Just-in-Time in situ pre-operative three-dimensional visualisation for anatomical lung surgery

Introduction: context and hypothesis/aims
For patients with local non-small cell lung cancer <2cm with poor pulmonary function, minimally-invasive surgical removal of a single or multiple lung segments (anatomical lung resection) might be considered as a treatment option.
The anatomical lung resection is, however, a complex and low-volume procedure. The use of three-dimensional (3D) models is proven to be of added value for these procedures and can potentially decrease intraoperative blood loss, operative time and patient complications.
Commercial services may provide preoperative models, yet these services are costly and require transfer of sensitive patient data to external entities.
This study aimed to improve patient informed consent and facilitate pre-operative planning of anatomical lung resections with 3D models compared to standard 2D contrast-enhanced CT scans.
We hypothesize that 3D models lead to changes in surgical plans and improve surgical anatomy knowledge. Methods and results: description of the methods used/study design/data collection. 
Presentation of the results addressing the study hypothesis/aims
An in-house CT-scan segmentation protocol using Mimics (Materialise N.V., Leuven, Belgium) software was developed and implemented at Medisch Spectrum Twente (MST), the Netherlands. Patients with stage 1A1-2 lung cancer who underwent a segmentectomy from January 2023 to August 2024 were included in the study. Informed consent for
prospective patients was obtained after local ethics approval. Two surgeons filled in questionnaires to estimate the change of surgical plan and model quality (Kirkpatrick level 2 learning and level 3 behaviour change).
In total, 14 patients were included to make 3D models. Questionnaires revealed that surgeons felt comfortable creating a preoperative plan using the 3D models. The quality of the 3D models was perceived to be good or excellent in all but one case. The use of 3D models changed 32% of all surgical plans compared to 2D CT. Out of these 14 patients, 3
patients received preoperative 3D models prospectively. For these patients, all relevant structures identified preoperatively in the 3D model were identified intra-operatively. In all cases, the just-in-time 3D models showed to have added value in increasing understanding of patient-specific anatomy enabling the surgeons to more thoroughly prepare
for each case.
Discussion of the impact/outcome, and novelty of the Research
Using in-situ preoperative 3D models created by technical medical doctors leads to a significant change in surgical plan and an increased understanding of the patient’s specific anatomical variations, potentially leading to more accurate and safer anatomical resections in the future. Because of low-volume surgery, further research must investigate the clinical benefits of 3D-guided lung segmentectomies (Kirkpatrick Level 4).

Keywords
3D visualisation, in situ simulation, minimally-invasive surgery, Cardio-thoracic surgery

References/Acknowledgements
1. Kato H, Oizumi H, Suzuki J, Suzuki K, Takamori S, Kato H, et al. Indications and technical details of sublobar
resections for small-sized lung cancers based on tumor characteristics. Mini-invasive Surg 2021;5:5. 2021-02-03;5(0).
2. Yotsukura M, Okubo Y, Yoshida Y, Nakagawa K, Watanabe S-i. Indocyanine green imaging for pulmonary
segmentectomy. JTCVS Techniques. 2021/04/01;6.