Reshaping the Robotic Learning Curve: The Role of Prior Video-Assisted Thoracoscopic Surgery in Anatomic Lung Resection

Reshaping the Robotic Learning Curve: The Role of Prior Video-Assisted Thoracoscopic Surgery in Anatomic Lung Resection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reshaping the Robotic Learning Curve: The Role of Prior Video-Assisted Thoracoscopic Surgery in Anatomic Lung Resection Donghee Kim, Geun Dong Lee, Hyeong Ryul Kim, Jae Kwang Yun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8052793/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted 13 You are reading this latest preprint version Abstract Objectives The adoption of robotic-assisted thoracoscopic surgery for lung cancer has steadily increased; however, the learning curve and influence of prior video-assisted thoracoscopic surgery (VATS) experience remain underexplored. This study investigates the impact of prior VATS experience on the learning trajectory of robotic anatomic lung resection. Methods We retrospectively analyzed 341 robotic anatomic lung resection procedures performed between January 2018 and December 2024 at a single tertiary referral center. Three thoracic surgeons with varying VATS experience—A (1,500 cases), B (350 cases), and C (50 cases)—were included. Learning curves were assessed using cumulative sum analysis for operative time, complication rates, lymph node yield, and postoperative hospital stay. Change points were identified, and early versus late-phase outcomes were compared. Results All surgeons demonstrated significant reductions in operative time after reaching their respective cumulative sum thresholds (cases 54, 35, and 34 for Surgeons A, B, and C, respectively). While Surgeon A exhibited early procedural stability, Surgeons B and C showed more rapid improvement with experience. Lymph node yield increased significantly for Surgeon B (p = 0.002) and marginally for Surgeon C (p = 0.074). Complication rates and hospital stay modestly increased in later phases, likely reflecting greater case complexity. Conclusion Although prior VATS experience supports initial operative consistency, it does not necessarily shorten the robotic learning curve. Instead, case volume and intensity of robotic exposure appear more critical. These findings underscore the need for structured training programs emphasizing high case density and progressive complexity to optimize robotic surgical proficiency. Robotic-assisted thoracoscopic surgery Lung cancer resection Surgeons' learning curve Prior experience CUSUM analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Robotic anatomic lung resection (RALR) has become an increasingly employed approach in thoracic surgery, offering enhanced visualization, dexterity, and ergonomics compared with conventional minimally invasive techniques [ 1 – 3 ]. As robotic systems are gaining acceptance, understanding the learning curve associated with this technique has become critical, particularly for thoracic surgeons transitioning from video-assisted thoracoscopic surgery (VATS) to robotic-assisted thoracic surgery (RATS). Compared with other robotic specialties such as urology or colorectal surgery, the learning curve in RATS has received relatively limited attention. Soomro et al. highlighted the methodological heterogeneity in robotic learning curve studies and underscored the need for standardized definitions and outcome metrics [ 4 ]. In thoracic surgery, Wilson-Smith et al. reported in a meta-analysis of over 200 studies that technical proficiency in robotic lobectomy is typically achieved after 25–40 cases, although this depends on individual training environments [ 5 ]. Some studies have proposed that prior VATS experience can shorten the robotic learning curve [ 6 – 8 ]; whereas others emphasize that high-volume robotic exposure and focused case repetition may be more critical in gaining proficiency [ 9 – 12 ]. Despite these insights, few studies have directly compared the robotic learning curve across surgeons with differing levels of VATS experience using multidimensional performance metrics. Furthermore, existing research often focuses on isolated outcomes, such as prolonged air leak or operative time, thereby limiting a comprehensive understanding of learning dynamics [ 12 – 13 ]. Therefore, this study aims to assess the learning curve of RALR in three thoracic surgeons with varying VATS backgrounds using cumulative sum (CUSUM) analysis. By examining changes in operative time, complication rates, lymph node harvest, and hospital stay, we aim to determine whether prior VATS experience or concentrated robotic exposure more strongly influences surgical proficiency. Materials and Methods Study Population We retrospectively reviewed patients with primary lung cancer who underwent RALR between January 2018 and December 2024 at a single high-volume tertiary institution. A total of 341 robotic procedures were performed in 340 patients during this period. This study was approved by the Institutional Review Board of our institution (IRB approval number: [2025 − 0897]). The requirement for written informed consent was waived owing to the retrospective nature of the study and the use of de-identified patient data. All research procedures conformed to the principles outlined in the Declaration of Helsinki and relevant institutional guidelines. To focus the analysis on technically comparable procedures, only anatomical lung resections, specifically, lobectomies and segmentectomies, were included. Wedge resections and other non-anatomical procedures were excluded, as their relatively lower complexity and shorter operative times could confound the interpretation of the learning curve. Clinical, operative, and perioperative data were collected from institutional records and reviewed for analysis. Patients were staged according to the eighth edition of the tumor-node-metastasis (TNM) classification system [ 14 ]. Surgeon Backgrounds and Procedural Approach All surgeries were performed using the da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) by three board-certified thoracic surgeons with differing levels of prior VATS experience. Surgeon A had performed approximately 1,500 VATS anatomical resections between February 2009 and December 2018; Surgeon B had performed 350 such cases from March 2015 to December 2018; Surgeon C had performed 50 VATS cases between February and June 2022, prior to initiating robotic practice. At the initiation of robotic practice, Surgeons A, B, and C were in their postgraduate year 24, 18, and 11, respectively (corresponding to 19, 13, and 6 years after board certification in thoracic surgery in South Korea). None of the three surgeons had prior experience with robotic-assisted thoracic surgery (RATS) at other institutions. Surgeons A and B initially performed robotic procedures using the da Vinci Si system and later transitioned to the X and Xi platforms as these became available. Surgeon C began robotic practice directly with the X and Xi systems and subsequently adopted the SP model for selected cases. Port configuration varied among surgeons and evolved. Surgeon A consistently employed a three-port configuration, with the camera port placed at the 6th intercostal space (ICS) along the anterior axillary line (AAL), and working ports at the 4th ICS AAL and the 8th ICS posterior axillary line (PAL). Surgeons B and C initially adopted a four-port approach. This included a utility incision at the 5th ICS AAL, a 12-mm port at the 7th ICS AAL, the camera port at the 9th ICS mid-axillary line (MAL), and an additional 12-mm port at the 11th ICS PAL for the fourth robotic arm. During the transition to single-port strategies, Surgeon C utilized both the da Vinci X/Xi platforms with single-site access and the dedicated SP model. For single-site approaches with the X/Xi system, a 5-cm incision was made at the 7th ICS AAL. When the SP platform was used, a 5-cm incision was created at the subcostal margin along the midclavicular line. Standard RALR techniques were applied, including sequential division of the pulmonary vein, artery, and bronchus. Depending on fissure anatomy and hilar adhesions, either a fissure-based or hilar-first approach was used. Stapling was performed using the da Vinci SureForm system (Intuitive Surgical, Sunnyvale, CA, USA), whereas the ENDO GIA stapler (Medtronic, Minneapolis, MN, USA) was employed for procedures conducted with the SP platform. For lymph node dissection, lobectomy cases underwent systematic hilar and mediastinal dissection (stations 2R, 4R, 7, 8, 9 for right-sided resections; 5, 6, 7, 8, 9 for left-sided resections). Segmentectomy cases included hilar and intersegmental nodes, with selective mediastinal dissection depending on tumor location. Conversion to thoracotomy or the addition of auxiliary ports was permitted in cases with inadequate exposure or complications such as major vascular injury. Outcome Measures Four perioperative outcome measures were assessed to evaluate the learning curve: total operative time, complication rate, number of harvested lymph nodes, and length of hospital stay. Total operative time was defined as the duration from the