Learning curve for double micro-portal video-assisted thoracoscopic lobectomy

Introduction

With almost 2.5 million new cases and over 1.8 million deaths worldwide, lung cancer is the leading cause of cancer morbidity and mortality in 2022, responsible for close to one in 8 (12.4%) cancers diagnosed globally and one in 5 (18.7%) cancer deaths (1). Since the first report of video-assisted thoracoscopic surgery (VATS) in 1992, this technique has become increasingly popular, with its feasibility and safety in radical treatment being widely recognized (2,3). The traditional three-ports VATS is the main surgical method for lobectomy: it offers a superior field of vision and convenient access, mitigating the limitations of surgery; it is more conducive to the surgeon’s intervention of hilar structures and lymphoid tissues, may minimize surgical errors, and contributes to a shortened operative duration (4). Based on the established three-port VATS, we have developed a refined double micro-portal VATS surgical technique, which is combined with the “tangential one-way” surgical method during the operation, and different surgical procedures are formulated for each lung lobe (5). This approach eliminates the need for frequent lung lobe rotations, thereby significantly reducing the risk of tumor metastasis associated with tumor cell ingress into the circulatory system (5,6). Ultimately, these advancements aim to achieve minimal incision, optimized surgical process, and superior outcomes.

Since the introduction of cumulative sum (CUSUM) analysis by Bolsin et al., it has been widely applied in the investigation of surgical technique learning processes (6). The learning curve is not only an important way to demonstrate the relationship between surgeon experience and patient perioperative prognosis, but also a means for surgeons to measure the number of surgeries required to achieve a certain level of technical proficiency during the learning process (7).

A total of 106 patients undergoing double micro-portal VATS were included in this study. Perioperative data were collected and analyzed, and learning curves were fitted. The results demonstrated that the double micro-portal VATS lobectomy is a safe, effective, and straightforward surgical approach to learn. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1000/rc).

Methods

Clinical data

We retrospectively analyzed the clinical data of 106 patients who underwent double micro-portal VATS lobectomy performed by the same surgeon in the same treatment group at the Department of Thoracic Surgery, the 2nd Affiliated Hospital of Air Force Medical University of PLA, from March 2015 to December 2016. The selection criteria for eligible patients adhered to the following standards: (I) patients must possess satisfactory functional capacity and demonstrate tolerability for general anesthesia surgery, with a performance status (PS) score (8) ≤1 and American Society of Anesthesiologists (ASA) score (9) ≤II; (II) the maximum ventilation volume (MVV) value in the preoperative pulmonary function test is ≥50% of the predicted value; (III) the operative method is double micro-portal VATS radical resection of lung cancer (lobectomy + systematic lymph node dissection); and (IV) all patients were diagnosed with primary lung cancer by post-pathology, of which 49 cases were diagnosed before operation.

Our department’s innovative double micro-portal VATS technique was utilized in the surgical procedure. All patients underwent general anesthesia procedures with two lumen tube intubation and underwent surgery in a lateral decubitus position with the operating table flexed to increase the intercostal space. During the surgical procedure, the surgeon and assistant surgeon assumed positions on the abdominal side of the patient, with the assistant responsible for holding the thoracoscope.

Incisions are used in the 4th, 7th, and 8th intercostal spaces, with the exception of the right middle lobe. The primary operation hole is located at the 4th intercostal space of the anterior axillary line, approximately 1–2 cm in length (the incision protector stretches the skin and muscle tissue). The diameter of the micro-portal inserted directly through the intercostal space is 5 mm. The length of the 8th intercostal hole of the posterior axillary line is approximately 5 mm, whereas the 7th intercostal space of the axillary midline is also about 5 mm. In cases where the tumor is located in the right middle lobe, the 3rd intercostal space of the anterior axillary line is selected as the main operating hole, whereas the remaining incisions remain unchanged.

Surgical procedures

In order to make the operation more reasonable and simpler, we designed different surgical procedures of hilar dissection for different lobectomies, which we called “tangent line single-direction” VATS lobectomy. This procedure was learned from Professor Liu, that he called “single direction thoracoscopic lobectomy” (10). There is no need to turn over the lobes numerous times during the operation, thus avoiding tumor cells entering the circulatory system as a result of turning and squeezing the lungs.

