The relationship of body mass index and chest wall thickness to camera cleaning for robotic lung resection

Introduction

Lung cancer remains the most lethal solid organ malignancy in the United States (1). The utilization of the robotic platform for lung resection has increased over recent decades. According to one study, robotic pulmonary lobectomy increased by 243% from 2013–2018 (2). Concurrently, obesity rates continue to rise, making lung resection in the obese population more common. Due to chest wall rigidity and thickness, patients with elevated body mass index (BMI) pose unique challenges for thoracic surgeons including reduced range of motion, limited instrument depth, and poorer visualization. Though clinical outcomes of obesity in robotic surgery have been studied (3-10), there is limited objective data on the impact of obesity on robotic thoracic surgery specific to these proposed challenges (e.g., poor visualization).

In our study, we aim to evaluate the relationship of BMI and chest wall thickness (CWT) to the technical aspects of performing robotic lung resection, specifically, the rate of robotic camera cleaning and operative time. We hypothesized that as BMI increases so does CWT and with a larger CWT there is more tissue to bleed onto the camera mandating camera clean. We present this article in accordance with the STROBE reporting checklist (available at https://vats.amegroups.com/article/view/10.21037/vats-2026-1-0006/rc).

Methods

Eligibility criteria

A retrospective cohort study was conducted for patients who underwent lung resection for lung cancer with a single surgeon (O.T.O.) at Thomas Jefferson University Hospital from 2020–2025 and had available robotic surgery data for review in the Intuitive^® platform. We included all patients who underwent robotic lobectomy and segmentectomy and excluded patients who underwent robotic wedge resection alone. A flow diagram is provided in Figure S1 for case inclusion. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was deemed exempt from Institutional Review Board approval at Thomas Jefferson University Hospital and individual consent for this retrospective analysis was waived.

Data collection

For each patient, demographic information and BMI were collected. A single measurement of CWT (distance from skin to the most internal aspect of rib space) was obtained from axial sections from the most recent pre-operative computed tomography (CT)- or positron emission tomography (PET)-scan at the standard point of access for the robotic camera port (8^th intercostal space; level of xiphoid process). Measurements were evenly divided between two researchers with a calculated inter-rater reliability of 90% (within 2.5 mm). For patients with high variability of CWT at the suspected camera port location, an average of three measurements was obtained. Through automated data collection in the Intuitive^® robotics platform, the frequency of camera cleaning per hour during each case was recorded. A sample of 10 available operative videos were simultaneously reviewed by three researchers to confirm accuracy of the automated camera cleaning count. There were no discrepancies between automatic and manual camera cleaning counts.

Statistical analysis

We performed simple linear regression to analyze the correlation of BMI and CWT as independent variables to the rate of camera cleaning and operative time as dependent variables. Simple linear regression was used to analyze the correlation of camera cleaning rates to operative time. The Pearson correlation coefficient was used for all correlation analyses with an alpha level of 0.05 for statistical significance. For regression analyses, additional analyses of residuals confirmed assumptions of linearity. To evaluate confounding factors, we performed additional confirmatory analyses in which patients who received neoadjuvant chemo/immunotherapy were excluded. The results reported in the discussion reflect data from the entire cohort of patients, including those who received neoadjuvant chemo/immunotherapy. All continuous data was reported using the mean and standard deviation (SD) while categorical data was reported using counts and proportions. Prism 10.01 (GraphPad Software, San Diego, CA, USA) and Microsoft Excel Version 16.102 (Microsoft Corporation) statistical software were used for analysis and creation of graphics.

Results

Within our cohort, 131 patients underwent lung resection and had robotic data available for review. Of this group, 28 patients were excluded for undergoing wedge resection alone leaving 113 patients who underwent either lobectomy or segmentectomy for analysis. Complete demographic information is displayed in Table 1.

Of the 113 patients, 91 patients underwent lobectomy, and 22 patients underwent segmentectomy. All 113 patients underwent an index lung resection (i.e., no re-operative cases). Most patients were female sex and White race. The mean age at the time of surgery was 67.3 years (SD: 7.9 years). The mean BMI was 27.6 kg/m² (SD: 5.9 kg/m²). This corresponds to a mean BMI of 27.1 kg/m² in females and 28.2 kg/m² in males. There were 83 patients with BMI <30 kg/m², 30 patients with BMI ≥30 kg/m², and 11 patients with BMI ≥35 kg/m². Across all cases, the mean CWT was 35.9 mm (SD: 15.8 mm). The average CWT was 32.1 mm for males and 38.4 mm for females. The average CWT was 30.5 mm (SD: 9.4 mm) for patients with BMI <30 kg/m², 50.7 mm (SD: 20.3 mm) for patients with BMI ≥30 kg/m², and 60.3 mm (SD: 23.4 mm) for patients with BMI ≥35 kg/m².