start of skin incision to completion of skin suturing. Console time would have provided a more precise measure of technical performance; however, these data were not consistently available across all cases. Complications were defined as any adverse event classified as Clavien–Dindo grade II or higher [ 15 ]. Additionally, operative cadence was assessed, defined as the average number of procedures performed per month during the study period, as well as the local operative cadence (reflecting the short-term case frequency around the cutoff point) within ± 5 procedures of the cutoff point. Treatment approaches for stage III or stage IV non-small cell lung cancer (NSCLC) were determined by a multidisciplinary team. Surgical indications included clinical stage III NSCLC that became resectable after achieving adequate response or nodal downstaging with immunochemotherapy [ 16 – 17 ]. In patients with oligometastatic NSCLC, surgical resection was selectively performed if patients were younger, had good performance status, and presented with a limited number of metastases (≤ 3) confined to a single organ (e.g., brain, adrenal), and if complete resection of both the primary and metastatic lesions was feasible within a multimodal treatment strategy [ 18 – 20 ]. Statistical Analysis Continuous variables are reported as mean (standard deviation) or median with interquartile range, while categorical variables are presented as counts and percentages. For two-group comparisons (e.g., early vs. late phases), the Student’s t-test or Mann–Whitney U test was applied, as appropriate. For three-group comparisons (e.g., baseline characteristics across surgeons), analysis of variance (ANOVA) or the Kruskal–Wallis test was used. Categorical variables were compared using the chi-square or Fisher’s exact test, as appropriate. The learning curve threshold, referred to as the “change point,” was defined as the case number showing the greatest deviation from the zero axis on the risk-adjusted CUSUM plot. Based on the identified change points, each surgeon’s series was divided into early (pre-change point) and late (post-change point) phases. To analyze performance improvement over time, unadjusted and risk-adjusted CUSUM analyses were separately performed for each surgeon and each outcome. Unadjusted CUSUM provided a representation of case-by-case trends in performance, whereas risk-adjusted CUSUM accounted for case-mix variability to more accurately identify technical improvement. To enable risk-adjusted CUSUM analysis, multivariable regression models were developed for each surgeon to estimate the expected value of each outcome based on patient and operative characteristics. Log-transformed linear and logistic regression models are presented in the Supplementary Materials. Covariates included age, sex, smoking, tumor histology, extent of surgery, port number, pathologic stage, neoadjuvant therapy, and combined surgery. Certain categorical levels (e.g., histology III, pStage IV) were excluded from coefficient estimation owing to near-zero case counts, resulting in non-estimable values (NA) in the regression outputs. Missing data were infrequent and were handled using complete-case analysis without imputation. All statistical analyses were performed using R software (R Foundation for Statistical Computing, Vienna, Austria). A p-value < 0.05 was considered statistically significant. Results A total of 341 RALRs were analyzed: 99 cases by Surgeon A, 141 by Surgeon B, and 101 by Surgeon C. The cumulative number of procedures performed by each surgeon is shown in Fig. 1 . Baseline patient characteristics were well balanced across the three groups (Table 1 ). Surgeon B’s patients were slightly younger on average (mean age difference ~ 3 years, p = 0.03), but no significant differences were observed in sex distribution, pulmonary function, or clinical stage. While slightly more frequent in Surgeon C’s cohort (4% vs. ≤ 1%, p = 0.037), neoadjuvant therapy was infrequent overall (five patients). Table 1 Patient demographics Variable A (N = 99) B (N = 141) C (N = 101) P-value Age, y ± SD 61.3 ± 8.9 59.9 ± 9.0 62.9 ± 8.6 0.033 Male sex, n (%) 42 (42.4%) 69 (48.9%) 43 (42.6%) 0.501 Comorbidity, n (%) 0.216 0 48 (48.5%) 55 (39.0%) 38 (37.6%) 1 34 (34.3%) 48 (34.0%) 32 (31.7%) 2 17 (17.2%) 38 (27.0%) 31 (30.7%) Smoking status, n (%) 0.342 Never 64 (64.6%) 78 (55.3%) 61 (60.4%) Ever 35 (35.4%) 63 (44.7%) 40 (39.6%) FEV1 pred < 60%, n (%) 0 (0.0%) 1 (0.7%) 0 (0.0%) 0.491 DLCO pred < 60%, n (%) 1 (1.0%) 3 (2.1%) 1 (1.0%) 0.695 Clinical stage, n (%) 0.929 I 82 (82.8%) 111 (78.7%) 83 (82.2%) II 10 (10.1%) 15 (10.6%) 9 (8.9%) III 4 (4.0%) 11 (7.8%) 7 (6.9%) IV 3 (3.0%) 4 (2.8%) 2 (2.0%) Neoadjuvant treatment, n (%) 1 (1.0%) 0 (0.0%) 4 (4.0%) 0.037 Comorbidity denotes the number of preexisting medical conditions: 0 = none; 1 = one; 2 = two or more. FEV₁ and DLCO are presented as percent predicted. Clinical stage is classified according to the 8th edition TNM system for NSCLC. Operative characteristics and perioperative outcomes are summarized in Table 2 . All three surgeons performed a similar proportion of lobectomies versus segmentectomies (lobectomy rate 80–89%, p = 0.09), with uniformly high R0 resection rates. Port configurations differed significantly between surgeons: Surgeon A primarily used a three-port approach, Surgeon B consistently employed four ports, and Surgeon C transitioned to reduced-port strategies in later cases ( p < 0.001). The overall conversion rate to thoracotomy remained low across all groups (0–1%). Unadjusted mean operative times varied modestly: 140–150 minutes overall, with Surgeon B’s cases averaging slightly longer (p = 0.039). Complication rates were comparable across surgeons (12–13%, p = 0.95), and mean postoperative hospital stays was 6 days in each group (p = 0.73). Estimated blood loss was generally low across all groups (median < 10 mL), and only one patient required intraoperative transfusion due to major bleeding. Table 2 Operative and pathological data Variable A (N = 99) B (N = 141) C (N = 101) P-value Operative time, min ± SD 140.2 ± 34.2 149.7 ± 30.3 140.7 ± 35.4 0.039 Combined resection, n (%) 6 (6.1%) 21 (14.9%) 12 (11.9%) 0.105 Port number 0.000 1 0 (0.0%) 0 (0.0%) 20 (19.8%) 2 3 (3.0%) 0 (0.0%) 10 (9.9%) 3 91 (91.9%) 11 (7.8%) 4 (4.0%) 4 5 (5.1%) 130 (92.2%) 67 (66.3%) Conversion, n (%) 0 (0.0%) 0 (0.0%) 1 (1.0%) 0.304 Surgical extent, n (%) 0.092 Lobectomy 84 (84.8%) 126 (89.4%) 80 (79.2%) Segmentectomy 15 (15.2%) 15 (10.6%) 21 (20.8%) Lymph nodes harvested, n ± SD 27.1 ± 10.6 28.0 ± 10.9 25.9 ± 9.9 0.314 Resection type, n (%) 0.390 R0 97 (98.0%) 139 (98.6%) 101 (100.0%) R1 2 (2.0%) 2 (1.4%) 0 (0.0%) Histology, n (%) 0.637 ADC 85 (85.9%) 125 (88.7%) 87 (86.1%) SCC 7 (7.1%) 12 (8.5%) 9 (8.9%) Others 7 (7.1%) 4 (2.8%) 5 (5.0%) Pathologic stage, n (%) 0.513 I 71 (71.7%) 95 (67.4%) 71 (70.3%) II 13 (13.1%) 12 (8.5%) 12 (11.9%) III 12 (12.1%) 30 (21.3%) 14 (13.9%) IV 3 (3.0%) 4 (2.8%) 4 (4.0%) Complication, n (%) 12 (12.1%) 19 (13.5%) 13 (12.9%) 0.954 Complication Grade 0.096 Grade I 1 (1.0%) 11 (7.8%) 4 (4.0%) Grade II 2 (2.0%) 3 (2.1%) 2 (2.0%) Grade IIIa 7 (7.1%) 5 (3.5%) 3 (3.0%) Grade IIIb 2 (2.0%) 0 (0.0%) 4 (4.0%) Pneumonia, n (%) 2 (2.0%) 1 (0.7%) 2 (2.0%) 0.621 Chylothorax, n (%) 3 (3.0%) 3 (2.1%) 2 (2.0%) 0.865 Vocal cord palsy, n (%) 1 (1.0%) 3 (2.1%) 2 (2.0%) 0.794 Readmission, n (%) 3 (3.0%) 2 (1.4%) 3 (3.0%) 0.636 Hospital stay, days ± SD 6.1 ± 3.0 6.0 ± 2.3 6.4 ± 4.4 0.726 R0: complete resection with negative margins; R1: microscopically positive margins; Histology: ADC = adenocarcinoma, SCC = squamous cell carcinoma; Pathologic stage: based on the 8th TNM classification for NSCLC; Complication grade: according to the Clavien–Dindo classification. CUSUM analysis of operative time demonstrated distinct learning curve profiles for each surgeon (Fig. 2 ). Significant reductions in mean operative time were observed after the CUSUM-defined thresholds: case 54 for Surgeon A (150.1 → 128.3 min, p = 0.001), case 35 for Surgeon B (170.3 → 142.9 min, p < 0.001), and case 34 for Surgeon C (164.9 → 128.4 min, p < 0.001). The overall operative cadences were 1.38, 1.92, and 3.35 cases per month for Surgeons A, B, and C, respectively, with corresponding local cadences at the cutoff cases of 2.50, 9.30, and 7.97 cases per month. For complication incidence, CUSUM learning curves demonstrated distinct patterns across surgeons (Fig. 3 ). Change points were identified at case 48 for Surgeon A, case 57 for Surgeon B, and case 75 for Surgeon C. Surgeon A’s curve exhibited a downward inflection at the change point, corresponding to a reduction in postoperative complications during the later phase of his experience (from 20.8 to 1.9%, p = 0.930). In contrast, the curves for Surgeons B and C showed upward inflections, suggesting increased complication rates in their late-phase cases (3.5% → 8.3%, p = 0.880; 5.3% → 15.4%, p = 0.199, respectively). Lymph node yield tended to improve over time for Surgeons B and C (Fig. 4 ). The curves for Surgeons B and C show upward inflections at cases 90 and 74, respectively, reflecting increased lymph node retrieval in their late-phase