Impact of video-assisted thoracoscopic and open surgical experience on the learning curve of robotic lobectomy

Introduction

Thoracic surgery has undergone significant advances with the introduction of minimally invasive techniques for anatomical oncologic lung resections. Since the 1990s, VATS has become the preferred approach for early-stage non-small cell lung cancer (1). However, it was not until the recent publication of the VIOLET randomized controlled trial that the superiority of VATS over open thoracotomy (THO) was demonstrated (2).

While video-assisted thoracic surgery (VATS) has several advantages compared with open surgery, such as smaller incisions, less pain, shorter hospital stays, quicker recoveries, and a faster return to routine daily activity, it also has some limitations, including the lack of articulation of the instrument, two-dimensional visualization, and counterintuitive movement of the instrument. Robotic-assisted thoracic surgery (RATS) has emerged as an evolving technique that helps overcome these limitations. High-definition three-dimensional stereo visualization, improved ergonomics, and tremor suppression are among the key advantages of RATS (3-5). Additionally, the articulating robotic instruments allow for better manoeuvrability in deep and narrow spaces, facilitating complex dissections such as hilar and mediastinal lymph node retrieval when compared to VATS (6-9).

Since Melfi et al. first applied robotic-assisted surgery to thoracic procedures in 2002, several studies have demonstrated its feasibility and safety in complex thoracic surgeries (7,8,10-13). More recently, comparative analyses between RATS and VATS have suggested potential benefits of the robotic approach (14,15). These include reduced blood loss, lower conversion rates, a higher number of harvested lymph nodes. Such findings are reflected in the most important randomized studies, such as the RVlob Trial (16), the ROMAN Study (17), the BRAVO Trial (18), and the RAVAL Trial (19). Reaching similar conclusions, we recently published shorter postoperative chest tube duration, and reduced hospital stay during the learning curve period (20).

Despite these reported advantages, the implementation of RATS remains limited across Europe. In Spain, for example, RATS is not yet available in all thoracic surgery departments, partly due to concerns regarding the learning curve and the ongoing assessment of its clinical and economic impact (21). As the demand for RATS increases, structured and standardized training programs become crucial to ensuring optimal patient outcomes. Consequently, continuous assessment of these programs is essential, often utilizing learning curves as a means of evaluation (22,23).

One of the key determinants for the widespread adoption of RATS is the learning curve required to develop proficiency in its technical aspects, particularly the role of prior experience in minimally invasive surgery (MIS). Although it is generally assumed that prior experience shortens the learning curve, to our knowledge, few studies have compared surgeons with and without VATS experience using adjusted CUSUM learning curves.

This study aims to evaluate, in our population, the impact of prior experience in VATS on the robotic assisted lung resections learning curve. We present this article in accordance with the TREND reporting checklist (available at https://ccts.amegroups.com/article/view/10.21037/ccts-25-35/rc).

Methods

Study design and setting

This prospective, multicentre, analytical study was conducted at two high-volume thoracic surgery institutions. The study included patients undergoing robotic-assisted pulmonary lobectomy performed by two different surgeons between January 2021 and March 2023.

One surgeon, from the Hospital Clínic of Barcelona, had prior experience in VATS, and patients operated on by this surgeon were included in the VATS to RATS group. The second surgeon, from the Hospital Universitari Parc Taulí of Sabadell, had no prior VATS experience and transitioned directly from open surgery to a robotic approach; patients treated by this surgeon were included in the THO to RATS group.

Patient selection and data collection

All patients included were 18 years or older and underwent non-extended elective anatomical lung resections (lobectomy), following standardized selection criteria from the American College of Chest Physicians evidence-based clinical practice guidelines (24). In our study, the preoperative physiologic assessment began with a cardiovascular evaluation and spirometry to measure the forced expiratory volume in one second (FEV1) and diffusing capacity for carbon monoxide (DLCO), followed by the calculation of predicted postoperative (PPO) lung functions. Patients with both %PPO FEV1 and %PPO DLCO values above 60% were considered at low risk for anatomic lung resection, without further tests. A multidisciplinary thoracic tumor board evaluated all patients before surgery. Selection criteria were consistent with those applied to both VATS and RATS in Hospital Cinic of Barcelona and Hospital Parc Taulí of Sabadell. A minimally invasive approach was considered for patients with tumors up to clinical stage IIB according to the 8th edition of the tumor-node-metastasis (TNM) classification.

