Comparative analysis of short-term outcomes, inflammatory markers, and complications in robotic-assisted vs. traditional thoracoscopic surgery for complex pulmonary segmentectomy
Original Article

Comparative analysis of short-term outcomes, inflammatory markers, and complications in robotic-assisted vs. traditional thoracoscopic surgery for complex pulmonary segmentectomy

Yuhao Xu1, Xuefeng Wang1, Biao Deng1,2, Zhu Liang3, Zhigang Wang3

1Graduate School, Guangdong Medical University, Zhanjiang, China; 2The First Clinical College of Guangdong Medical University, Zhanjiang, China; 3Department of Cardiothoracic Surgery, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, China

Contributions: (I) Conception and design: Y Xu; (II) Administrative support: Z Liang; (III) Provision of study materials or patients: Z Wang; (IV) Collection and assembly of data: X Wang; (V) Data analysis and interpretation: B Deng; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Zhigang Wang, PhD; Zhu Liang, PhD. Department of Cardiothoracic Surgery, The Affiliated Hospital of Guangdong Medical University, No. 57 South People’s Avenue, Xiaoshan District, Zhanjiang 524000, China. Email: 18218258228@139.com; liangzhuwsh@163.com.

Background: Robotic-assisted thoracic surgery (RATS) and video-assisted thoracic surgery (VATS) have been utilized in the context of complex pulmonary segmentectomy. However, the extant literature offers a paucity of information with regard to the early outcomes and inflammatory impacts of these approaches inadequately. We analyzed perioperative outcomes, inflammatory markers, and complications in 31 RATS and 66 VATS cases.

Methods: A retrospective study was conducted on 97 patients undergoing complex pulmonary segmentectomy for pulmonary nodules at The Affiliated Hospital of Guangdong Medical University between July 2022 and October 2024. Short-term surgical outcomes and inflammatory markers were compared.

Results: Among 97 patients with comparable baseline characteristics, no significant differences were observed in intraoperative blood loss, extubation time, or overall complication rates between RATS (n=31) and VATS (n=66). However, RATS demonstrated shorter operative time (174.00±50.67 vs. 224.24±61.65 min, P<0.001), reduced inflammatory responses [postoperative white blood cell counts: (9.90±2.10)×109/L vs. (13.31±4.03)×109/L, P<0.001], and fewer dissected lymph nodes (7.60±4.79 vs. 12.48±7.80, P=0.002). RATS also exhibited superior ergonomic design and three-dimensional (3D) visualization.

Conclusions: For patients diagnosed with early-stage pulmonary nodules, RATS significantly shortens operative time, mitigates inflammatory responses, and enhances ergonomic efficiency compared to VATS.

Keywords: Complex pulmonary segmentectomy; robotic-assisted; inflammatory markers


Submitted Oct 19, 2025. Accepted for publication Feb 04, 2026. Published online Mar 24, 2026.

doi: 10.21037/jtd-2025-aw-2146


Highlight box

Key findings

• Robotic-assisted thoracic surgery (RATS) demonstrated perioperative outcomes comparable to those of video-assisted thoracic surgery (VATS).

• RATS was shown to be a safe and feasible minimally invasive approach for thoracic surgical management.

What is known and what is new?

• VATS has been widely established as a standard minimally invasive technique in thoracic surgery. RATS has emerged as an alternative approach with potential technical advantages, including enhanced three-dimensional visualization and improved instrument maneuverability.

• This propensity score–matched analysis provides further evidence that RATS achieves comparable perioperative safety and feasibility to VATS.

What is the implication, and what should change now?

• The findings support the broader clinical adoption of RATS in appropriately selected patients.

• Future prospective multicenter studies are needed to further evaluate long-term outcomes and cost-effectiveness of RATS.