cases. Surgeon B’s average yield increased significantly from 25.7 to 32.0 nodes (p = 0.002), while that of Surgeon C increased from 24.6 to 29.2 nodes without reaching statistical significance (p = 0.074). In contrast, surgeon A’s curve inflects downward at case 39, showing an overall decreasing trend with a secondary peak in the later phase. Accordingly, the mean number of lymph nodes retrieved declined slightly from 29.1 to 25.9 (p = 0.144). Postoperative hospital stays modestly increased during the later phase for all surgeons (Fig. 5 ). All three surgeons’ curves exhibit upward inflections at their respective change points: case 72 for Surgeon A, 45 for Surgeon B, and 58 for Surgeon C, indicating longer postoperative stays in the late phase of their learning curves. Specifically, the mean hospital stay increased from 4.4 to 5.3 days for Surgeon A (p = 0.015), 4.1 to 5.0 days for Surgeon B (p = 0.001), and 4.3 to 5.0 days for Surgeon C (p = 0.271). This observation likely reflects a trend toward more complex cases as experience accumulated. Discussion This study evaluated the learning curves of RALR among three surgeons with varying levels of prior VATS experience. All surgeons demonstrated reduced operative time after reaching CUSUM-defined thresholds; however, the pace of improvement varied. Surgeon A, with extensive VATS experience, showed early procedural consistency but reached proficiency more gradually. In contrast, Surgeons B and C, despite limited VATS backgrounds, achieved faster operative gains with concentrated robotic exposure. Complication rates remained similar between early and late phases. Lymph node yield improved for Surgeons B and C, whereas hospital stay modestly increased across all surgeons, possibly reflecting a shift toward more complex case selection over time. This study differs from prior investigations in several important ways. Although several earlier studies examined robotic learning curves in single-surgeon settings or focused on limited outcome measures [ 3 , 6 , 13 , 21 ], we evaluated multiple performance indicators across three surgeons with varying levels of prior experience. By assessing operative time, complication rates, lymph node yield, and hospital stay together, we aimed to provide a more balanced view of robotic proficiency development. Additionally, the use of both unadjusted and risk-adjusted CUSUM analyses allowed us to account for variations in case complexity over time [ 22 ]. To our knowledge, this is among the first thoracic surgical studies to apply such a structured framework across multiple operators using real-world institutional data. As noted above, surgeons with limited prior VATS experience demonstrated faster improvement in operative efficiency, despite having less initial exposure to minimally invasive techniques [ 7 ]. Although prior VATS experience likely contributes to early procedural familiarity and technical safety [ 6 , 8 ], our findings indicate that it does not guarantee accelerated proficiency or necessarily translate directly to robotic surgery. Instead, sustained and focused exposure to robotic techniques may be more instrumental in accelerating proficiency than the cumulative volume of prior thoracoscopic procedures alone(5). Complication rates did not change significantly between early and late phases; however, their trajectories varied according to the surgeon. Surgeon A demonstrated a decreasing trend over time, whereas Surgeons B and C experienced modest increases despite technical improvement. This pattern likely reflects a gradual shift toward more complex case selection as surgeons gained confidence with the robotic platform. As proficiency increased, surgeons may have undertaken more challenging resections, such as larger tumors, post-neoadjuvant cases, or reduced-port procedures (See Supplementary Figs. 1 and 2 ), which could offset the expected reductions in complication rates. These findings highlight the need to interpret perioperative outcomes within the context of evolving case complexity, rather than attributing them solely to technical skill [ 23 – 24 ]. Compared with published benchmarks (major complication rates ~ 4–9%, chylothorax ~ 0.5–1%, RLN palsy < 1%), our rates are broadly in line. Some complication rates appear relatively elevated, but given the small number of events, firm conclusions seem to be limited [ 25 , 26 ]. Lymph node yield improved notably over time for Surgeons B and C, while remaining stable or slightly decreasing for Surgeon A. This result may reflect differences in emphasis during the learning process; less experienced surgeons may have gradually increased the extent of lymphadenectomy as their confidence grew, whereas a more experienced surgeon may have already reached a performance plateau or tailored dissection based on case-specific factors. Similarly, postoperative hospital stay increased modestly during the later phase across all surgeons. As with complications, this trend likely reflects a transition toward more complex cases, such as higher-stage tumors or those managed with reduced-port approaches, rather than a deterioration in operative efficiency. Taken together, these findings underscore the importance of structured robotic training pathways that emphasize high case volume and consistent exposure, rather than assuming an automatic translatability of VATS skills [ 27 – 29 ]. While prior thoracoscopic experience may offer initial procedural stability, the intensity and frequency of robotic case engagement appears to play a pivotal role in accelerating proficiency and broadening surgical capability. Tailoring training curricula to balance safety, exposure, and gradual complexity may help optimize the learning process and ensure safe integration of robotic platforms into thoracic surgical practice [ 30 ]. This study has some limitations. First, its retrospective, single-institution design introduces selection bias and limits generalizability. Surgeons selected their cases, and less complex procedures may have been preferentially performed during the early phase. Despite employing risk-adjusted CUSUM analyses to account for measurable confounders, residual bias from unmeasured variables cannot be excluded. Second, methodological and technical variations among surgeons complicate direct comparisons. Surgeon C introduced reduced-port techniques, such as dual-port and single-port approaches, later in their robotic experience, resulting in distinct learning phases that were not encountered by Surgeons A or B. This observation is reflected in the multi-peaked operative time CUSUM curve observed for Surgeon C. Heterogeneity in port configuration between surgeons may have influenced operative time through differences in bedside assistant involvement, introducing residual bias in inter-surgeon comparisons. Additionally, Surgeon B frequently delegated console time to assistants for proctoring purposes, which may have influenced operative metrics and further limited inter-surgeon comparability. Third, although each surgeon contributed approximately 100 cases, the sample size may be underpowered to detect subtle differences in infrequent outcomes, such as complication rates. Moreover, all data were derived from a high-volume tertiary center with experienced surgical teams and institutional support for robotic programs; thus, the findings may not fully translate to smaller centers or institutions with differing resources. Finally, our analysis focused on short-term perioperative outcomes. While oncologic data were collected, limited follow-up duration precluded definitive conclusions regarding long-term endpoints such as recurrence or survival. In conclusion, this study demonstrates that the learning curve for RALR is shaped by prior VATS experience and, to a greater extent, by the intensity and continuity of robotic case exposure. While experienced surgeons may benefit from early procedural stability, consistent engagement with the robotic platform appears to be essential for achieving technical proficiency and broadening surgical scope. These findings may inform the development of future training curricula and credentialing standards in robotic thoracic surgery. Abbreviations RALR: robotic anatomic lung resection VATS: video-assisted thoracoscopic surgery RATS: robotic-assisted thoracic surgery CUSUM: cumulative sum TNM: tumor-node-metastasis Declarations Clinical registration number Not applicable Conflict of interest statement None declared. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Donghee Kim and Jae Kwang Yun. The first draft of the manuscript was written by Donghee Kim and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement We would like to thank Editage (www.editage.co.kr) for English language editing. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Cerfolio RJ, Bryant AS, Skylizard L, Minnich DJ . Initial consecutive experience of completely portal robotic pulmonary resection with 4 arms. J Thorac Cardiovasc Surg. 2011;142(4):740-6. https://doi.org/10.1016/j.jtcvs.2011.07.022 Louie BE, Farivar AS, Aye RW, Vallières E . Early experience with robotic lung resection results in similar operative outcomes and morbidity when compared with matched video-assisted thoracoscopic surgery cases. 