Demographic and clinical data were collected for both cohorts. Patient characteristics, including age, gender, comorbidities, functional status, tumor size, induction therapy, and centrality [defined as a tumor growing in the inner third of the thorax on a computed tomography (CT) scan] (25), were recorded preoperatively.

Surgical technique

All robotic lobectomies were performed using the Da Vinci^TM Xi surgical system (Intuitive, Sunnyvale, CA, USA). Both groups followed the same anesthetic protocol, including single-lung ventilation with a double-lumen endotracheal tube. A 30° optic was employed for every case, with patients positioned in lateral decubitus and slight thoracic flexion. Port placement was standardized: all robotic and assistant ports were located in the eighth intercostal space, except for the anterior port, positioned in the sixth intercostal space. Low-pressure CO₂ insufflation (5 mmHg, flow 5 L/min) was applied to optimize exposure throughout the procedure. In all cases, the stapler (surgical mechanical suture) was operated by either the console surgeon or the assistant.

In all cases, all resections were anatomical and met the criteria for a complete resection, as described by Ramón Rami-Porta in 2005 (26). Achieving a complete resection necessitates fulfilling several criteria: ensuring clear resection margins confirmed under microscopic examination, performing systematic nodal dissection or lobe-specific systematic nodal dissection, the absence of an extracapsular nodal extension of the tumor, and confirming that the negativity of the highest mediastinal node is removed.

Surgical and postoperative variables

Intraoperative parameter “console time” (defined as duration of the surgery in minutes, from the moment the console surgeon takes control of the arms to the extraction of the lobe), was documented as the primary outcome. Postoperative outcomes, considered secondary endpoints, included the incidence of complications, day of chest tube removal, hospital discharge, 30-day readmission, and 90-day mortality. The days of chest tube removal and hospital discharge were used to calculate the total duration of chest tube drainage and the length of hospital stay (LOS).

Chest tube and discharge

In both groups, pleural drainage removal was assessed using identical criteria; specifically, a threshold of less than 400 mL of fluid output over 24 hours, absence of air leak, and confirmation of lung expansion on chest X-ray, all evaluated by the operating surgeon. Prior to hospital discharge, a chest radiograph was performed in every case to ensure proper lung reexpansion.

Surgeons’ expertise and training

The VATS to RATS group surgeon completed a 5-year national thoracic surgery training program in Spain and an additional 16-month fellowship in minimal invasive surgery. Before initiating VATS anatomical lung resections as the primary surgeon, more than 100 low-complexity thoracoscopic procedures were performed. Before performing RATS lobectomies, the surgeon had already completed 100 lobectomies via VATS.

The THO to RATS group surgeon also completed a 5-year national thoracic surgery training program in Spain and had extensive experience in lung resections via THO. However, only five VATS lobectomies were performed before transitioning to RATS. The surgeon conducted a few minor robotic-assisted lung resections before performing RATS major anatomical lung resections.

To initiate robotic-assisted surgery, both surgeons obtained Intuitive Surgical certification, completed online Da Vinci technology modules, undergone more than 20 hours of simulator training, and assisted to a 2-day hands-on console training course. First initial robotic procedures were conducted under the supervision of a proctor using a second console.

Statistical analysis

Comparisons between the two groups were performed for sociodemographic characteristics and key surgical outcomes, including operative time, duration of pleural drainage, hospital stay, and postoperative complications. The sample size was justified based on previously published studies cited in the bibliography, which report comparable numbers of cases required to complete the learning curve for robotic-assisted lobectomy (27-29). Categorical variables were informed as frequency (percentages), and analyzed using the Chi-squared test or Fisher’s exact test, as appropriate. Continuous data were shown as mean ± standard deviation (SD) when normally distributed, and nonparametric data were presented as median [interquartile range (IQR)]. Means of two continuous variables were compared by independent samples Student’s t-test when normally distributed or by Mann-Whitney U test when not normally. Statistical significance was set at P<0.05, and all P values presented are two-tailed.

Cumulative sum (CUSUM) learning curve analysis

To evaluate the learning process of robotic-assisted lobectomy for each surgeon, a CUSUM analysis was performed. The CUSUM method allows for the graphical representation of procedural performance over time, identifying trends, learning phases, and plateaus in skill acquisition.

The point between the “plateau” and “downslope” of the parabolic curve was considered the point where the expertise has been reached. The primary variable analyzed using CUSUM was operative time, as it is a reliable marker of technical proficiency.