Introduction

Globally, robotic surgery has advanced rapidly over the past decade, integrating cutting-edge technology with minimally invasive surgery (MIS) techniques. Compared to open surgery, MIS offers improved short-term oncologic outcomes and reduced complications (1-3). While robotic systems minimize hand tremors and enhance visualization, ergonomics, and dexterity, robust evidence supporting robotic-assisted thoracic surgery (RATS) over video-assisted thoracic surgery (VATS) in complex pulmonary segmentectomy—particularly regarding inflammatory modulation—remains limited (4).

RATS utilizes robotic platforms controlled via a remote console, providing surgeons with three-dimensional (3D) optics, extended instrument range, and ergonomic benefits. Despite its advantages, debates persist regarding its superiority over VATS in specific procedures, high costs, and prolonged setup times (5,6). Furthermore, large-scale randomized trials comparing RATS and VATS outcomes are scarce, underscoring the need for data validating RATS’ non-inferiority in morbidity, mortality, recovery, and cost-effectiveness (7,8).

Tissue injury and inflammatory markers critically influence postoperative recovery, measurable through circulating inflammatory proteins, stress hormones, and tissue damage indicators (9,10). Studies suggest that VATS induces less tissue trauma and inflammation than open surgery, but evidence on RATS’ anti-inflammatory efficacy in segmentectomy is lacking (11,12). To address this gap, we analyzed perioperative outcomes and inflammatory markers in 31 RATS and 66 VATS cases undergoing complex segmental lung resection. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2146/rc).


Methods

Patient groups

This study is a single-center retrospective cohort study that included patients undergoing complex segmental resection for pulmonary nodules at The Affiliated Hospital of Guangdong Medical University between July 2022 and October 2024. Regarding the definition of complex segmentectomy, this paper adopts the Handa classification. Its core principle is based on the number and morphology of inter-segmental planes required during surgery. Specifically, simple segmentectomy typically involves managing only one primary, relatively linear inter-segmental plane, presenting a technically straightforward approach. In contrast, complex segmentectomy requires handling two or more inter-segmental planes, or planes exhibiting significant non-linearity, curvature, or multi-interface combinations. Inclusion criteria: (I) lung segmentectomy performed via RATS or VATS; (II) preoperative diagnosis of ground-glass nodule with consolidation-to-tumor ratio (CTR) <0.75 and tumor diameter ≤2 cm; (III) adequate cardiopulmonary function for surgery; and (IV) complete clinical and follow-up data. Exclusion criteria: (I) tumor diameter >2 cm; (II) preoperative neoadjuvant or ablation therapy; (III) patients forced to undergo segmentectomy due to inadequate cardiopulmonary function; and (IV) postoperative resection of lung tissue outside the nodule or combined resection of other tissues. Ultimately, we systematically reviewed the clinical data of 97 patients who successfully underwent complex segmentectomy. All patient data collected in this paper were performed by the same surgeon, who has over 20 years of experience and is highly proficient in both thoracoscopic surgery and Da Vinci robot-assisted lung cancer resection. The surgeon has performed over 1,000 VATS procedures and over 400 RATS procedures individually, demonstrating exceptional mastery and proficiency in both RATS and VATS techniques. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Affiliated Hospital of Guangdong Medical University (No. PJKT2023-080), and all patients signed informed consent forms.

Clinical information

Surgical preparation and postoperative treatment options were similar for both RATS and VATS. Clinical data on baseline characteristics, perioperative outcomes, and pathological outcomes were collected from the electronic medical records of each patient. The perioperative period corresponds to the entire course surrounding the operation, including pre-, during-, and post-operative periods. Specifically, starting from confirmation of surgical treatment (approximately 5–7 days prior to surgery) until completion of procedure-related treatment (7–12 days after surgery). The patient’s pathological staging was assessed using the 8th edition of the tumor-node-metastasis (TNM) classification system.