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Cancers (Basel). 2024;16(7). https://doi.org/10.3390/cancers16071302 Wang M, Wang X, Yang R, Geng M, Zhang S, Yang Z, et al.Conversion Surgery for Initially Unresectable Stage Ⅲ Nonsmall Cell Lung Cancer After Induction Treatment of Immunochemotherapy: A Multicenter Study. Clin Lung Cancer. 2025;26(3):e131-e40.e1. https://doi.org/10.1016/j.cllc.2024.11.005 Couñago F, Luna J, Guerrero LL, Vaquero B, Guillén-Sacoto MC, González-Merino T, et al.Management of oligometastatic non-small cell lung cancer patients: Current controversies and future directions. World J Clin Oncol. 2019;10(10):318-39. https://doi.org/10.5306/wjco.v10.i10.318 Chen YH, Ho UC, Kuo LT . Oligometastatic Disease in Non-Small-Cell Lung Cancer: An Update. Cancers (Basel). 2022;14(5). https://doi.org/10.3390/cancers14051350 Yoo S, Cho WC, Lee GD, Choi S, Kim HR, Kim Y-H, et al.Long-term Surgical Outcomes in Oligometastatic Non-small Cell Lung Cancer: A Single-Center Study. 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J Thorac Dis. 2017;9(9):3105-13. https://doi.org/10.21037/jtd.2017.08.11 Wilson-Smith AR, Wilson-Smith CJ, Anning N, Muston B, Eranki A, Williams ML, et al.The perioperative outcomes of uniportal robotic-assisted thoracic surgeries-a systematic review and meta-analysis of surgical cohort studies and case reports. Ann Cardiothorac Surg. 2023;12(2):73-81. https://doi.org/10.21037/acs-2023-urats-37 Cao C, Louie BE, Melfi F, Veronesi G, Razzak R, Romano G, et al.Outcomes of major complications after robotic anatomic pulmonary resection. J Thorac Cardiovasc Surg. 2020;159(2):681-6. https://doi.org/10.1016/j.jtcvs.2019.08.057 Bottet B, Seguin-Givelet A, Fourdrain A, Sarsam M, Boddaert G, Boulate D, et al.Multicenter evaluation of patient safety incidents in lung surgery: The Epithor Patient Safety Incident study. J Thorac Cardiovasc Surg. 2025;169(5):1356-66.e4. https://doi.org/10.1016/j.jtcvs.2024.10.054 Shahin GMM, Brandon Bravo Bruinsma GJ, Stamenkovic S, Cuesta MA . Training in robotic thoracic surgery-the European way. Ann Cardiothorac Surg. 2019;8(2):202-9. https://doi.org/10.21037/acs.2018.11.06 Kingma BF, Hadzijusufovic E, Van der Sluis PC, Bano E, Lang H, Ruurda JP, et al.A structured training pathway to implement robot-assisted minimally invasive esophagectomy: the learning curve results from a high-volume center. Diseases of the Esophagus. 2020;33(Supplement_2). https://doi.org/10.1093/dote/doaa047 Kim SS, Schumacher L, Cooke DT, Servais E, Rice D, Sarkaria I, et al.The Society of Thoracic Surgeons Expert Consensus Statements on a Framework for a Standardized National Robotic Curriculum for Thoracic Surgery Trainees. Ann Thorac Surg. 2025;119(4):719-32. https://doi.org/10.1016/j.athoracsur.2024.12.003 Al Zaidi M, Wright GM, Yasufuku K . Suggested robotic-assisted thoracic surgery training curriculum. J Thorac Dis. 2023;15(2):791-8. https://doi.org/10.21037/jtd-22-598 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Supplementaryfigurelegend0630.docx Supplementaryfigure1.jpg Supplementaryfigure2.jpg Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 15 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 07 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 06 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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18:14:16","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120730,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/a20ae59ebfb0763a9369ccea.html"},{"id":96604189,"identity":"eb658852-1ea8-4275-9d0d-30d5b2f77d5c","added_by":"auto","created_at":"2025-11-24 09:13:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2722180,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative number of robotic anatomic lung resection (RALR) cases performed by each surgeon from January 2018 to December 2024. 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Change points are indicated by red dotted vertical lines.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/4c89cde3847d0fff068a27bc.jpg"},{"id":96604143,"identity":"58f77590-432c-4290-a345-dddb95e5cf08","added_by":"auto","created_at":"2025-11-24 09:12:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3202268,"visible":true,"origin":"","legend":"\u003cp\u003eUnadjusted and risk-adjusted CUSUM curves for postoperative complications. Change points are indicated by red dotted vertical lines.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/2bad281232325baa59a2b84e.jpg"},{"id":96493100,"identity":"233217de-1c02-4506-bc20-f405822a65f1","added_by":"auto","created_at":"2025-11-21 18:14:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3420376,"visible":true,"origin":"","legend":"\u003cp\u003eUnadjusted and risk-adjusted CUSUM learning curves for total lymph nodes harvested. Change points are indicated by red dotted vertical lines.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/1ca9b016427de9c5797133d4.jpg"},{"id":96493095,"identity":"1ecd459e-fe07-4544-90db-e3a1ad3db431","added_by":"auto","created_at":"2025-11-21 18:14:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3317601,"visible":true,"origin":"","legend":"\u003cp\u003eCUSUM learning curves for length of postoperative hospital stay. Change points are indicated by red dotted vertical lines.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/21863eb9fc5f2243b336442b.jpg"},{"id":99172904,"identity":"89bb1883-6f89-4541-b513-fdc111d72638","added_by":"auto","created_at":"2025-12-29 16:12:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16592486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/aea013c3-55b2-415f-8612-c2e2fa26e41b.pdf"},{"id":96493088,"identity":"736ecce8-908a-4e67-b12e-27d50a2064d2","added_by":"auto","created_at":"2025-11-21 18:14:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66270,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/c10aec90ef50abc1596433ad.docx"},{"id":96493086,"identity":"b0d0c0f7-8549-4e15-b934-2050c5eea8b8","added_by":"auto","created_at":"2025-11-21 18:14:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":361387,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigurelegend0630.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/c974957a47c3c065c9959021.docx"},{"id":96604066,"identity":"c459eb78-817c-405a-9bef-89bb8310ad0d","added_by":"auto","created_at":"2025-11-24 09:12:38","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3764257,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/706ff54911620117042da7e2.jpg"},{"id":96604093,"identity":"de94c023-75b5-4cb1-bf75-c9148d258b4b","added_by":"auto","created_at":"2025-11-24 09:12:42","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2556183,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052793/v1/83101c340e5163699e5543cc.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reshaping the Robotic Learning Curve: The Role of Prior Video-Assisted Thoracoscopic Surgery in Anatomic Lung Resection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRobotic anatomic lung resection (RALR) has become an increasingly employed approach in thoracic surgery, offering enhanced visualization, dexterity, and ergonomics compared with conventional minimally invasive techniques [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As robotic systems are gaining acceptance, understanding the learning curve associated with this technique has become critical, particularly for thoracic surgeons transitioning from video-assisted thoracoscopic surgery (VATS) to robotic-assisted thoracic surgery (RATS).\u003c/p\u003e\u003cp\u003eCompared with other robotic specialties such as urology or colorectal surgery, the learning curve in RATS has received relatively limited attention. Soomro et al. highlighted the methodological heterogeneity in robotic learning curve studies and underscored the need for standardized definitions and outcome metrics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In thoracic surgery, Wilson-Smith et al. reported in a meta-analysis of over 200 studies that technical proficiency in robotic lobectomy is typically achieved after 25\u0026ndash;40 cases, although this depends on individual training environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Some studies have proposed that prior VATS experience can shorten the robotic learning curve [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; whereas others emphasize that high-volume robotic exposure and focused case repetition may be more critical in gaining proficiency [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these insights, few studies have directly compared the robotic learning curve across surgeons with differing levels of VATS experience using multidimensional performance metrics. Furthermore, existing research often focuses on isolated outcomes, such as prolonged air leak or operative time, thereby limiting a comprehensive understanding of learning dynamics [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, this study aims to assess the learning curve of RALR in three thoracic surgeons with varying VATS backgrounds using cumulative sum (CUSUM) analysis. By examining changes in operative time, complication rates, lymph node harvest, and hospital stay, we aim to determine whether prior VATS experience or concentrated robotic exposure more strongly influences surgical proficiency.