Adjustment of CUSUM curves

Also, adjusted CUMSUM curves were performed. This method consisted of adjusting a multivariable linear regression for the operative time, with selected covariables. The full model included all relevant variables (i.e., clinically relevant or the ones with P<0.20 in univariable tests with console time), then a backward stepwise variable selection method was applied to refine the model by systematically eliminating non-significant variables, with a stopping criterion based on Akaike information criterion (AIC). In our study, the variables finally selected for curve adjustment using this method were tumour size, DLCO, centrality, and lymph node involvement (pN). Model residuals were inspected for normality (Shapiro-Wilk test and Q-Q plot) and homoscedasticity (Breusch-Pagan test). The adjusted CUSUM for each surgeon was computed as the CUSUM of the centered residuals, ordered sequentially by case number.

To identify the inflection point (“learning completion”), a segmented linear regression (piecewise model) was fitted to the adjusted CUSUM curve. Since the early cases showed only random variability without systematic trend, models allowing two breakpoints were explored, and the second (downward) inflection was interpreted as completion of the learning phase.

The 95% confidence intervals (95% CIs) for each breakpoint were obtained by nonparametric bootstrap with 2,000 resamples, recalculating the segmented regression for each replicate.

All statistical analyses were performed using R software 4.3.0 version (R Foundation for Statistical Computing, Vienna, Austria).

Ethical consideration

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and approved by the Ethics Committee of Hospital Clinic Barcelona (Reg. HCB/2022/0201 V3 on 19 April 2021), and informed consent was obtained from all individual participants.

Results

A total of 60 patients who underwent robotic-assisted lobectomy during the study period were included in the analysis. Patients were categorized into two groups based on their surgeon’s prior experience: the VATS to RATS group (n=30), consisting of patients operated on by a surgeon with previous experience in VATS, and the THO to RATS group (n=30), including those treated by a surgeon transitioning directly from open THO to robotic-assisted surgery. No significant differences were observed in demographic or preoperative clinical characteristics between both cohorts (Table 1).

Surgical outcomes and learning curve

The mean operative time was 162.5±47.5 minutes in the VATS to RATS group and 159.4±36.3 minutes in the THO to RATS group, with no statistical difference (P=0.78).

The median duration of pleural drainage showed no significant difference between groups (P=0.77). In the VATS to RATS group, the median pleural drainage duration was 3 days (IQR, 2–5), while in the THO to RATS group, it was also 3 days (IQR, 2–4). The mean LOS was identical in both groups, averaging 4 days, with a median IQR of 3–6 days in the VATS to RATS group compared to 3–7 days in the THO to RATS group, and the difference did not reach statistical significance (P=0.06) (Table 2). The incidence of postoperative complications was similar between both groups (P=0.50). The VATS to RATS group had four cases of readmission, whereas the THO to RATS group had no readmissions (P=0.11). No 90-day mortality was observed in either group.

Learning curve CUSUM analysis demonstrated that the learning phase for RATS lobectomy was completed after 22.92 procedures [95% confidence interval (CI): 20.38–25.45] in the VATS to RATS group, whereas the THO to RATS group reached competency after 20 procedures (95% CI: 17.88–22.12) (Figure 1).

Figure 1 CUSUM curve, for the console time of the lobectomies. CX1: VATS to RATS group. CX2: THO to RATS group. CUSUM, cumulative sum; RATS, robotic-assisted thoracic surgery; THO, thoracotomy; VATS, video-assisted thoracic surgery.

After adjustment for tumor size, pN status, and other covariates, the regression models showed no significant heteroscedasticity (Breusch-Pagan P>0.10) and approximately normal residual distributions (Shapiro-Wilk P>0.05).

The adjusted CUSUM curves demonstrated a three-phase pattern for both groups, with an initial upward followed by a plateau and finally a sustained downward trend indicating performance improvement.

The segmented regression identified the learning-completion point at case 20.4 (95% CI: 17.6–23.2) for VATS to RATS group and case 21.6 (95% CI: 19.8–23.4) for THO to RATS group (Figures 2,3). The mean difference was 2.19 cases (bootstrap 95% CI: −0.12 to 9.3), indicating no statistically significant between-surgeon difference in learning-completion point.

Figure 2 Adjusted CUSUM curve, for the console time of the lobectomies in the VATS to RATS group (21 interventions). CUSUM, cumulative sum; RATS, robotic-assisted thoracic surgery; VATS, video-assisted thoracic surgery.