Surgical technique

The RATS procedure was performed using the Da Vinci S/Si robotic system (Intuitive Surgical, Sunnyvale, CA, USA) with a four-arm configuration, as detailed in our previous studies (12,13). Concurrently, video-assisted endoscopic guidance (Karl Storz, Tuttlingen, Germany) was employed via a single incision during thoracoscopic procedures. The primary steps of both approaches were similar: initial dissection of target anatomical structures at the segmental hilum using a guidewire or stapler, followed by precise identification of the intersegmental plane by the surgeon. The surgeon must meticulously dissect the intersegmental plane. Arterial and venous vessels were ligated using Hem-o-Lok clips (Teleflex, Morrisville, NC, USA) or vascular staplers. Bronchi were subsequently dissected and ligated. Following alveolar ventilation, hypothetical inter-segmental plane stapling was achieved via residual lung inflation. All procedures were performed by a general thoracic surgeon (Z.L.) and clinically assessed according to National Comprehensive Cancer Network guidelines. In this study, N0 status must first be confirmed. N1 and N2 lymph node dissection and localization constitute routine procedures in lung cancer surgery, involving lymph nodes in zones 12 and 13. At least three N2 lymph node regions should be sampled or standardized lymph node dissection performed. If lymph node enlargement (greater than 1 cm) or positive margins are detected, frozen section analysis is conducted.

Propensity score matching (PSM)

Given the retrospective, non-randomized design of this study, PSM was performed to reduce selection bias between the RATS and VATS groups. Propensity scores were estimated using a logistic regression model with surgical approach as the dependent variable. The following preoperative variables were included based on clinical relevance and their potential association with treatment selection and outcomes: age, sex, smoking history, drinking history, diabetes, cardiovascular disease, hypertension, and body mass index. Patients were matched using 1:2 nearest-neighbor matching. Baseline characteristics after matching are shown in Table 1.

Table 1

Baseline characteristics before and after PSM

Variables Before PSM After 1:2 PSM
RATS VATS SMD P value RATS VATS SMD P value
Age (years) 54.65±13.03 54.81±12.01 −0.013 0.95 54.65±13.03 55.42±11.66 −0.063 0.78
Gender 9 (29.0) 25 (39.1) −0.212 0.47 9 (29.0) 10 (16.1) 0.309 0.24
Smoking history 1 (3.2) 10 (15.6) −0.424 0.10 1 (3.2) 1 (1.6) 0.105 >0.99
Drinking history 1 (3.2) 3 (4.7) −0.075 >0.99 1 (3.2) 1 (1.6) 0.105 >0.99
Diabetes 3 (9.7) 5 (7.8) 0.066 0.71 3 (9.7) 2 (3.2) 0.263 0.33
Cardiopathy 0 (0.0) 3 (4.7) −0.310 0.55 0 (0.0) 0 (0.0) 0.000 >0.99
Hypertensive disease 6 (19.4) 10 (15.6) 0.098 0.87 6 (19.4) 15 (24.2) −0.117 0.79
BMI (kg/m2) 22.23±4.92 22.14±3.28 0.023 0.92 22.23±4.92 22.41±2.93 −0.044 0.85

Continuous data are presented as mean ± SD, and categoric data are presented as n (%). P values were calculated using Welch’s t-test or Fisher’s exact test as appropriate. BMI, body mass index; PSM, propensity score matching; RATS, robotic-assisted thoracic surgery; SD, standard deviation; SMD, standardized mean difference; VATS, video-assisted thoracic surgery.

Statistical analysis

Statistical analyses were performed using SPSS software (version 22.0; IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro-Wilk test and visual inspection of distribution plots. Normally distributed variables are presented as mean ± standard deviation and were compared using Student’s t-test or Welch’s t-test, as appropriate. Non-normally distributed variables are presented as median (interquartile range) and were compared using the Wilcoxon rank-sum test. Categorical variables were compared using the χ2 test or Fisher’s exact test and are presented as n (%).

All analyses were exploratory in nature. No formal correction for multiple comparisons was applied; therefore, the potential risk of type I error should be considered when interpreting the results. Analyses were performed using available data. Variables with substantial missing values were excluded from specific analyses.