Across all cases, the average count of camera cleans was 9.4 per case (SD: 9.4), corresponding to an average camera cleaning rate of 4.1 per hour (SD: 3.2). For patients with BMI <30 kg/m², the mean number of camera cleans per case was 7.6 (SD: 5.7), corresponding to an average cleaning rate of 3.4 per hour (SD: 2.1). For patients with BMI ≥30 kg/m², the mean number of camera cleans per case was 14.4 (SD 14.4), corresponding to an average cleaning rate of 5.9 per hour (SD: 4.7). For patients with BMI ≥35 kg/m², the mean number of camera cleans per case was 19.4 (SD 19.0), corresponding to an average cleaning rate of 7.7 (SD: 5.7) per hour. Summary data is listed in Table 2.

Simple linear regression of BMI to camera cleaning rate was positive with R=0.48 (95% CI: 0.33–0.61, P<0.001) and R²=0.23 while simple linear regression of CWT to camera cleaning rate was positive with R=0.43 (95% CI: 0.26–0.57, P<0.001) and R²=0.18. Simple linear regression of BMI and CWT showed a positive correlation with R=0.67 (95% CI: 0.55–0.76, P<0.001) and R²=0.44. Simple linear regression of camera cleaning rate and operative console time showed positive correlation with R=0.45 (95% CI: 0.28–0.58, P<0.001) and R²=0.20 (Figure 1).

Figure 1 Simple linear regression for measured outcomes. scatterplots of raw data with regression lines (solid black line) and 95% confidence interval bands (dashed black lines) for various measures. (A) Correlation of BMI and CWT: R=0.67 (95% CI: 0.55–0.76; P<0.001). Goodness of fit: R²=0.4439 and equation: y=1.806x−13.94; (B) correlation of BMI and robotic camera cleaning rate (per hour): R=0.48 (95% CI: 0.33–0.61; P<0.001). Goodness of fit: R²=0.2327 and equation: y=0.2612x−3.12; (C) correlation of CWT and robotic camera cleaning rate (per hour): R=0.43 (95% CI: 0.26–0.57; P<0.001). Goodness of fit: R²=0.1818 and equation: y=0.08515x+1.027; (D) correlation of camera cleaning and total robotic console time: R=0.45 (95% CI: 0.28–0.58; P<0.001). Goodness of fit: R²=0.1985 and equation: y=2.271x+111.0. BMI, body mass index; CI, confidence interval; CWT, chest wall thickness.

To evaluate the impact of neoadjuvant chemoimmunotherapy as a possible confounding variable, we created a separate cohort consisting of patients who did not receive neoadjuvant therapy prior to resection. There were 18 patients who received neoadjuvant therapy prior to lung resection, leaving 95 patients who received no pre-operative treatment in the new cohort. Compared to the initial study group, this new cohort did not demonstrate significant differences with respect to demographics, case type, or average BMI, CWT, and camera cleaning rate. For this cohort, simple linear regression of BMI to camera cleaning rate was moderately positive with R=0.49 (95% CI: 0.31–0.63, P<0.001) while simple linear regression of CWT to camera cleaning rate was moderately positive with R=0.39 (95% CI: 0.21–0.55, P<0.001). Simple linear regression of BMI and CWT showed a strong positive correlation with R=0.70 (95% CI: 0.58–0.79, P<0.001). Simple linear regression of camera cleaning rate and operative console time showed moderate positive correlation with R=0.45 (95% CI: 0.27–0.59, P<0.001). This cohort was only used for sensitivity analysis, and the overall results and implications were made using data from the entire cohort of 113 patients.

Discussion

In this study, we examined the relationship between BMI, CWT, camera cleaning rate for robotic surgery, and console time for patients undergoing robotic lung resection. We a strong positive association between CWT and BMI and found a moderate positive correlation between both BMI with camera cleaning rate and CWT with camera cleaning rate. Increased camera cleaning rate showed moderate positive correlation with operative console time. Through a sub-analysis of measured variables stratified by BMI, we found noticeable differences for patients with BMI ≥30 and ≥35 kg/m² compared to counterparts with BMI <30 kg/m². Specifically, patients with BMI ≥35 kg/m² had 1.9 times thicker chest walls and 2.2 times more frequent camera cleaning, on average. However, there was not an observed difference with operative time between these elevated BMI groups.

