3D Virtual Resection for Predicting Lung Function in VATS

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: 1. VATS Segmentectomy Group (n=30) 2. 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: 1. To assess long-term prediction accuracy at 6 and 12 months. 2. To quantify the "Compensation Coefficient" (CC) of the residual lung using Linear Mixed-Effects (LME) models, adjusting for age, BMI, and smoking history. 3. 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.