Results

A total of 97 patients who received the complex pulmonary segmentectomy through either the RATS (n=31) or the VATS (n=66) met the selection criteria and were included in the study. Patient demographic and clinical characteristics are summarized in Table 1. The RATS and VATS cohorts were comparable in terms of age, sex, body mass index, and personal history. Baseline characteristics after PSM are presented in Table 1. After matching, the distribution of baseline variables between the RATS and VATS groups was more comparable.

Tables 2,3 show the results of preoperative and postoperative blood routine in RATS or VATS groups, respectively. Before surgery, most of the blood routine results of the two groups were not significantly different (P>0.05), except for the absolute count of basophils. After the operation, white blood cell counts and cell proportion in the VATS group. The white blood cell count was significantly higher in the VATS group (13.31±4.03)×109/L than in the RATS group (9.90±2.10)×109/L (P<0.001). The proportion of neutrophils was significantly higher in the VATS group (81.64%±5.73%) than that in the RATS group (78.50%±6.90%) (P=0.02).

Table 2

Preoperative blood routine examination

Variables RATS (n=31) VATS (n=66) P value
WBC (×109/L) 6.29±1.42 5.85±1.25 0.13
Proportion of neutrophils (%) 53.53±8.44 54.53±7.64 0.56
Lymphocyte ratio (%) 35.02±7.11 34.48±7.50 0.73
Monocyte ratio (%) 7.57±1.60 8.07±1.51 0.14
Eosinophil ratio (%) 2.50 (1.85, 3.80) 2.20 (1.40, 2.98) 0.08
Proportion of basophils (%) 0.60 (0.40, 0.80) 0.50 (0.40, 0.70) 0.08
Neutrophils (×109/L) 3.43±1.20 3.21±0.87 0.29
Lymphocytes (×109/L) 2.14±0.41 2.01±0.55 0.23
Monocytes (×109/L) 0.48±0.15 0.47±0.13 0.82
Eosinophils (×109/L) 0.14 (0.11, 0.24) 0.13 (0.08, 0.19) 0.045
Basophils (×109/L) 0.04 (0.03, 0.05) 0.03 (0.02, 0.04) 0.02
RBC (×1012/L) 4.63±0.47 4.52±0.55 0.32
HGB (g/L) 131.41±25.09 133.18±13.84 0.65
HCT (%) 41.51±3.53 40.35±3.83 0.16
MCV (fL) 89.83±4.94 89.78±6.45 0.97
MCH (pg) 29.26±2.21 29.66±2.58 0.46
MCHC (g/L) 325.52±11.67 330.03±12.27 0.09
RDV.CV (%) 12.80±0.97 12.92±1.38 0.68

Data are presented as mean ± SD or median (IQR). HCT, hematocrit; HGB, hemoglobin; IQR, interquartile range; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RATS, robotic-assisted thoracic surgery; RBC, red blood cell; RDV.CV, red blood cell distribution width-coefficient of variation; SD, standard deviation; VATS, video-assisted thoracic surgery; WBC, white blood cell.

