Overview
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.
Description
Background: Lung cancer remains a leading cause of cancer mortality. For early-stage NSCLC, VATS anatomical resection (lobectomy or segmentectomy) is the standard treatment. However, the safety of surgery depends heavily on the patient's pulmonary reserve. Traditional prediction methods, such as the segment-counting rule, have shown prediction errors of up to 20-30% because they do not account for regional heterogeneity in lung ventilation.
Study Design: This is a prospective, multi-center, longitudinal cohort study. The study intends to enroll 60 patients eligible for VATS anatomical resection. Patients will be stratified into two groups:
- VATS Segmentectomy Group (n=30)
- VATS Lobectomy Group (n=30)
- Methodology
1.Preoperative Assessment: Within 30 days before surgery, all participants will undergo high-resolution thin-slice (1 mm) chest CT and standard Pulmonary Function Tests (PFT).
2.3-D Virtual Resection: Using Synapse 3-D software, a patient-specific anatomical model will be reconstructed. The investigator will perform a "virtual resection" simulation to mark the planned resection area. The system will calculate the Planned Resected Ventilated Lung Volume Fraction (pRVLVF), defined based on well-aerated lung tissue (CT attenuation -950 to -700 HU).
3.Surgical Procedure: Patients will undergo standard VATS lobectomy or segmentectomy as clinically indicated.
4.Postoperative Follow-up: PFTs will be performed at 3, 6, and 12 months post-surgery. Follow-up CT scans will be performed at 6 and 12 months to assess structural remodeling.
Objectives and Analysis:
Primary Objective: To validate the accuracy of the pRVLVF-based prediction model. The primary endpoint is the Mean Absolute Error (MAE) of the predicted FEV1 at 3 months post-surgery, with a target accuracy of MAE \< 180 mL.
Secondary Objectives:
- To assess long-term prediction accuracy at 6 and 12 months.
- To quantify the "Compensation Coefficient" (CC) of the residual lung using Linear Mixed-Effects (LME) models, adjusting for age, BMI, and smoking history.
- To evaluate the impact of postoperative complications on the functional recovery curve.
This study seeks to establish a precise, accessible, and dynamic tool for surgical risk assessment and decision-making in thoracic surgery.
Eligibility
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.
