Mapping Experimental Treatment 1 Intervention

All patients in Phase I and those randomized to Intervention in Phase II will have preoperative 3D VR reconstructions of their pulmonary anatomy with the target lesion created using the Elucis platform.

Group II: 3D Modelling (Synapse 3D) with Intravascular Indocyanine Green Fluorescence Mapping Active Control 1 Intervention

The patients that are randomized to Control in Phase II will undergo the same intervention as above, but instead of using Elucis for 3D VR reconstructions, Synapse 3D will be used for 3D reconstructions.

Who Is Running the Clinical Trial?

St. Joseph's Healthcare Hamilton

Lead Sponsor

Trials

203

Recruited

26,900+

Findings from Research

The study demonstrated that it is feasible to model the positions of primary tumors and lymph nodes in Stage III lung cancer patients using anatomical surrogates, with mean prediction errors ranging from 0.8 to 1.4 mm based on different modeling approaches.

Using inferential modeling with anatomical surrogates can potentially reduce the processing time of 4D CT scans, which may help in creating more personalized treatment margins for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Inferring positions of tumor and nodes in Stage III lung cancer from multiple anatomical surrogates using four-dimensional computed tomography. Malinowski, KT., Pantarotto, JR., Senan, S., et al.[2021]

References

1. United States pubmed.ncbi.nlm.nih.gov

Inferring positions of tumor and nodes in Stage III lung cancer from multiple anatomical surrogates using four-dimensional computed tomography. [2021]

2. Ireland pubmed.ncbi.nlm.nih.gov

Quantitative helical dynamic contrast enhanced computed tomography assessment of the spatial variation in whole tumour blood volume with radiotherapy in lung cancer. [2022]

3. United States pubmed.ncbi.nlm.nih.gov

Circulating tumor cells from a 4-dimensional lung cancer model are resistant to cisplatin. [2014]

4. China pubmed.ncbi.nlm.nih.gov

[Computer-based three-dimensional reconstruction of lung cancer using 64-slice CT scanning data and virtual surgery]. [2018]

5. United States pubmed.ncbi.nlm.nih.gov

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion. [2021]

6. China pubmed.ncbi.nlm.nih.gov

[Correlation of blood flow assessed by CT perfusion imaging and microvascular ultrastructure in non-small cell lung cancer: a preliminary study]. [2018]

7. Ireland pubmed.ncbi.nlm.nih.gov

Dynamic volume perfusion CT in patients with lung cancer: baseline perfusion characteristics of different histological subtypes. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Assessment of tumor vascularity in lung cancer using volume perfusion CT (VPCT) with histopathologic comparison: a further step toward an individualized tumor characterization. [2022]