Table 3

Postoperative blood routine examination

Variables RATS (n=31) VATS (n=66) P value
WBC (×109/L) 9.90±2.10 13.31±4.03 <0.001
Proportion of neutrophils (%) 78.50±6.90 81.64±5.73 0.02
Lymphocyte ratio (%) 13.54±5.47 10.53±4.31 0.004
Monocyte ratio (%) 6.35±1.72 6.89±1.72 0.15
Eosinophil ratio (%) 0.90 (0.50, 2.00) 0.40 (0.10, 1.15) 0.002
Proportion of basophils (%) 0.20 (0.10, 0.25) 0.20 (0.10, 0.20) 0.61
Neutrophils (×109/L) 7.82±1.96 10.97±3.80 <0.001
Lymphocytes (×109/L) 1.30±0.55 1.32±0.49 0.89
Monocytes (×109/L) 0.63±0.24 0.90±0.32 <0.001
Eosinophils (×109/L) 0.08 (0.04, 0.18) 0.04 (0.01, 0.12) 0.01
Basophils (×109/L) 0.02 (0.01, 0.03) 0.02 (0.01, 0.03) 0.12
RBC (×1012/L) 4.24±0.50 4.17±0.45 0.51
HGB (g/L) 125.87±14.32 123.76±13.18 0.48
HCT (%) 38.30±3.88 37.41±3.65 0.27
MCV (fL) 90.65±5.25 90.01±6.68 0.64
MCH (pg) 29.78±2.25 29.83±2.63 0.93
MCHC (g/L) 328.29±10.22 330.46±11.24 0.37
RDV.CV (%) 12.91±0.86 13.12±1.26 0.40
CRP (mg/L) 13.29±2.18 86.86±16.21 <0.001

Data are presented as mean ± SD or median (IQR). CRP, C-reactive protein; HCT, hematocrit; HGB, hemoglobin; IQR, interquartile range; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RATS, robotic-assisted thoracic surgery; RBC, red blood cell; RDV.CV, red blood cell distribution width-coefficient of variation; SD, standard deviation; VATS, video-assisted thoracic surgery; WBC, white blood cell.

Type of the removed lung segment: Table 4 shows the types of lung segment surgeries performed by either RATS or VATS. The RATS group performed more single complex segmental resections, while the VATS group performed more single or double segmental resections. Additionally, the distribution trends of resected segment locations were broadly similar between the two groups. Specifically, 17 cases (54.9%) in the RATS group and 73 cases (52.4%) in the VATS group were on the right side. On the left side, 14 cases (45.1%) were in the RATS group and 44 cases (47.6%) were in the VATS group. In all cases, RS2 was the most common complex segment procedure, followed by RS9.

Table 4

Locations of resected subsegments

Surgery category RATS (n=31) VATS (n=66) P value
Location of nodules 0.85
   Right 29 (59.2) 73 (52.4)
   Left 29 (40.8) 44 (47.6)
Type of subsegmentectomy 0.61
   Single 20 34
   Two 10 21
   Three 1 11

Data are presented as number (%) or number. RATS, robot-assisted thoracic surgery; VATS, video-assisted thoracic surgery.

The perioperative results are summarized in Table 5. Operation time (the specific duration of surgery refers to the time elapsed from the completion of preoperative verification and the surgeon initiating the first procedure step until the final suturing is completed): operation time was significantly shorter in the RATS group (174.00±50.67 min) than that in the VATS group (224.24±61.65 min) (P<0.001). Blood loss: less blood loss in the RATS group, with blood loss in 93.5% of patients below 50 mL, compared to 90.9% in the VATS group (P>0.99). Pneumothorax and effusion: no significant difference between the two groups (RATS 58.1% vs. VATS 60.6%), and no significant difference in effusion incidence (RATS 42.9% vs. VATS 30.3%) (P=0.72 and P=0.21). Level of pain: less pain was reported in the RATS group, with 0% of patients with grade 0 pain compared to 2.3% in the VATS group (P=0.12). Thoracic drain retention time and drainage flow: shorter in the RATS group (42 vs. 60 hours), although the difference did not reach significance (P=0.13). The 24-h total thoracic drainage discharge was significantly higher in the VATS group than in the RATS group (375.00 vs. 262.50 mL) (P<0.001). Length of stay: the length of stay was significantly longer in the VATS group (6.14±4.18 vs. 3.17±0.79 days) (P<0.001). Number of lymph nodes and lymph node sites: the number of lymph nodes in the VATS group (12.48±7.80 vs. 7.60±4.79) and the number of lymph node sites (4.76±1.43 vs. 3.80±1.77) were significantly greater than those in the RATS group (P=0.002 and P=0.006).