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eWe retrospectively reviewed patients with primary lung cancer who underwent RALR between January 2018 and December 2024 at a single high-volume tertiary institution. A total of 341 robotic procedures were performed in 340 patients during this period.\u003c/p\u003e\u003cp\u003e This study was approved by the Institutional Review Board of our institution (IRB approval number: [2025\u0026thinsp;\u0026minus;\u0026thinsp;0897]). The requirement for written informed consent was waived owing to the retrospective nature of the study and the use of de-identified patient data. All research procedures conformed to the principles outlined in the Declaration of Helsinki and relevant institutional guidelines.\u003c/p\u003e\u003cp\u003eTo focus the analysis on technically comparable procedures, only anatomical lung resections, specifically, lobectomies and segmentectomies, were included. Wedge resections and other non-anatomical procedures were excluded, as their relatively lower complexity and shorter operative times could confound the interpretation of the learning curve. Clinical, operative, and perioperative data were collected from institutional records and reviewed for analysis. Patients were staged according to the eighth edition of the tumor-node-metastasis (TNM) classification system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSurgeon Backgrounds and Procedural Approach\u003c/h3\u003e\n\u003cp\u003eAll surgeries were performed using the da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) by three board-certified thoracic surgeons with differing levels of prior VATS experience. Surgeon A had performed approximately 1,500 VATS anatomical resections between February 2009 and December 2018; Surgeon B had performed 350 such cases from March 2015 to December 2018; Surgeon C had performed 50 VATS cases between February and June 2022, prior to initiating robotic practice. At the initiation of robotic practice, Surgeons A, B, and C were in their postgraduate year 24, 18, and 11, respectively (corresponding to 19, 13, and 6 years after board certification in thoracic surgery in South Korea). None of the three surgeons had prior experience with robotic-assisted thoracic surgery (RATS) at other institutions.\u003c/p\u003e\u003cp\u003eSurgeons A and B initially performed robotic procedures using the da Vinci Si system and later transitioned to the X and Xi platforms as these became available. Surgeon C began robotic practice directly with the X and Xi systems and subsequently adopted the SP model for selected cases. Port configuration varied among surgeons and evolved. Surgeon A consistently employed a three-port configuration, with the camera port placed at the 6th intercostal space (ICS) along the anterior axillary line (AAL), and working ports at the 4th ICS AAL and the 8th ICS posterior axillary line (PAL). Surgeons B and C initially adopted a four-port approach. This included a utility incision at the 5th ICS AAL, a 12-mm port at the 7th ICS AAL, the camera port at the 9th ICS mid-axillary line (MAL), and an additional 12-mm port at the 11th ICS PAL for the fourth robotic arm. During the transition to single-port strategies, Surgeon C utilized both the da Vinci X/Xi platforms with single-site access and the dedicated SP model. For single-site approaches with the X/Xi system, a 5-cm incision was made at the 7th ICS AAL. When the SP platform was used, a 5-cm incision was created at the subcostal margin along the midclavicular line.\u003c/p\u003e\u003cp\u003eStandard RALR techniques were applied, including sequential division of the pulmonary vein, artery, and bronchus. Depending on fissure anatomy and hilar adhesions, either a fissure-based or hilar-first approach was used. Stapling was performed using the da Vinci SureForm system (Intuitive Surgical, Sunnyvale, CA, USA), whereas the ENDO GIA stapler (Medtronic, Minneapolis, MN, USA) was employed for procedures conducted with the SP platform. For lymph node dissection, lobectomy cases underwent systematic hilar and mediastinal dissection (stations 2R, 4R, 7, 8, 9 for right-sided resections; 5, 6, 7, 8, 9 for left-sided resections). Segmentectomy cases included hilar and intersegmental nodes, with selective mediastinal dissection depending on tumor location. Conversion to thoracotomy or the addition of auxiliary ports was permitted in cases with inadequate exposure or complications such as major vascular injury.\u003c/p\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n\u003cp\u003eFour perioperative outcome measures were assessed to evaluate the learning curve: total operative time, complication rate, number of harvested lymph nodes, and length of hospital stay. Total operative time was defined as the duration from the start of skin incision to completion of skin suturing. Console time would have provided a more precise measure of technical performance; however, these data were not consistently available across all cases. Complications were defined as any adverse event classified as Clavien\u0026ndash;Dindo grade II or higher [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, operative cadence was assessed, defined as the average number of procedures performed per month during the study period, as well as the local operative cadence (reflecting the short-term case frequency around the cutoff point) within \u0026plusmn;\u0026thinsp;5 procedures of the cutoff point.\u003c/p\u003e\u003cp\u003eTreatment approaches for stage III or stage IV non-small cell lung cancer (NSCLC) were determined by a multidisciplinary team. Surgical indications included clinical stage III NSCLC that became resectable after achieving adequate response or nodal downstaging with immunochemotherapy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In patients with oligometastatic NSCLC, surgical resection was selectively performed if patients were younger, had good performance status, and presented with a limited number of metastases (\u0026le;\u0026thinsp;3) confined to a single organ (e.g., brain, adrenal), and if complete resection of both the primary and metastatic lesions was feasible within a multimodal treatment strategy [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are reported as mean (standard deviation) or median with interquartile range, while categorical variables are presented as counts and percentages. For two-group comparisons (e.g., early vs. late phases), the Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test was applied, as appropriate. For three-group comparisons (e.g., baseline characteristics across surgeons), analysis of variance (ANOVA) or the Kruskal\u0026ndash;Wallis test was used. Categorical variables were compared using the chi-square or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e\u003cp\u003eThe learning curve threshold, referred to as the \u0026ldquo;change point,\u0026rdquo; was defined as the case number showing the greatest deviation from the zero axis on the risk-adjusted CUSUM plot. Based on the identified change points, each surgeon\u0026rsquo;s series was divided into early (pre-change point) and late (post-change point) phases.\u003c/p\u003e\u003cp\u003eTo analyze performance improvement over time, unadjusted and risk-adjusted CUSUM analyses were separately performed for each surgeon and each outcome. Unadjusted CUSUM provided a representation of case-by-case trends in performance, whereas risk-adjusted CUSUM accounted for case-mix variability to more accurately identify technical improvement. To enable risk-adjusted CUSUM analysis, multivariable regression models were developed for each surgeon to estimate the expected value of each outcome based on patient and operative characteristics. Log-transformed linear and logistic regression models are presented in the Supplementary Materials. Covariates included age, sex, smoking, tumor histology, extent of surgery, port number, pathologic stage, neoadjuvant therapy, and combined surgery. Certain categorical levels (e.g., histology III, pStage IV) were excluded from coefficient estimation owing to near-zero case counts, resulting in non-estimable values (NA) in the regression outputs.\u003c/p\u003e\u003cp\u003eMissing data were infrequent and were handled using complete-case analysis without imputation.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using R software (R Foundation for Statistical Computing, Vienna, Austria). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 341 RALRs were analyzed: 99 cases by Surgeon A, 141 by Surgeon B, and 101 by Surgeon C. The cumulative number of procedures performed by each surgeon is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Baseline patient characteristics were well balanced across the three groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Surgeon B\u0026rsquo;s patients were slightly younger on average (mean age difference\u0026thinsp;~\u0026thinsp;3 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), but no significant differences were observed in sex distribution, pulmonary function, or clinical stage. While slightly more frequent in Surgeon C\u0026rsquo;s cohort (4% vs. \u0026le; 1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), neoadjuvant therapy was infrequent overall (five patients).