ObjectiveTo evaluate the feasibility and clinical value of robot-assisted lung segmentectomy through anterior approach.MethodsWe retrospectively analyzed the clinical data of 77 patients who underwent robotic lung segmentectomy through anterior approach in our hospital between June 2018 to October 2019. There were 22 males and 55 females, aged 53 (30-71) years. Patients' symptoms, general conditions, preoperative imaging data, distribution of resected lung segments, operation time, bleeding volume, number of lymph node dissected, postoperative duration of chest tube insertion, drainage volume, postoperative hospital stay, postoperative complications, perioperative death and other indicators were analyzed.ResultsAll operations were successfully completed. There was no conversion to thoracotomy, serious complications or perioperative death. The postoperative pathology revealed early lung cancer in 48 patients, and benign tumors in 29 patients. The mean clinical parameters were following: the robot Docking time 1-30 (M=4) min, the operation time 30-170 (M=76) min, the blood loss 20-400 (M=30) mL, the drainage tube time 2-15 (M=4) days, the drainage fluid volume 200-3 980 (M=780) mL and the postoperative hospital time 3-19 (M=7) days.ConclusionRobotic lung segmentectomy through anterior approach is a safe and convenient operation method for pulmonary nodules.
Segmentectomy is the removal of certain segments of the lung with lesions and retaining the normal lung tissue of the lobe. Lung segmentectomy is considered difficult due to the lack of clear anatomical boundaries between lung segments. Segmentectomy has a variety of indications, such as lung cancer, metastatic lung tumors, and many non-malignant diseases. In the treatment of early stage lung cancer, segmentectomy was initially considered only as a treatment option for patients not suitable for conventional lobectomy. As more evidence emerged, the indications for segmentectomy have continued to change over time, and segmentectomy has been widely performed in patients with early stage lung cancer. Theoretically, segmentectomy leads to better preservation of lung function than lobectomy, but the risk of incomplete tumor resection is higher, so the indication of segmentectomy has become a focus of debate. This article will introduce the surgical techniques of segmentectomy and summarize the published and unpublished clinical studies on segmentectomy for the treatment of early stage lung cancer.
ObjectiveTo explore the clinical value of three-dimensional computed tomography bronchography and angiography (3D-CTBA) in robotic lung segmentectomy.MethodsA non-randomized control study was performed and continuously enrolled 122 patients who underwent robotic lung segmentectomy in our hospital from January 2019 to January 2020. 3D-CTBA was performed before operations in 53 patients [a 3D-CTBA group, including 18 males, 35 females, with a median age of 52 (26-69) years] and not performed in the other 69 patients [a traditional group, including 23 males, 46 females, with a median age of 48 (30-76) years]. The clinical data of the patients were compared between the two groups.ResultsAll the patients were successfully completed the surgery and recovered from hospital, with no perioperative death. The baseline characteristics of the patients were not significantly different between the two groups (P>0.05). No significant difference was found in the operative time [120 (70-185) min vs. 120 (45-225) min, P=0.801], blood loss [50 (20-300) mL vs. 30 (20-400) mL, P=0.778], complications rate (17.0% vs. 11.6%, P=0.162), postoperative hospital stay [7 (4-19) d vs. 7 (3-20) d, P=0.388] between the two groups. In the 3D-CTBA group, 5 (9.4%) patients did not find nodules after segmentectomy, and only 1 (1.9%) of them needed lobectomy, but in the traditional group, 8 (11.6%) patients did not find nodules and had to carry out lobectomy, the difference was statistically significant (P<0.05). The follow-up time was 10 (1-26) months, and during this period, there was no recurrence, metastasis or death in the two groups.Conclusion3D-CTBA is helpful for accurate localization of nodules and reasonable surgical planning before operations, and reducing wrong resections in segmentectomy, without increasing the operation time, blood loss and complications. It is safe and effective in anatomical lung segmentectomy.
ObjectiveTo compare the clinical application of empirical thoracoscopic segmentectomy and precise segmentectomy planned by artificial intelligence software, and to provide some reference for clinical segmentectomy. MethodsA retrospective analysis was performed on the patients who underwent thoracoscopic segmentectomy in our department from 2019 to 2022. The patients receiving empirical thoracoscopic segmentectomy from January 2019 to September 2021 were selected as a group A, and the patients receiving precise segmentectomy from October 2021 to December 2022 were selected as a group B. The number of preoperative Hookwire positioning needle, proportion of patients meeting oncology criteria, surgical time, intraoperative blood loss, postoperative chest drainage time, postoperative hospital stay, and number of patients converted to thoracotomy between the two groups were compared. Results A total of 322 patients were collected. There were 158 patients in the group A, including 56 males and 102 females with a mean age of 56.86±8.82 years, and 164 patients in the group B, including 55 males and 109 females with a mean age of 56.69±9.05 years. All patients successfully underwent thoracoscopic segmentectomy, and patients whose resection margin did not meet the oncology criteria were further treated with extended resection or even lobectomy. There was no perioperative death. The number of positioning needles used for segmentectomy in the group A was more than that in the group B [47 (29.7%) vs. 9 (5.5%), P<0.001]. There was no statistical difference in the number of positioning needles used for wedge resection between the two groups during the same period (P=0.572). In the group A, the nodule could not be found in the resection target segment in 3 patients, and the resection margin was insufficient in 10 patients. While in the group B, the nodule could not be found in 1 patient, and the resection margin was insufficient in 3 patients. There was a statistical difference between the two groups [13 (8.2%) vs. 4 (2.4%), P=0.020]. There was no statistical difference between the two groups in terms of surgical time, intraoperative blood loss, duration of postoperative thoracic drainage, postoperative hospital stay, or conversion to open chest surgery (P＞0.05). Conclusion Preoperative surgical planning performed with the help of artificial intelligence software can effectively guide the completion of thoracoscopic anatomical segmentectomy. It can effectively ensure the resection margin of pulmonary nodules meeting the oncological requirements and significantly reduce the number of positioning needles of pulmonary nodules.