Table 5

Perioperative results

Variables RATS (n=31) VATS (n=66) P value
Operative duration (min) 174.00±50.67 224.24±61.65 <0.001
Blood loss >0.99
   <50 mL 29 (93.5) 60 (90.9)
   ≥50 mL 2 (6.5) 6 (9.1)
Conversion thoracotomy >0.99
   No 31 (100.0) 66 (100.0)
Pneumothorax 0.72
   No 18 (58.1) 40 (60.6)
   Yes 13 (42.9) 26 (39.4)
Hydrothorax 0.21
   No 18 (58.1) 46 (69.7)
   Yes 13 (42.9) 20 (30.3)
Pain degree 0.12
   1 0 (0.0) 2 (3.0)
   2 4 (12.9) 14 (21.2)
   3 16 (51.6) 39 (59.1)
   4 10 (32.3) 11 (16.7)
   5 1 (3.2) 0 (0.0)
Pleural canals indwelling time (h) 42.00 (31.00, 66.00) 60.00 (38.00, 85.00) 0.13
24 h pleural canals drainage (mL) 150.00 (62.50, 200.00) 160.00 (100.00, 270.00) 0.37
Total pleural canals drainage (mL) 262.50 (167.50, 345.00) 375.00 (300.00, 692.50) <0.001
Length of stay (days) 3.17±0.79 6.14±4.18 <0.001
LN number 7.60±4.79 12.48±7.80 0.002
LN stations number 3.80±1.77 4.76±1.43 0.006

Continuous data are presented as mean ± SD or median (IQR), and categorical data are presented as n (%). IQR, interquartile range; LN, lymph node; RATS, robot-assisted thoracic surgery; SD, standard deviation; VATS, video-assisted thoracic surgery.


Discussion

Rapid advances in minimally invasive thoracic surgery have led to the clinical use of robotic-assisted systems, which have facilitated the development of anatomical lung dissection (1,14-16). Previous studies have shown that robotic lung segmental resection is safe, effective, and provides excellent perioperative outcomes (17). This study provides the first detailed analysis of short-term comparative outcomes and inflammatory response in RATS and VATS cancer patients undergoing complex lung segmental surgery. The results show that for complex lung segment surgery, RATS offers significant advantages in terms of pain relief, reduced drainage, and shorter hospital stay. As the number of patients diagnosed with early-stage pulmonary nodules through active screening programmes increases rapidly, it has become crucial to clarify the comparative benefits of segmentectomy vs. lobectomy for patients with a high suspicion of malignant pathology (18,19). To date, segmental resection has been evaluated as a surgical procedure; however, we believe that segmental resection may be further subdivided depending on the surgical procedure and the intersegmental plane. Simple segmental resection involves resection of the RS6, LS6, left upper sector, or lingual segments and is considered a relatively easy procedure, whereas complex segmental resection creates several complex intersegmental planes and is a much more complicated procedure resecting the S3, LS9, RS1+3, LS9+10 segments, and so on (20-22). Complex segmental resection remains highly controversial as a treatment option for lung cancer and the use of complex procedures in the management of segmental bronchioles, arteries, veins and intersegmental planes has led to increased concern among surgeons about the short-term complication rate of the procedure and long-term survival outcomes.

RATS demonstrates significant technical and clinical advantages over conventional thoracoscopy in complex segmental lung resections. Based on the data analyzed in this study, RATS demonstrated many potential benefits in terms of reduced drainage, postoperative pain control, inflammatory response modulation, and ergonomics. Combined with perioperative outcomes and postoperative recovery (23). The results of this study showed that RATS was superior to conventional VATS in complex segmental resections (e.g., double-segment and triple-segment combined resections). Specific anatomical regions (e.g., complex segments of the left upper lobe S1+2, right lung S6, etc.) due to the complexity of the anatomical structures and the requirement of precision in intraoperative manipulation, conventional VATS often requires the surgeon to perform difficult separation and The 3D high-definition (HD) field of view and flexible multi-degree-of-freedom robotic arm of RATS can significantly improve the precision of intraoperative dissection and operation (24-27).