Figure 3 Adjusted CUSUM curve, for the console time of the lobectomies in the THO to RATS group (23 interventions). CUSUM, cumulative sum; RATS, robotic-assisted thoracic surgery; THO, thoracotomy.

Discussion

In order to evaluate the impact of previous MIS experience on the robotic lobectomy learning curve, we evaluated the first 30 robotic anatomical lung resections performed by surgeons with and without previous experience in minimal invasive thoracic surgery (VATS). We compared the learning curves in terms of operating console time as well as clinical outcomes. No significant differences were detected among them.

Learning curves measured through standardized methods are a useful tool to assess new surgical techniques. Different approaches and parameters are used to do that. Multiple studies over the past decade have explored the learning curve for robotic-assisted lobectomy (RATS), with findings largely centered on operative time, surgical outcomes, and surgeon proficiency determining a minimum number of lobectomies required to achieve technical expertise. In our study the use of console time was used to determine the learning curve.

In this regard, many reports use operating time; Veronesi et al. [2011] estimated that competency in robotic lobectomy is achieved after 20 cases (27), and Toker et al. [2016] determined that proficiency was attained earlier only after 14 cases (28). Other studies added different variables; Lee et al. introduced lymph node dissection proficiency (29). This study reached proficiency after performing 20 lobectomies.

Likewise, Baldonado et al. [2019] demonstrated improvements in operative efficiency, blood loss, and hospital stay with increasing case volume in a retrospective review of 272 robotic lobectomies performed between 2011 and 2017 (30). Karnik et al. [2020] reported increasing surgical complexity without negatively impacting postoperative morbidity and mortality in their first 79 robotic cases (31).

Other investigations refined learning curve estimates using advanced statistical methods. Song et al. [2019] used the CUSUM method to define proficiency thresholds at 20, 34, and 32 cases for docking time, console time, and total procedure duration, respectively (32). Arnold et al. identified a primary learning curve at 22 cases, with mastery achieved after 63 procedures (23). Yang et al. [2021] suggested a three-phase learning curve, with initial proficiency reached after 10 cases but complete mastery requiring 56 procedures, based on a CUSUM analysis of 100 cases (33).

A novel approach by the European Institute of Oncology [2023] assessed the learning curve using autonomic nervous system responses, such as heart and respiratory rate monitoring of the operating surgeon. Their findings suggested that confidence, technical proficiency, and oncologic safety were attained between 20 and 30 cases without compromising efficiency (34).

As demonstrated across the literature, learning curve estimations for RATS have evolved, with increasing use of CUSUM analyses to detect procedural efficiency trends. In line with these findings, our own series identified a learning curve completion at 23 cases based on operative time, further supporting the notion that approximately 20–30 procedures are necessary for consistent surgical proficiency in robotic lobectomy (13).

When comparing RATS learning curves to the gold standard oncologic minimal invasive surgery approach (VATS) some differences are revealed. Kanzaki et al. [2021] established a 15-case threshold for operative time stabilization, noting longer operative times for RATS compared to VATS but satisfactory perioperative outcomes (35). Ahn et al. [2019] found that robotic lobectomy was associated with longer operative times and greater intraoperative blood loss than VATS, despite yielding superior lymph node retrieval (36) suggesting more difficult learning process.

On the other hand, some studies pointed out that RATS learning curve could be easier than VATS. Andersson et al. [2021] found similar learning curve patterns for both techniques, although slightly shorter for robotic lobectomy (45 vs. 53 cases) (37). Fukui et al. [2021] estimated a 28-case learning curve for RATS and 35 cases for VATS (38).

Others find no differences among RATS and VATS surgeries. Gómez-Hernández et al. [2022] reported comparable operative times and perioperative outcomes between RATS and VATS, with surgical proficiency achieved after 32 and 34 cases, respectively (39).

Many of these studies compared the initial phases of robotic thoracic surgery to an established VATS program. In our previously published study, we compared the initial learning curves of the same surgeon, aiming to evaluate the learning process of both techniques, showing similar learning curves in both periods with better clinical results (days of drainage and LOS) during the RATS learning curve (20).

The role of VATS in the learning curve for RATS remains a topic of debate. This raises the question of whether the robotic learning curve should be considered independently, without assuming that VATS is a mandatory step in the training of modern thoracic surgeons.