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA (N\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB (N\u0026thinsp;=\u0026thinsp;141)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC (N\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, y\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (42.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (48.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (30.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (64.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (55.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1 pred\u0026thinsp;\u0026lt;\u0026thinsp;60%, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDLCO pred\u0026thinsp;\u0026lt;\u0026thinsp;60%, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (82.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (78.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (82.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoadjuvant treatment, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eComorbidity denotes the number of preexisting medical conditions: 0\u0026thinsp;=\u0026thinsp;none; 1\u0026thinsp;=\u0026thinsp;one; 2\u0026thinsp;=\u0026thinsp;two or more. FEV₁ and DLCO are presented as percent predicted. Clinical stage is classified according to the 8th edition TNM system for NSCLC.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOperative characteristics and perioperative outcomes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All three surgeons performed a similar proportion of lobectomies versus segmentectomies (lobectomy rate 80\u0026ndash;89%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09), with uniformly high R0 resection rates. Port configurations differed significantly between surgeons: Surgeon A primarily used a three-port approach, Surgeon B consistently employed four ports, and Surgeon C transitioned to reduced-port strategies in later cases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall conversion rate to thoracotomy remained low across all groups (0\u0026ndash;1%). Unadjusted mean operative times varied modestly: 140\u0026ndash;150 minutes overall, with Surgeon B\u0026rsquo;s cases averaging slightly longer (p\u0026thinsp;=\u0026thinsp;0.039). Complication rates were comparable across surgeons (12\u0026ndash;13%, p\u0026thinsp;=\u0026thinsp;0.95), and mean postoperative hospital stays was 6 days in each group (p\u0026thinsp;=\u0026thinsp;0.73). Estimated blood loss was generally low across all groups (median\u0026thinsp;\u0026lt;\u0026thinsp;10 mL), and only one patient required intraoperative transfusion due to major bleeding.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOperative and pathological data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA (N\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB (N\u0026thinsp;=\u0026thinsp;141)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC (N\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperative time, min\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.2\u0026thinsp;\u0026plusmn;\u0026thinsp;34.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149.7\u0026thinsp;\u0026plusmn;\u0026thinsp;30.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.7\u0026thinsp;\u0026plusmn;\u0026thinsp;35.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined resection, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePort number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (91.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130 (92.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConversion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical extent, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (89.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegmentectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph nodes harvested, n\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResection type, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (98.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139 (98.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistology, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (85.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (88.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (86.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (71.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (67.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71 (70.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (12.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplication, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (12.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplication Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade IIIa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade IIIb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChylothorax, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocal cord palsy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReadmission, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital stay, days\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eR0: complete resection with negative margins; R1: microscopically positive margins; Histology: ADC\u0026thinsp;=\u0026thinsp;adenocarcinoma, SCC\u0026thinsp;=\u0026thinsp;squamous cell carcinoma; Pathologic stage: based on the 8th TNM classification for NSCLC; Complication grade: according to the Clavien\u0026ndash;Dindo classification.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCUSUM analysis of operative time demonstrated distinct learning curve profiles for each surgeon (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significant reductions in mean operative time were observed after the CUSUM-defined thresholds: case 54 for Surgeon A (150.1 \u0026rarr; 128.3 min, p\u0026thinsp;=\u0026thinsp;0.001), case 35 for Surgeon B (170.3 \u0026rarr; 142.9 min, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and case 34 for Surgeon C (164.9 \u0026rarr; 128.4 min, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall operative cadences were 1.38, 1.92, and 3.35 cases per month for Surgeons A, B, and C, respectively, with corresponding local cadences at the cutoff cases of 2.50, 9.30, and 7.97 cases per month.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor complication incidence, CUSUM learning curves demonstrated distinct patterns across surgeons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Change points were identified at case 48 for Surgeon A, case 57 for Surgeon B, and case 75 for Surgeon C. Surgeon A\u0026rsquo;s curve exhibited a downward inflection at the change point, corresponding to a reduction in postoperative complications during the later phase of his experience (from 20.8 to 1.9%, p\u0026thinsp;=\u0026thinsp;0.930). In contrast, the curves for Surgeons B and C showed upward inflections, suggesting increased complication rates in their late-phase cases (3.5% \u0026rarr; 8.3%, p\u0026thinsp;=\u0026thinsp;0.880; 5.3% \u0026rarr; 15.4%, p\u0026thinsp;=\u0026thinsp;0.199, respectively).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLymph node yield tended to improve over time for Surgeons B and C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The curves for Surgeons B and C show upward inflections at cases 90 and 74, respectively, reflecting increased lymph node retrieval in their late-phase cases. Surgeon B\u0026rsquo;s average yield increased significantly from 25.7 to 32.0 nodes (p\u0026thinsp;=\u0026thinsp;0.002), while that of Surgeon C increased from 24.6 to 29.2 nodes without reaching statistical significance (p\u0026thinsp;=\u0026thinsp;0.074). In contrast, surgeon A\u0026rsquo;s curve inflects downward at case 39, showing an overall decreasing trend with a secondary peak in the later phase. Accordingly, the mean number of lymph nodes retrieved declined slightly from 29.1 to 25.9 (p\u0026thinsp;=\u0026thinsp;0.144).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePostoperative hospital stays modestly increased during the later phase for all surgeons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). All three surgeons\u0026rsquo; curves exhibit upward inflections at their respective change points: case 72 for Surgeon A, 45 for Surgeon B, and 58 for Surgeon C, indicating longer postoperative stays in the late phase of their learning curves. Specifically, the mean hospital stay increased from 4.4 to 5.3 days for Surgeon A (p\u0026thinsp;=\u0026thinsp;0.015), 4.1 to 5.0 days for Surgeon B (p\u0026thinsp;=\u0026thinsp;0.001), and 4.3 to 5.0 days for Surgeon C (p\u0026thinsp;=\u0026thinsp;0.271). This observation likely reflects a trend toward more complex cases as experience accumulated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the learning curves of RALR among three surgeons with varying levels of prior VATS experience. All surgeons demonstrated reduced operative time after reaching CUSUM-defined thresholds; however, the pace of improvement varied. Surgeon A, with extensive VATS experience, showed early procedural consistency but reached proficiency more gradually. In contrast, Surgeons B and C, despite limited VATS backgrounds, achieved faster operative gains with concentrated robotic exposure. Complication rates remained similar between early and late phases. Lymph node yield improved for Surgeons B and C, whereas hospital stay modestly increased across all surgeons, possibly reflecting a shift toward more complex case selection over time.