During the process of lobectomy, it is necessary to expose pulmonary blood vessels and bronchi, as well as dissect the pulmonary hilum or interstitial pleura. The left hand provides tissue tension through the suction and pressure of the suction device for blunt separation, whereas the right hand uses an ultrasonic knife to directly clamp the pleural tissue for incision. After clamping the target tissue, the ultrasonic knife can coagulate or cut the tissue without pulling in other directions. The ultrasonic knife can also directly treat smaller diameter pulmonary blood vessels.

The following criteria were used to assess pleural drainage removal: a threshold of less than 300 mL of fluid for two consecutive days and the absence of an active bleeding or air leak, with a chest X-ray showing lung expansion. These criteria were assessed by the surgeon who performed the surgical procedure. One day after the drainage tube was removed, the patient was allowed to be discharged. Postoperative complications were graded according to the Clavien-Dindo grading system.

Research methods

The perioperative data of 106 patients were collected and analyzed, including sex, age, tumor location, operation time, intraoperative blood loss (IBL), number of dissected lymph nodes and conversion rate to thoracotomy, postoperative hospital stay, incidence of postoperative complications, reoperation rate, postoperative mortality, and disease stage. Operative time is defined as the time from the beginning of incision to the end of suture, including the time of rapid freezing examination and the operative time of wedge pneumonectomy. Intraoperative bleeding volume includes the sum of hemostatic gauze and attractor.

CUSUM analysis

All cases were sorted by operation date using SPSS 29.0 software (IBM Corp., Armonk, NY, USA). The CUSUM1 value of the first case is the difference between the operating time of the first case (OT1) and the average operating time (OTmean) of all cases, namely CUSUM1 = (OT1 − OTmean). The OTn-OTmean value of the second and subsequent cases is the difference between the operating time OTn of this case and the average operating time OTmean, plus the CUSUM value of the previous case, namely, CUSUMn = (OTn − OTmean) + CUSUM (n − 1), which continues to accumulate according to this rule until the last case CUSUM is 0.

The scatter plot of the learning curve was drawn with the number of surgical cases as the abscissa and the value of CUSUM as the ordinate, and the CUSUM learning curve was fitted by SPSS 29.0 software. The goodness-of-fit was judged by the P value, and the curve fitting was successful when P<0.05. The goodness-of-fit is judged by the coefficient R²: the closer R² is to 1, the higher the goodness of curve fitting, and the model with the highest R² is the best fitting model. Using the vertices of the CUSUM fitting curve as the boundary, the learning curve is divided into different stages. The horizontal coordinate value corresponding to the vertex of the curve is the minimum number of surgical cases that must be accumulated over the learning curve.

Statistical analysis

The statistical software SPSS 29.0 was used for data analysis. The clinical data of each group were statistically analyzed and compared according to the different stages of the learning curve. Measurement data that conformed to normal distribution were described by mean ± standard deviation, and independent samples t-test was used to compare between groups; median (range) was used for measurement data that did not conform to normal distribution, Wilcoxon rank sum test was used to compare between groups, and count data were described by number of cases or percentage, and χ² test was used to compare between groups. P≤0.05 indicated a statistically significant difference.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the Ethics Review Committee of the 2nd Affiliated Hospital of Air Force Military Medical University (dated on May 23rd, 2024; approval No. K202405-27). Due to the retrospective nature of the study, written informed consent was not required.

Results

Patient characteristics

Table 1 shows the patient characteristics outcomes. A total of 106 patients were enrolled in this study from March 2015 to December 2016, including 69 males and 37 females, with a mean age of 61.02 years, mean height of 167.16 cm, mean weight of 64.77 kg, and mean body mass index (BMI) of 23.13 kg/m². The study included 93 cases of lobectomy and 13 cases of bilobectomy including four cases of pneumonectomy. According to preoperative computed tomography (CT) scans, there were 24 cases of central lung cancer and 82 cases of peripheral lung cancer. Before surgery, 49 cases were diagnosed by tracheoscopy or puncture biopsy, whereas 57 cases required frozen-section examination during surgery. According to the postoperative pathological types, there were 40 cases of squamous cell carcinoma, 56 cases of adenocarcinoma, 6 cases of adenosquamous carcinoma, 1 case of large cell carcinoma, 1 case of carcinoid, and 2 cases of small cell carcinoma. The staging of lung cancer was determined according to the 7th edition of the International Union Against Cancer (UICC) tumor, node, metastasis (TNM) staging criteria, with 60 cases classified as stage I, 29 cases as stage II, and 17 cases as stage III (11).