One may think that, several factors influence the adaptation process to RATS technology, such as prior experience with minimally invasive techniques, the surgeon’s ability to manipulate robotic instruments, and the learning curve of the surgical team as a whole. Understanding these variables is essential to optimize training pathways and ensuring the safe implementation of the technique in clinical practice.

In these directions, Baldonado et al. (30) analyzed RATS learning curves in experienced VATS surgeons and concluded that there is no well-defined learning curve for robotic lobectomy in this population. Additionally, Gallagher et al. (40) evaluated thoracic surgeons proficient in open lobectomy transitioning to RATS and found that conversion rates significantly decreased after the first 40 cases, while operative times approached those of THO after 60 cases, suggesting that the learning curve for robotic lobectomy may be influenced by the surgeon’s baseline surgical technique rather than VATS experience alone.

Oh et al. [2013] confirmed the feasibility of transitioning from open lobectomy to RATS with excellent postoperative outcomes, positioning robotic lobectomy as a viable alternative to VATS, though without defining a specific learning curve threshold (41). Lee et al. [2014] compared established VATS surgeons performing their first 35 RATS lobectomies and found no significant clinical advantage in transitioning to the robotic approach, and the learning curve for upper lobectomies was steeper than for lower lobectomies (42).

In our study, we observed that surgeons with previous VATS experience (VATS to RATS group) reached technical proficiency slightly earlier than those without this background (THO to RATS group).

While some studies suggest that prior experience in VATS may facilitate the transition to robotic surgery by providing a foundation in minimally invasive techniques, our findings indicate that it is not an essential prerequisite. Our results suggest that completing the VATS learning curve may not be a necessary prerequisite for transitioning to RATS, potentially broadening access to minimally invasive approaches for a greater number of patients. However, this finding has significant implications for the training of thoracic surgery residents. If VATS is no longer considered a mandatory intermediate step, to what extent should RATS training be expanded within thoracic surgery residency programs? Should we rethink surgical education structures to ensure that residents gain proficiency in robotic-assisted procedures at earlier stages of training? These considerations highlight the need to adapt residency curricula to the evolving role of robotic surgery and its impact on modern surgical practice.

Limitations

This study has several limitations that should be acknowledged. First, the sample size is relatively small (60 patients), although it was sufficient to complete the learning curves as determined by the CUSUM methodology. A larger cohort could provide a more robust analysis of outcomes and potential differences between groups. Second, this is not a randomized study, which may introduce selection bias and limit the generalizability of the findings. Additionally, the study focuses on short-term perioperative outcomes, and long-term follow-up data on oncologic efficacy and functional recovery were not analyzed. The results are based on selected cases used to initiate a robotic surgery program, and therefore represent procedures considered to be predictably less complex. Finally, the influence of external factors, such as differences in robotic system familiarity among surgical teams or institutional variations in perioperative care, could not be fully controlled. Future studies with larger, randomized cohorts and extended follow-up are necessary to validate these findings.

Conclusions

Based on the results observed in this study, previous experience in VATS was not statistically associated with major differences in the learning curve for robotic-assisted lobectomy. Both groups demonstrated comparable performance trends throughout the learning process, suggesting that prior VATS experience may not materially influence the acquisition of proficiency, although institutional and individual factors could have influenced the observed patterns.

Regardless of prior minimal invasive surgical background, competency was achieved within a similar number of procedures, with similar complication rate and length of stay. All that suggests that transitioning directly from open surgery to RATS can be accomplished with outcomes comparable to those of surgeons with prior VATS experience.

These findings reinforce the notion that robotic-assisted surgery provides an intuitive platform that facilitates the adaptation of surgeons with different backgrounds. However, further studies with larger cohorts are necessary to better define the impact of prior experience on long-term surgical performance and patient outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TREND reporting checklist. Available at https://ccts.amegroups.com/article/view/10.21037/ccts-25-35/rc

Data Sharing Statement: Available at https://ccts.amegroups.com/article/view/10.21037/ccts-25-35/dss

Peer Review File: Available at https://ccts.amegroups.com/article/view/10.21037/ccts-25-35/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://ccts.amegroups.com/article/view/10.21037/ccts-25-35/coif). L.M. serves as an unpaid editorial board member of Current Challenges in Thoracic Surgery from March 2025 to February 2027. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and approved by the Ethics Committee of Hospital Clinic Barcelona (Reg. HCB/2022/0201 V3 on 19 April 2021), and informed consent was obtained from all individual participants.

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

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