Overall, these findings confirm our hypothesis of there being a positive association of robotic camera cleaning and CWT. Based on our findings, 18% of the variability in camera cleaning rate can be explained by CWT while around 23% of the variability can be explained by BMI. These findings corroborate our hypothesis that patients with elevated BMI have increased camera cleaning rates and prompt surgeons to consider this association when planning for surgery in this population. They suggest that the observed differences in camera cleaning are particularly apparent in the cohorts of patients with BMI ≥30 and ≥35 kg/m² and that there may be increased disruption of surgical workflow for these patients. This also calls for additional investigation of methods or techniques that may be used to reduce the observed technical barriers posed by obese patients in robotic thoracic surgery.

There are prior studies regarding lung resection in obese patients to discuss. In 2012, Julien et al. performed a retrospective analysis of 19,337 patients using the Society for Thoracic Surgeons (STS) Database and determined that operative time increased by around 7 minutes for every 10-point increase in BMI (9). Additionally, in 2017 Montané et al. performed a retrospective review of 280 patients who underwent robotic lobectomy to evaluate the impact of elevated BMI on short term outcomes. They found no increased risk for intraoperative complications, rate of conversion to open surgery, or short-term postoperative outcomes aside from an increased risk for pneumonia in patients with BMI ≥30 kg/m² (7). In a more recent study from 2022, Seder et al. used the STS Database to perform a propensity score analysis comparing clinical outcomes for robotic-assisted (RATS) versus video-assisted thoracic surgery (VATS) in 8,108 patients with BMI ≥30 kg/m². They demonstrated increased operative time and shorter length of stay for RATS compared to VATS, though they showed no difference in other short term clinical outcomes (11). While this current literature provides insight into the overall impact of elevated BMI on robotic thoracic surgery, they do not identify or quantify the specific reasons why these results are seen.

From a technical perspective, there is a high amount of variability in how and when the primary camera port is inserted during robotic lung resection. For techniques such as the of the primary surgeon’s in the analysis, the camera port is the first placed and is done using blunt technique which may lead to high rates of local trauma around the port. Regardless, of technique though, thicker chest walls have more tissue that can bleed or leach fluid to obscure the camera.

This study has some limitations to note. Firstly, this is a retrospective review susceptible to sampling bias. However, we did include all patients who underwent lung resection with available robotic data for review. Additionally, while using data from a single surgeon for a specific procedure may provide more consistent data, this may result in skewed patient selection and limit study generalizability. Furthermore, while our results show an association of elevated BMI with increased rates of camera cleaning, this is only a single technical factor faced in this patient population. Due to the nature of this database and its available data, we are unable to examine additional perioperative outcomes including post-operative pain control, estimated blood loss, and complication rates. Lastly, neoadjuvant therapy has been shown to result in more challenging operative cases with more difficult dissection and higher rates of conversion to thoracotomy (12,13). To evaluate the possible confounding effect of neoadjuvant therapy on camera cleaning and operative time, we performed a separate analysis excluding these patients. Overall, these two groups did not show significant differences in patient demographics, case distribution, or any of the simple regression analyses performed. Therefore, we decided to include all cases for our analysis and discussion of implications.

In the future, it would be helpful to look more broadly at the technical impact of obesity in thoracic surgery by expanding this evaluation to include patients undergoing other intra-thoracic robotic procedures including esophagectomy and mediastinal mass resection. In this way, we could increase the generalizability of these findings. Additionally, there needs to be further evaluation of new technology which may help limit or improve robotic camera cleaning. While more common for robotic arthroscopic procedures, there are some new devices which help provide a barrier between the insertion site and the robotic camera (14). This type of technology could be utilized with the robotic camera port in thoracic cases for obese patients to help reduce camera cleaning rates. This may limit the frequency of robotic camera cleaning not only resulting in shorter case times but also limiting the dangerous periods of non-visualization of the operative field during camera exchange. A prospective trial could be performed both with and without use of this device to evaluate operative times and perioperative outcomes.

Conclusions

In this limited study, there was an association of increased CWT and higher BMI. The positive correlation of BMI and CWT to camera cleaning frequency suggest that obesity is associated with the technical facility of robotic lung resection. While additional investigation needs to be considered, these findings may inform thoracic surgeons and patients on operative planning and expectations of outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://vats.amegroups.com/article/view/10.21037/vats-2026-1-0006/rc

Data Sharing Statement: Available at https://vats.amegroups.com/article/view/10.21037/vats-2026-1-0006/dss

Peer Review File: Available at https://vats.amegroups.com/article/view/10.21037/vats-2026-1-0006/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://vats.amegroups.com/article/view/10.21037/vats-2026-1-0006/coif). N.E.III has received speaker and consultant honoraria from Intuitive, Merck, Bristol Myers Squibb, and AstraZeneca. O.T.O. has received honoraria from Intuitive Surgical, Johnson & Johnson, Atricure and AstraZeneca. 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. The study was deemed exempt from Institutional Review Board approval at Thomas Jefferson University Hospital and individual consent for this retrospective analysis was waived.

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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