In addition, complex segmental resection is often associated with higher intraoperative risks and postoperative complications, such as the risk of pulmonary inflammatory reactions and leakage (28). In this study, the amount of postoperative drainage fluid and hospital stay were significantly less in the RATS group than in the VATS group, suggesting that RATS is more effective in reducing postoperative inflammatory reactions (29). This may be related to the following factors: (I) the RATS technique reduces the pulling and damage to the surrounding tissues through precise anatomical manipulation; (II) the stability of the robotic arm allows for a higher quality of suture, which avoids leakage and related inflammation caused by improper suture; and (III) precise lymph node dissection manipulation reduces mechanical stimulation of neighboring structures, which in turn alleviates postoperative inflammatory reactions (30-33). These results suggest that RATS in the management of complex anatomical structures can significantly reduce the incidence of inflammatory complications and promote rapid postoperative patient recovery. For future clinical practice, these features of RATS make it more advantageous in high-risk, complex lung segmental resection cases.

Postoperative inflammatory response is one of the most important factors in determining the speed of recovery of patients (34-36). The VATS group exhibited significantly higher white blood cell counts and pleural drainage volumes than the RATS group, with results demonstrating statistically significant differences. This suggests that RATS may offer potential advantages in mitigating postoperative inflammation. The mechanisms may include: more precise incision and dissection manipulation reduces vascular and tissue irritation; robotic arm-assisted minimally invasive manipulation reduces intraoperative thermal injury and tissue pulling; and intraoperative 3D HD imaging helps the operator to avoid critical structures more efficiently, which reduces postoperative inflammatory response in the neighboring tissues (37). Combined with the available data, the reduced inflammatory response may further shorten patient recovery time and reduce the need for postoperative related therapies. Due to the significant number of missing postoperative C-reactive protein values, this variable was excluded from the assessment of inflammatory response indicators. Further future studies on postoperative inflammatory markers (e.g., cytokine levels, etc.) could more fully validate the effectiveness of RATS in inflammation control (38).

RATS has been shown to exhibit a surgical time advantage in complex segmental lung resections (174.0±50.67 vs. 224.24±61.65 min, P<0.001). This is not only due to the technical characteristics of the procedure, but also closely related to the optimization of human factors engineering (HFE) (39-41). The robotic system is designed through a console to allow the operator to operate in a seated position, thus avoiding muscle fatigue caused by prolonged standing and fixed posture in conventional endoscopic surgery (VATS) (42). Studies have shown that VATS operators need to coordinate hand-eye movements in two-dimensional vision, and are prone to prolonged operation times due to visual fatigue and postural discomfort The 3D HD vision and device freedom (e.g., 7-axis wrist devices) have been shown to significantly improve hand-eye coordination and reduce the need for repeated lens adjustments, thus shortening the operating time for critical steps (43-46). For instance, the literature states that the tremor filtering function of robotic systems reduces operational errors and enables smoother fine movements such as stitching and separation. The modular design of robotic surgery systems, incorporating features such as master and slave control and voice commands, facilitates enhanced communication between the surgeon and the assistant, thereby reducing the complexity of team collaboration (47,48). For instance, the AESOP system utilizes voice control for endoscopic positioning, thereby reducing the delay in assistant operation. Furthermore, the real-time data integration of the robotic system [e.g., intraoperative navigation, artificial intelligence (AI)-assisted image analysis] can rapidly provide anatomical structure information, thereby reducing intraoperative decision-making time. Studies have demonstrated that the application of AI algorithms in preoperative planning can optimize device paths and further reduce operation time (49). Despite the initial learning curve associated with robotic surgery, the intuitive operation interface and simulation training system can facilitate rapid operator adaptation to complex procedures. Studies have demonstrated that after performing 4–5 robotic surgeries, there is a substantial enhancement in docking time and operation efficiency (50).