CUSUM analysis

The operation time and IBL of all patients were plotted according to the order of the number of operation cases. As illustrated in Figures 1,2, the fitted linear analysis demonstrated a reduction in both operation time and IBL as the number of operation cases increased. The median operative time for all patients was 155 minutes and the median blood loss was 175 mL. The CUSUM learning curve was fitted using SPSS 29.0 software. The test for the curve fitting model of operative time and IBL showed a P value of less than 0.001. The cubic curve was judged to be the best fitting model. Figure 3 shows that the fitting equation for the operation time was CUSUM (min) y=134.6 + 15.84×x − 0.1397×x² − 0.000215×x³. The fitting curve for operation time reached its peak when the number of operation cases accumulated to the 51st case, and the goodness-of-fit coefficient was R²=0.878. Figure 4 shows that the fitting equation used to calculate IBL was CUSUM (mL) y=−238.89+ 81.87×x − 0.9912×x² + 0.002161×x³. The curve reached its peak when the number of operation cases accumulated to 49 cases, and the goodness-of-fit coefficient R²=0.858 (where x represents the number of operation cases). Therefore, the learning curve can be divided into two stages: A and B. Stage A represents the learning progress stage, whereas stage B represents the mastery stage. After a thorough analysis, it was determined that a minimum of 51 cases is required to cross the learning curve.

Figure 1 Operative time of surgeries in chronological order.

Figure 2 Intraoperative bleeding of surgeries in chronological order.

Figure 3 CUSUM OT of learning curve. Dotted line indicates the slope of the best fit curve. The curve is divided into two phases. CUSUM, cumulative sum; OT, operative time.

Figure 4 CUSUM IBL of learning curve. Dotted line indicates the slope of the best fit curve. The curve is divided into two phases. CUSUM, cumulative sum; IBL, intraoperative blood loss.

Clinical results

We compared the clinical data of patients in the two stages. There were no significant differences in sex, mean age, height, weight, BMI, preoperative diagnosis, pathological type, and tumor stage between the two stages. There was a significant difference in tumor location (P=0.003). The two groups showed significant differences in operation time (P=0.008) and IBL (P<0.001). In the proficiency phase, there were no statistically significant differences in conversion rate, postoperative hospital stay, drainage, and postoperative complications compared to the learning phase. In addition, there was no difference in catheterization time and number of lymph node dissection stations between the two groups. Although the number of lymph node dissections was higher in the proficiency stage, there was no statistical difference between the two groups (Table 2). In the subgroup analysis of tumor location types, there were no significant differences in perioperative data between the two groups of patients with central lung cancer (Table 3). However, in patients with peripheral lung cancer, there were significant differences in operating time (P<0.01) and IBL (P<0.001) between the two groups. The study analyzed the preoperative diagnosis of patients and found that those with a preoperative diagnosis had less IBL (P<0.05) but more lymph node dissection (P<0.05), indicating a statistically significant difference. In addition, patients who underwent rapid intraoperative frozen pathology showed significant differences in operative time (P<0.001) and IBL (P<0.001) between the two stages (Table 4). Of all patients, eight required thoracotomy, 20 had delayed discharge due to pulmonary infection, and 25 had delayed remove the drainage tube due to persistent air leak.

Discussion

Lobectomy combined with lymph node dissection is considered the standard surgical treatment for lung cancer patients (12). VATS has significantly reduced the trauma associated with traditional thoracotomy and is widely used in clinical practice. Currently, the most popular and well-established incisions are three-port thoracoscopic surgery and uniportal VATS. Three-port VATS is a widely used clinical procedure due to its advantages including unrestricted surgical instrument angles, convenient left-right hand cooperation, faster operation speed, and ease of learning for less experienced surgeons. Yao et al. analyzed the learning curve of three-port thoracoscopic lobectomy using the CUSUM method, and the results showed that the learning curve for VATS lobectomy for lung cancer requires approximately 26 cases (13). Uniportal VATS is the least invasive technique, but it does have some disadvantages (14). First, the chest tube is placed between the fourth or fifth intercostal space, which may not be conducive to the drainage of pleural effusion. Second, the camera and multiple other instruments are introduced via the single incision, and repeated compression to the intercostal nerve may increase postoperative pain. Third, the uniportal VATS is technical demanding, which is not beneficial for its widespread application, and may require long-term cooperation between the surgeon and assistant. Li et al.’s study of the learning curve of uniportal VATS lobectomy showed that with the increase in the number of surgeries and the accumulation of surgical experience, the stage of proficiency is reached at approximately the 156 cases, and full proficiency, defined as a surgical outcome minimally influenced by surgical experience and being able to perform the procedure independently with the highest quality, can be achieved when 244 cases have been reached (7). Thus, the technique is relatively difficult for beginners and requires a relatively long period of learning and training.