\u003c/p\u003e\u003cp\u003eThis study differs from prior investigations in several important ways. Although several earlier studies examined robotic learning curves in single-surgeon settings or focused on limited outcome measures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], we evaluated multiple performance indicators across three surgeons with varying levels of prior experience. By assessing operative time, complication rates, lymph node yield, and hospital stay together, we aimed to provide a more balanced view of robotic proficiency development. Additionally, the use of both unadjusted and risk-adjusted CUSUM analyses allowed us to account for variations in case complexity over time [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To our knowledge, this is among the first thoracic surgical studies to apply such a structured framework across multiple operators using real-world institutional data.\u003c/p\u003e\u003cp\u003eAs noted above, surgeons with limited prior VATS experience demonstrated faster improvement in operative efficiency, despite having less initial exposure to minimally invasive techniques [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although prior VATS experience likely contributes to early procedural familiarity and technical safety [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], our findings indicate that it does not guarantee accelerated proficiency or necessarily translate directly to robotic surgery. Instead, sustained and focused exposure to robotic techniques may be more instrumental in accelerating proficiency than the cumulative volume of prior thoracoscopic procedures alone(5).\u003c/p\u003e\u003cp\u003eComplication rates did not change significantly between early and late phases; however, their trajectories varied according to the surgeon. Surgeon A demonstrated a decreasing trend over time, whereas Surgeons B and C experienced modest increases despite technical improvement. This pattern likely reflects a gradual shift toward more complex case selection as surgeons gained confidence with the robotic platform. As proficiency increased, surgeons may have undertaken more challenging resections, such as larger tumors, post-neoadjuvant cases, or reduced-port procedures (See \u003cb\u003eSupplementary Figs.\u0026nbsp;1 and 2\u003c/b\u003e), which could offset the expected reductions in complication rates. These findings highlight the need to interpret perioperative outcomes within the context of evolving case complexity, rather than attributing them solely to technical skill [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Compared with published benchmarks (major complication rates\u0026thinsp;~\u0026thinsp;4\u0026ndash;9%, chylothorax\u0026thinsp;~\u0026thinsp;0.5\u0026ndash;1%, RLN palsy\u0026thinsp;\u0026lt;\u0026thinsp;1%), our rates are broadly in line. Some complication rates appear relatively elevated, but given the small number of events, firm conclusions seem to be limited [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLymph node yield improved notably over time for Surgeons B and C, while remaining stable or slightly decreasing for Surgeon A. This result may reflect differences in emphasis during the learning process; less experienced surgeons may have gradually increased the extent of lymphadenectomy as their confidence grew, whereas a more experienced surgeon may have already reached a performance plateau or tailored dissection based on case-specific factors. Similarly, postoperative hospital stay increased modestly during the later phase across all surgeons. As with complications, this trend likely reflects a transition toward more complex cases, such as higher-stage tumors or those managed with reduced-port approaches, rather than a deterioration in operative efficiency.\u003c/p\u003e\u003cp\u003eTaken together, these findings underscore the importance of structured robotic training pathways that emphasize high case volume and consistent exposure, rather than assuming an automatic translatability of VATS skills [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While prior thoracoscopic experience may offer initial procedural stability, the intensity and frequency of robotic case engagement appears to play a pivotal role in accelerating proficiency and broadening surgical capability. Tailoring training curricula to balance safety, exposure, and gradual complexity may help optimize the learning process and ensure safe integration of robotic platforms into thoracic surgical practice [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, its retrospective, single-institution design introduces selection bias and limits generalizability. Surgeons selected their cases, and less complex procedures may have been preferentially performed during the early phase. Despite employing risk-adjusted CUSUM analyses to account for measurable confounders, residual bias from unmeasured variables cannot be excluded. Second, methodological and technical variations among surgeons complicate direct comparisons. Surgeon C introduced reduced-port techniques, such as dual-port and single-port approaches, later in their robotic experience, resulting in distinct learning phases that were not encountered by Surgeons A or B. This observation is reflected in the multi-peaked operative time CUSUM curve observed for Surgeon C. Heterogeneity in port configuration between surgeons may have influenced operative time through differences in bedside assistant involvement, introducing residual bias in inter-surgeon comparisons. Additionally, Surgeon B frequently delegated console time to assistants for proctoring purposes, which may have influenced operative metrics and further limited inter-surgeon comparability. Third, although each surgeon contributed approximately 100 cases, the sample size may be underpowered to detect subtle differences in infrequent outcomes, such as complication rates. Moreover, all data were derived from a high-volume tertiary center with experienced surgical teams and institutional support for robotic programs; thus, the findings may not fully translate to smaller centers or institutions with differing resources. Finally, our analysis focused on short-term perioperative outcomes. While oncologic data were collected, limited follow-up duration precluded definitive conclusions regarding long-term endpoints such as recurrence or survival.\u003c/p\u003e\u003cp\u003eIn conclusion, this study demonstrates that the learning curve for RALR is shaped by prior VATS experience and, to a greater extent, by the intensity and continuity of robotic case exposure. While experienced surgeons may benefit from early procedural stability, consistent engagement with the robotic platform appears to be essential for achieving technical proficiency and broadening surgical scope. These findings may inform the development of future training curricula and credentialing standards in robotic thoracic surgery.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRALR: robotic anatomic lung resection\u003c/p\u003e\n\u003cp\u003eVATS: video-assisted thoracoscopic surgery\u003c/p\u003e\n\u003cp\u003eRATS: robotic-assisted thoracic surgery\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCUSUM: cumulative sum\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTNM: tumor-node-metastasis\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical registration number\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConflict of interest statement\u003c/h2\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Donghee Kim and Jae Kwang Yun. The first draft of the manuscript was written by Donghee Kim and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.co.kr) for English language editing.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCerfolio RJ, Bryant AS, Skylizard L, Minnich DJ\u003cstrong\u003e. \u003c/strong\u003eInitial consecutive experience of completely portal robotic pulmonary resection with 4 arms. 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Surgery. 