3D Lung Reconstructions Using Open-source Software for Lung Cancer Surgery

Preoperative three-dimensional (3D) lung reconstructions can reduce intraoperative blood loss, conversion rate, and operation duration. Commercial products predominantly provide these 3D reconstructions, hence the aim of this study was to assess the usability and performance of preoperative 3D lung reconstructions created with open-source software.

A single-center prospective pilot study is conducted within the thoracic surgery department of the Zuyderland Medical Center, Heerlen, the Netherlands. Patients are recruited during preoperative consultation with the thoracic surgeon. The aim of this study is to include at least 12 patients in this pilot study based on the sample size for pilot studies.

Before the pilot study, the surgeons involved in the study set several requirements for the 3D lung reconstructions. A technical physician, a healthcare professional in the Netherlands who combines knowledge of technology and medicine and is licensed to perform medical interventions, creates three-dimensional lung reconstructions using open-source 3D Slicer software that fulfills these aforementioned requirements and uses the available preoperative diagnostic late arterial phase contrast-enhanced CT scans.

Three thoracic surgeons (with at least five years of uniportal VATS experience) and one surgical resident in thoracic surgery assess these 3D lung reconstructions for preoperative planning of uniportal VATS anatomical resections and compare the outcome measures based on the 3D lung reconstructions with the 2D CT scan and intraoperative findings.

Patient characteristics, including age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, body mass index, American Society of Anesthesiologists classification, forced expiratory volume in 1s (FEV1), diffusion capacity of the lung for carbon monoxide by the single-breath technique (DLCO-SB), intoxications, clinical Tumor Nodule Metastasis (TNM) classification 8th edition, previous thoracic surgery, and comorbidities are extracted from patient records. The following perioperative and postoperative data are collected: surgical approach, planned lung resection, performed lung resection, surgery duration, conversion, blood loss, surgery difficulty, the radicality of resection, pathological TNM classification 8th edition, tumor size, final pathology, drain duration, length of hospital stay, and complications within 30 days of surgery. The difficulty of the surgical procedure will be assessed by the thoracic surgeon and rated on a 5-point Likert scale from 1 (very easy) to 5 (very difficult). Length of hospital stay is defined as the number of days between the first day after surgery and the day the patient was clinically discharged.

Descriptive statistical analyses are performed in IBM SPSS Statistics v28 (MacOS, Armonk, New York, United States). Missing data will be reported as such.

Eligibility Criteria:
Inclusion Criteria:

Patients with suspected or biopsy-proven lung cancer scheduled for video-assisted thoracoscopy surgery for lobectomy or segmentectomy

Exclusion Criteria:

* Computed Tomography scan contained motion artifacts
* Surgery was canceled
* Planned surgery changed intraoperatively to another procedure
* Patients who did not provide informed consent