Double micro-portal VATS effectively solves these problems and has the following characteristics: First, the surgical and observation instruments are not in the same channel, which reduces instrument interference. Second, the “tangent line single-direct VATS lobectomy” surgical procedure, which reduces tumor metastasis caused by tumor cells entering the circulatory system due to overturning and squeezing the lung. Third, the combination of suction device and ultrasonic knife avoids grasping the tumor and lymph nodes to ensure complete resection of the tumor and lymph nodes. Fourth, the total length of the three incisions for surgery does not exceed 3 cm, resulting in minimal surface trauma. Fifth, the use of a 5-mm drainage tube after surgery can reduce patients’ pain, reduce the risk of postoperative infection, shorten the hospital stay, and accelerate recovery. Therefore, double portal VATS and traditional three-port VATS provide the same visual field and operation habits to shorten the learning time. At the same time, it is easier to use and work with the instruments without the need for special instruments (5). This study evaluated the learning curve of double micro-portal VATS and the results showed that the learning curve of operation time reached the peak at the 51st case, and when it crossed the fixed point, it changed from the learning improvement stage to the proficiency stage. By comparing the two stages, we found that after the initial learning stage of the first 51 cases, the operation time was significantly shortened and the amount of IBL was also significantly reduced. At the same time, the proficient stage of surgery was associated with reduced postoperative hospital stay, postoperative drainage, and the occurrence of postoperative complications, but there was no statistical difference; more lymph nodes were removed at the proficiency stage, but there was still no statistical difference. Dimitrovska et al. retrospectively evaluated the learning curve of two ports VATS segmentectomy and found that the CUSUM values of 47 cases changed during the surgical operation, and the surgical time and bleeding were significantly reduced (14).

The “learning curve” refers to the rate at which skills or knowledge are acquired over a period of time (15). With the development of modern minimally invasive surgery technology, there has been a lack of research on how to properly understand the growth of surgeons in the field of minimally invasive surgery. As a result, the concept of a “learning curve” has been introduced to describe and guide the level of efficacy and performance of minimally invasive procedures (16). Many scholars attribute the slow adoption of VATS to the high learning curve required (17). The International VATS Lobectomy Consensus Panel advocates that 50 cases are required for technical proficiency in VATS lobectomy (18). In addition, operative time and IBL are important indicators for evaluating surgical proficiency because they are easy to observe and record, and the management of intraoperative crisis situations, such as hemorrhage, is also an important factor that affects surgical technique and better reflects the operator’s surgical level and therapeutic outcome (19). Fiorelli et al. reported that proficiency in VATS was achieved after 49 procedures; vascular injury occurred in all the cases of the early stages of the learning curve, but was managed safely and did not affect the patient’s prognosis (20). Prior to learning curve analysis in the study, the surgeon was proficient in three ports VATS, but had no experience using double micro-portal VATS, so the learning curve was closer to reality. Liang et al. used the multidimensional CUSUM method to analyze the learning curve of single-port VATS, and through a comprehensive analysis of operative time, estimated blood loss, and postoperative hospital stay, they found that 33 cases were required to overcome the learning curve in the senior thoracic surgeon (STS) group and 25 cases in the junior thoracic surgeon (JTS) group. The slope of the JTS group was greater than that of the STS group, which implies that junior thoracic surgeons are more efficient learners; the possible reason for this analysis is that junior surgeons are more likely to improve their surgical skills through up-to-date methods such as learning through video training, long term refresher training, simulator training, and webcasting compared to senior surgeons (21). Thus, expert consensus reported that experience in open heart surgery or multivessel VATS does not affect the learning curve (22).