2019;165(2):450-4. https://doi.org/10.1016/j.surg.2018.06.011\u003c/li\u003e\n\u003cli\u003eSu L, Ho H, Stock CT, Quadri SM, Williamson C, Servais EL\u003cstrong\u003e. \u003c/strong\u003eSurgeon Experience Is Associated With Prolonged Air Leak After Robotic-assisted Pulmonary Lobectomy. Ann Thorac Surg. 2022;114(2):434-41. https://doi.org/10.1016/j.athoracsur.2021.07.033\u003c/li\u003e\n\u003cli\u003eGoldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al.The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016;11(1):39-51. https://doi.org/10.1016/j.jtho.2015.09.009\u003c/li\u003e\n\u003cli\u003eDindo D, Demartines N, Clavien PA\u003cstrong\u003e. \u003c/strong\u003eClassification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240(2):205-13. https://doi.org/10.1097/01.sla.0000133083.54934.ae\u003c/li\u003e\n\u003cli\u003eVerma S, Breadner D, Mittal A, Palma DA, Nayak R, Raphael J, et al.An Updated Review of Management of Resectable Stage III NSCLC in the Era of Neoadjuvant Immunotherapy. Cancers (Basel). 2024;16(7). https://doi.org/10.3390/cancers16071302\u003c/li\u003e\n\u003cli\u003eWang M, Wang X, Yang R, Geng M, Zhang S, Yang Z, et al.Conversion Surgery for Initially Unresectable Stage Ⅲ Nonsmall Cell Lung Cancer After Induction Treatment of Immunochemotherapy: A Multicenter Study. Clin Lung Cancer. 2025;26(3):e131-e40.e1. https://doi.org/10.1016/j.cllc.2024.11.005\u003c/li\u003e\n\u003cli\u003eCou\u0026ntilde;ago F, Luna J, Guerrero LL, Vaquero B, Guill\u0026eacute;n-Sacoto MC, Gonz\u0026aacute;lez-Merino T, et al.Management of oligometastatic non-small cell lung cancer patients: Current controversies and future directions. World J Clin Oncol. 2019;10(10):318-39. https://doi.org/10.5306/wjco.v10.i10.318\u003c/li\u003e\n\u003cli\u003eChen YH, Ho UC, Kuo LT\u003cstrong\u003e. \u003c/strong\u003eOligometastatic Disease in Non-Small-Cell Lung Cancer: An Update. Cancers (Basel). 2022;14(5). https://doi.org/10.3390/cancers14051350\u003c/li\u003e\n\u003cli\u003eYoo S, Cho WC, Lee GD, Choi S, Kim HR, Kim Y-H, et al.Long-term Surgical Outcomes in Oligometastatic Non-small Cell Lung Cancer: A Single-Center Study. Journal of Chest Surgery. 2023;56(1):25-32. https://doi.org/10.5090/jcs.22.101\u003c/li\u003e\n\u003cli\u003eGomez-Hernandez MT, Fuentes MG, Novoa NM, Rodriguez I, Varela G, Jimenez MF\u003cstrong\u003e. \u003c/strong\u003eThe robotic surgery learning curve of a surgeon experienced in video-assisted thoracoscopic surgery compared with his own video-assisted thoracoscopic surgery learning curve for anatomical lung resections. Eur J Cardiothorac Surg. 2022;61(2):289-96. https://doi.org/10.1093/ejcts/ezab385\u003c/li\u003e\n\u003cli\u003eFortea-Sanchis C, Escrig-Sos J\u003cstrong\u003e. \u003c/strong\u003eQuality control techniques in surgery: Application of cumulative sum (CUSUM) charts. Cir Esp (Engl Ed). 2019;97(2):65-70. https://doi.org/10.1016/j.cireng.2019.01.010\u003c/li\u003e\n\u003cli\u003eKuo SW, Huang PM, Lin MW, Chen KC, Lee JM\u003cstrong\u003e. \u003c/strong\u003eRobot-assisted thoracic surgery for complex procedures. J Thorac Dis. 2017;9(9):3105-13. https://doi.org/10.21037/jtd.2017.08.11\u003c/li\u003e\n\u003cli\u003eWilson-Smith AR, Wilson-Smith CJ, Anning N, Muston B, Eranki A, Williams ML, et al.The perioperative outcomes of uniportal robotic-assisted thoracic surgeries-a systematic review and meta-analysis of surgical cohort studies and case reports. Ann Cardiothorac Surg. 2023;12(2):73-81. https://doi.org/10.21037/acs-2023-urats-37\u003c/li\u003e\n\u003cli\u003eCao C, Louie BE, Melfi F, Veronesi G, Razzak R, Romano G, et al.Outcomes of major complications after robotic anatomic pulmonary resection. J Thorac Cardiovasc Surg. 2020;159(2):681-6. https://doi.org/10.1016/j.jtcvs.2019.08.057\u003c/li\u003e\n\u003cli\u003eBottet B, Seguin-Givelet A, Fourdrain A, Sarsam M, Boddaert G, Boulate D, et al.Multicenter evaluation of patient safety incidents in lung surgery: The Epithor Patient Safety Incident study. J Thorac Cardiovasc Surg. 2025;169(5):1356-66.e4. https://doi.org/10.1016/j.jtcvs.2024.10.054\u003c/li\u003e\n\u003cli\u003eShahin GMM, Brandon Bravo Bruinsma GJ, Stamenkovic S, Cuesta MA\u003cstrong\u003e. \u003c/strong\u003eTraining in robotic thoracic surgery-the European way. Ann Cardiothorac Surg. 2019;8(2):202-9. https://doi.org/10.21037/acs.2018.11.06\u003c/li\u003e\n\u003cli\u003eKingma BF, Hadzijusufovic E, Van der Sluis PC, Bano E, Lang H, Ruurda JP, et al.A structured training pathway to implement robot-assisted minimally invasive esophagectomy: the learning curve results from a high-volume center. Diseases of the Esophagus. 2020;33(Supplement_2). https://doi.org/10.1093/dote/doaa047\u003c/li\u003e\n\u003cli\u003eKim SS, Schumacher L, Cooke DT, Servais E, Rice D, Sarkaria I, et al.The Society of Thoracic Surgeons Expert Consensus Statements on a Framework for a Standardized National Robotic Curriculum for Thoracic Surgery Trainees. Ann Thorac Surg. 2025;119(4):719-32. https://doi.org/10.1016/j.athoracsur.2024.12.003\u003c/li\u003e\n\u003cli\u003eAl Zaidi M, Wright GM, Yasufuku K\u003cstrong\u003e. \u003c/strong\u003eSuggested robotic-assisted thoracic surgery training curriculum. J Thorac Dis. 2023;15(2):791-8. https://doi.org/10.21037/jtd-22-598\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Robotic-assisted thoracoscopic surgery, Lung cancer resection, Surgeons' learning curve, Prior experience, CUSUM analysis","lastPublishedDoi":"10.21203/rs.3.rs-8052793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8052793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThe adoption of robotic-assisted thoracoscopic surgery for lung cancer has steadily increased; however, the learning curve and influence of prior video-assisted thoracoscopic surgery (VATS) experience remain underexplored. This study investigates the impact of prior VATS experience on the learning trajectory of robotic anatomic lung resection.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed 341 robotic anatomic lung resection procedures performed between January 2018 and December 2024 at a single tertiary referral center. Three thoracic surgeons with varying VATS experience\u0026mdash;A (1,500 cases), B (350 cases), and C (50 cases)\u0026mdash;were included. Learning curves were assessed using cumulative sum analysis for operative time, complication rates, lymph node yield, and postoperative hospital stay. Change points were identified, and early versus late-phase outcomes were compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAll surgeons demonstrated significant reductions in operative time after reaching their respective cumulative sum thresholds (cases 54, 35, and 34 for Surgeons A, B, and C, respectively). While Surgeon A exhibited early procedural stability, Surgeons B and C showed more rapid improvement with experience. Lymph node yield increased significantly for Surgeon B (p\u0026thinsp;=\u0026thinsp;0.002) and marginally for Surgeon C (p\u0026thinsp;=\u0026thinsp;0.074). Complication rates and hospital stay modestly increased in later phases, likely reflecting greater case complexity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAlthough prior VATS experience supports initial operative consistency, it does not necessarily shorten the robotic learning curve. Instead, case volume and intensity of robotic exposure appear more critical. These findings underscore the need for structured training programs emphasizing high case density and progressive complexity to optimize robotic surgical proficiency.\u003c/p\u003e","manuscriptTitle":"Reshaping the Robotic Learning Curve: The Role of Prior Video-Assisted Thoracoscopic Surgery in Anatomic Lung Resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 18:14:10","doi":"10.21203/rs.3.rs-8052793/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T21:48:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T15:59:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T16:14:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252221371599802780149795950222993736836","date":"2025-11-16T15:04:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156194771563531927042762890757563741619","date":"2025-11-15T09:00:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211313866373854110230450743226712247567","date":"2025-11-13T19:29:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291944192274981977037079772043393302086","date":"2025-11-12T08:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145823481991703413086917860587103749568","date":"2025-11-11T16:35:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286237407947294114553372981243344705270","date":"2025-11-11T15:00:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T14:55:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T20:34:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T04:25:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Robotic Surgery","date":"2025-11-07T03:59:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d42c30ec-3680-48a6-8564-a6e594455d45","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-8052793","link":"https://doi.org/10.1007/s11701-025-03054-1","journal":{"identity":"journal-of-robotic-surgery","isVorOnly":false,"title":"Journal of Robotic Surgery"},"publishedOn":"2025-12-22 15:58:02","publishedOnDateReadable":"December 22nd, 2025"},"versionCreatedAt":"2025-11-21 18:14:10","video":"","vorDoi":"10.1007/s11701-025-03054-1","vorDoiUrl":"https://doi.org/10.1007/s11701-025-03054-1","workflowStages":[]},"version":"v1","identity":"rs-8052793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8052793","identity":"rs-8052793","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}