3D Virtual Resection for Predicting Lung Function in VATS

This study aims to validate a novel preoperative assessment strategy using three-dimensional (3-D) computed tomography (CT) reconstruction and virtual resection simulation. The goal is to accurately predict postoperative pulmonary function in patients with non-small cell lung cancer (NSCLC) undergoing Video-Assisted Thoracoscopic Surgery (VATS) anatomical resection. Accurate prediction of postoperative lung function is crucial for patient safety. Traditional methods, such as segment counting, often lack precision because they assume all lung segments contribute equally to function, ignoring variations caused by tumors or emphysema. This study utilizes 3-D "virtual resection" to quantify the "Planned Resected Ventilated Lung Volume Fraction" (pRVLVF) before surgery. The study will recruit 60 participants divided into two groups: those undergoing lobectomy (n=30) and those undergoing segmentectomy (n=30). Participants will undergo standard thin-slice CT scans and pulmonary function tests (PFT) before surgery. Postoperatively, lung function and recovery will be tracked at 3, 6, and 12 months to develop a dynamic prediction model and evaluate the compensatory capacity of the residual lung.

Sex

All

Minimum age

18 Years

Maximum age

80 Years

Inclusion Criteria:

* Patients scheduled for video-assisted thoracoscopic (VATS) lobectomy or segmentectomy at National Taiwan University Hospital or NTU Cancer Center.
* Age between 18 and 80 years.
* Patients who have signed the informed consent form agreeing to provide imaging data for 3D modeling.

Exclusion Criteria:

* Age younger than 18 or older than 80 years.
* Patients not scheduled for VATS lobectomy or segmentectomy.
* Patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD).
* Patients unable or unwilling to sign the informed consent form.
* Vulnerable populations.

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