We further analyzed the influence of tumor location on the learning curve and found that the learning speed of peripheral lung cancer was faster than that of central lung cancer. This is because in patients with central lung cancer, the tumor is located near the hilum of the lung, and it is easy to invade normal tissues such as blood vessels and bronchi. At the same time, it is difficult to dissect the hilar tissue because the border between the tumor and the lymph nodes is unclear. Intraoperative bleeding often affects the visual field and requires more time for dissection and bleeding control. Frozen-section examination during surgery will affect the surgical duration and the amount of blood loss during surgery, whereas most patients with central lung cancer undergo pathology and diagnosis under bronchoscopy. Most patients who send frozen-section examination during surgery are those with peripheral lung cancer, but because the difficulty of surgery of peripheral lung cancer is relatively lower than that of central lung cancer, the learning speed is relatively faster. Therefore, there are still significant differences between the two groups in terms of operation time and IBL.

This study has some shortcomings: First, it is a retrospective study, not a prospective randomized controlled trial. Second, the study was conducted in the same treatment group, and the learning curve of junior surgeons may be longer due to differences in experience in minimally invasive surgery. The use of CUSUM analysis in this study also has some limitations: CUSUM is designed for statistical process control, and the CUSUM method can monitor and detect data changes in real time (23,24). It is also sensitive to small changes and can detect subtle changes in trends. However, when CUSUM is applied to a series where the average of the whole series is the target result, the CUSUM analysis becomes self-referential and can be overinterpreted, if not misinterpreted. Referring to the previous articles, we found that after fitting the learning curve, most researchers introduced the concept of inflection point in the mathematical model, which is the point where the concave direction of the function has changed, and the significance of the mathematical model does not necessarily represent the progress of actual surgical techniques (25). In terms of operation time and IBL, we also found that as the volume of surgery increases, the operation time gradually decreases, and the volume of IBL also decreases. Finally, CUSUM analysis is based on the adjustment of continuous variable operation time, the analysis of the learning curve, the improvement of operation technique, the reduction of operation time and IBL, and the incidence of postoperative complications is also an important reflection (26). Due to the small number of cases included, there is no significant difference in the incidence of postoperative complications.

Conclusions

According to the CUSUM analysis, double micro-portal VATS lobectomy has the best fit when the learning curve is fitted to a cubic curve. The curve can be divided into an ascending stage and a descending stage when bounded by n=51, corresponding to the stages of learning improvement and proficiency, respectively. Therefore, based on the current situation, we believe that at least 51 operations are required to achieve satisfactory results and reach the proficiency stage in performing double micro-portal VATS lobectomy. Double micro-portal VATS lobectomy is easy to learn, safe, effective, and deserves wider adoption.

