Clinical trial NCT05716815: Prospective rAndomized sTudy efficaCy tHree-dimensional rEconstructions Segmentectomy
Official title: Prospective Randomized Study on the Efficacy of Three-dimensional Reconstructions of Bronchial and Vascular Structures on Preoperative Chest CT Scan in Patients Who Are Candidates for Pulmonary Segmentectomy Surgery
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Lung Cancer
Lung; Node
Keywords: Lung Segmentectomy
CT 3D Recostruction
VATS
RATS
Lead sponsor: Ospedale Centrale Bolzano
Sponsor class: OTHER
Collaborators: name: European Institute of Oncology
name: Philipps University Marburg
name: Ernst von Bergmann Hospital
name: Chinese University of Hong Kong
name: Wolfson Medical Center
name: University of Belgrade
name: Harvard University
name: University of Salamanca
First posted: 2023-02-08
Enrollment: 288
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: A prospective multicenter randomized study
Primary purpose: TREATMENT

Brief summary: With this project we want to study the effectiveness of 3D reconstruction of preoperative CT to reduce operating times, blood loss and conversions after segmentectomy performed in thoracoscopy / robotics.

Detailed description: Pulmonary segmentectomy is the surgery of first choice in malignant lung tumors with a diameter \<2 cm in diameter. Patients who are candidates for this type of treatment perform usually a preoperative CT scan with two-dimensional reconstructions (2D: axial, coronary and sagittal). Today it is possible to perform a preoperative three-dimensional reconstruction (3D: volume rendering) of the vessels and bronchi using special software.

Objectives of the study is to analyze, in these patients, the benefits of 3D reconstruction of vessels and bronchi compared to 2D reconstruction, analyzing intra- and post-operative data.The study aims to randomize 288 patients over 36 months.

Study design: Prospective, randomized, controlled study. In 50% of patients the preoperative study of anatomical structures will be performed with the standard 2D method, in the remaining 50% with a 2D and 3D reconstruction. The assignment will take place through access to an online feature on the study website.

Evaluations and statistical methods The statistical analysis will be carried out using parametric and nonparametric descriptive, inferential statistical methods, while the main outcome will be carried out using the analysis of variance (ANOVA) and covariance (ANCOVA) techniques.

Ethical aspects. The study will be conducted in accordance with applicable current legislation. Approval by all relevant ethics committees will be required. Each patient will provide a written consent to participate in the study, after being properly informed.

Eligibility criteria: Inclusion Criteria:

* Segmentectomy performed through a minimally invasive approach (VATS or RATS).
* Pathologically proven NSCLC on the resected specimen.
* Age ≥18
* Signed written informed consent

Exclusion Criteria:

* Prior homolateral cardiothoracic surgery.
* Allergy or any other contraindication to iodinated contrast media.
* Segmentectomy performed through an open approach (thoracotomy)
* Histology different than NSCLC.
* Pregnancy

Primary outcomes:
measure: Conversions from minimally invasive to thoracotomic procedure
timeFrame: Intraoperative
measure: Margin- and disease-free resection
timeFrame: 1 Month postoperative
Secondary outcomes:
measure: Operating times
timeFrame: Intraoperative
measure: Intraoperative blood loss
timeFrame: Intraoperative
measure: Intraoperative major bleeding
timeFrame: Intraoperative
measure: Intraoperative air leaks and the use of sealants
timeFrame: Intraoperative
measure: Postoperative air leaks
timeFrame: up to 2 weeks
measure: Hospitalization times
timeFrame: up to 3 weeks
Other outcomes:
Locations:
Study officials:
affiliation: Central Hospital Bolzano
name: ZARACA FRANCESCO
role: PRINCIPAL_INVESTIGATOR
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