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Survival Effectiveness of Uniport Anatomic Resections in Patients With Clinical Stage I Lung Cancer: An Observation
Ching Feng Wu1,2,3, Ming Ju Hsieh1, Yueh Fu Fang4
1Chang Gung University, Division of Thoracic and Cardiovascular Surgery, Department of Surgery, Chang Gung Memorial Hospital, Linkou, Taiwan.
Objective:
Although uniportal video-assisted thoracoscopic surgery (uVATS) is increasingly adopted for early-stage lung cancer, long-term survival data comparing different forms of anatomic resection remain limited. This study aimed to evaluate the long-term oncologic outcomes-specifically, 5-year disease-free survival (DFS) and overall survival (OS)-of patients with clinical stage I non-small cell lung cancer (NSCLC) who underwent uVATS segmentectomy or lobectomy. Secondary outcomes included perioperative parameters and complication rates.
Method:
We conducted a retrospective analysis of patients with clinical stage I NSCLC who underwent uVATS anatomical resection (lobectomy or segmentectomy) between January 2014 and December 2020. The primary endpoints were 5-year DFS and OS, while the secondary endpoints included operative time, drainage duration, hospital stay, conversion rates, and postoperative complications.
Results:
A total of 386 patients with clinical stage I NSCLC underwent uVATS anatomical resection, with 280 receiving lobectomy and 106 undergoing segmentectomy. The 5-year DFS and OS rates did not significantly differ between segmentectomy and lobectomy for patients with pathological stage IA tumors. Segmentectomy was associated with a shorter drainage duration. The overall conversion rate to multiple-port VATS or thoracotomy was 1.8%, with no 30-day surgical mortality observed. Prolonged air leaks were the most common complication.
Conclusion:
uVATS anatomical resection is an effective treatment option for clinical stage I NSCLC, offering comparable long-term survival outcomes for segmentectomy and lobectomy in selected patients. Further prospective studies are warranted to confirm these findings and optimize patient selection.