Acknowledgments

Funding: This work was supported by Clinical Research Funding Program of Air Force Medical University of PLA (No. 2021LC2203).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1000/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1000/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1000/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1000/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the Ethics Review Committee of the 2nd Affiliated Hospital of Air Force Military Medical University (dated on May 23rd, 2024; approval No. K202405-27). Due to the retrospective nature of the study, written informed consent was not required.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Hartwig MG, D'Amico TA. Thoracoscopic lobectomy: the gold standard for early-stage lung cancer? Ann Thorac Surg 2010;89:S2098-101. [Crossref] [PubMed]
3. Swanson SJ, Meyers BF, Gunnarsson CL, et al. Video-assisted thoracoscopic lobectomy is less costly and morbid than open lobectomy: a retrospective multiinstitutional database analysis. Ann Thorac Surg 2012;93:1027-32. [Crossref] [PubMed]
4. Magouliotis DE, Fergadi MP, Spiliopoulos K, et al. Uniportal Versus Multiportal Video-Assisted Thoracoscopic Lobectomy for Lung Cancer: An Updated Meta-analysis. Lung 2021;199:43-53. [Crossref] [PubMed]
5. Tong L, Zheng X, Duan H, et al. The feasibility and efficacy of two micro-portal video-assisted thoracic surgery in pulmonary lobectomy for lung cancer. J Thorac Dis 2018;10:5898-903. [Crossref] [PubMed]
6. Bolsin S, Colson M. The use of the Cusum technique in the assessment of trainee competence in new procedures. Int J Qual Health Care 2000;12:433-8. [Crossref] [PubMed]
7. Li WH, Cheng H, Gan XF, et al. Learning curve of uniportal video-assisted thoracoscopic lobectomy: an analysis of the proficiency of 538 cases from a single centre. Interact Cardiovasc Thorac Surg 2022;34:799-807. [Crossref] [PubMed]
8. Azam F, Latif MF, Farooq A, et al. Performance Status Assessment by Using ECOG (Eastern Cooperative Oncology Group) Score for Cancer Patients by Oncology Healthcare Professionals. Case Rep Oncol 2019;12:728-36. [Crossref] [PubMed]
9. Horvath B, Kloesel B, Todd MM, et al. The Evolution, Current Value, and Future of the American Society of Anesthesiologists Physical Status Classification System. Anesthesiology 2021;135:904-19. [Crossref] [PubMed]
10. Liu L, Che G, Pu Q, et al. A new concept of endoscopic lung cancer resection: Single-direction thoracoscopic lobectomy. Surg Oncol 2010;19:e71-7. [Crossref] [PubMed]
11. Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 2010;17:1471-4.
12. Ginsberg RJ, Rubinstein LV. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. Ann Thorac Surg 1995;60:615-22; discussion 622-3. [Crossref] [PubMed]
13. Yao F, Wang J, Yao J, et al. Video-Assisted Thoracic Surgical Lobectomy for Lung Cancer: Description of a Learning Curve. J Laparoendosc Adv Surg Tech A 2017;27:696-703. [Crossref] [PubMed]
14. Dimitrovska NT, Bao F, Yuan P, et al. Learning curve for two-port video-assisted thoracoscopic surgery lung segmentectomy. Interact Cardiovasc Thorac Surg 2022;34:402-7. [Crossref] [PubMed]
15. Valsamis EM, Chouari T, O'Dowd-Booth C, et al. Learning curves in surgery: variables, analysis and applications. Postgrad Med J 2018;94:525-30. [Crossref] [PubMed]
16. Weksler B. Defining the learning curve for minimally invasive segmentectomy. J Thorac Cardiovasc Surg 2018;156:2004-5. [Crossref] [PubMed]
17. Petersen RH, Hansen HJ. Learning curve associated with VATS lobectomy. Ann Cardiothorac Surg 2012;1:47-50. [Crossref] [PubMed]
18. Yan TD, Cao C, D'Amico TA, et al. Video-assisted thoracoscopic surgery lobectomy at 20 years: a consensus statement. Eur J Cardiothorac Surg 2014;45:633-9. [Crossref] [PubMed]
19. Liu L, Mei J, He J, et al. International expert consensus on the management of bleeding during VATS lung surgery. Ann Transl Med 2019;7:712. [Crossref] [PubMed]
20. Fiorelli A, Cascone R, Carlucci A, et al. Bleeding during Learning Curve of Thoracoscopic Lobectomy: CUSUM Analysis Results. Thorac Cardiovasc Surg 2023;71:317-26. [Crossref] [PubMed]
21. Liang M, Wu P, Xu C, et al. Junior surgeons are quicker to master the single-port thoracoscopic lobectomy: comprehensive analysis of the learning curve and oncological outcomes. World J Surg Oncol 2023;21:134. [Crossref] [PubMed]
22. Han D, Cao Y, Wu H, et al. Uniportal video-assisted thoracic surgery for the treatment of lung cancer: a consensus report from Chinese Society for Thoracic and Cardiovascular Surgery (CSTCVS) and Chinese Association of Thoracic Surgeons (CATS). Transl Lung Cancer Res 2020;9:971-87. [Crossref] [PubMed]
23. Chaput de Saintonge DM, Vere DW. Why don't doctors use cusums? Lancet 1974;1:120-1. [Crossref] [PubMed]
24. Wohl H. The cusum plot: its utility in the analysis of clinical data. N Engl J Med 1977;296:1044-5. [Crossref] [PubMed]
25. Woodall WH, Rakovich G, Steiner SH. An overview and critique of the use of cumulative sum methods with surgical learning curve data. Stat Med 2021;40:1400-13. [Crossref] [PubMed]
26. Matsuura N, Igai H, Ohsawa F, et al. Learning curve for uniportal thoracoscopic pulmonary segmentectomy: how many procedures are required to acquire expertise? Transl Lung Cancer Res 2023;12:1466-76. [Crossref] [PubMed]