In contrast, VATS necessitates a more protracted period to cultivate hand-eye coordination and spatial perception (51). Moreover, the force feedback technology of the robot system [e.g., the robot-assisted microsurgery (RAMS) system] facilitates the adeptness of novice operators in performing precise movements, thereby reducing the overall operation time. The enhanced stability of robotic surgery mitigates the occurrence of intraoperative incidents (e.g., bleeding, accidental injury) and circumvents the additional time expenditure incurred by management complications (52-55). For instance, the literature indicates that RATS reduces tissue traction damage by precise dissection, which in turn reduces the need for postoperative bleeding and secondary exploration. Additionally, the automatic functions of the robot system (e.g., device self-inspection, path memory) reduce the equipment debugging time, further optimizing the process (56).

Although RATS demonstrated many advantages in this study, there are some limitations. In the methodological design of this study, on the one hand, selection bias cannot be entirely eliminated due to its retrospective and non-randomized nature. Although PSM was employed to balance measured baseline characteristics, residual confounding factors arising from unmeasured variables may still persist. On the other hand, the relatively small sample size in the RATS group may limit statistical power and generalizability. In the results section of this paper, RATS is still slightly inferior to VATS in terms of the number and extent of lymph node dissection. This may be attributed to the fact that all enrolled patients in this study had ground-glass nodules. Based on the JCOG0804 series of studies, ground-glass nodules with a CTR less than 0.25 do not require lymph node dissection. However, the RATS group demonstrated superior lymph node clearance efficacy compared to the VATS group when addressing hilar mediastinal lymph nodes. This advantage was manifested in the RATS group achieving more complete clearance of individual lymph node groups, whereas the VATS group often struggled to clear entire groups due to factors like energy-based instruments, leading to increased lymph node counts. This difference may also be related to the limited experience of surgeons during the early adoption phase of RATS technology (57-60). In addition, compared to VATS, the high cost of robotic systems like the da Vinci system and the longer intraoperative preparation time in RATS also limit their widespread adoption in primary-care hospitals. In the future, with further optimization of RATS technology, its applicability in complex cases will be further enhanced. Combined with the application of AI, augmented reality (AR), and other technologies, RATS may play a greater role in preoperative planning, intraoperative navigation, and postoperative monitoring (61,62). With the decrease in equipment cost and the accumulation of operating experience, RATS will be expected to gradually replace traditional pulmonectomy as one of the preferred surgical procedures for lung segmental resection (63-66).

In conclusion, RATS has demonstrated obvious technical advantages in lung segmental resection, especially in terms of precise intraoperative operation, postoperative inflammatory response control, and operator comfort. However, its shortcomings in lymph node clearance efficiency and equipment cost need to be further optimized. With the continuous development of technology and accumulation of experience, RATS is expected to occupy a more important position in the future field of thoracic surgery and provide patients with better medical services.


Conclusions

In complex segmental lung resection procedures, RATS offers certain advantages in reducing operative time, decreasing postoperative thoracic drainage volume and hospital stay duration, while also significantly lowering postoperative inflammatory response levels. Both surgical approaches demonstrate comparable perioperative safety profiles. RATS may hold potential advantages in complex anatomical manoeuvres, though this requires validation through further large-scale prospective studies.


Acknowledgments

Thanks to The Affiliated Hospital of Guangdong Medical University’s database for providing a platform and sharing meaningful datasets.


Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2146/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2146/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2146/prf

Funding: This project was supported by the Zhanjiang Science and Technology Project (Nos. 2025A502006 and 2025B01261), the Clinical Research Program, Affiliated Hospital of Guangdong Medical University (Nos. LCYJ2021A004 and LCYJ2022DL003), the National College Students’ Innovation and Entrepreneurship Training Program (Nos. 202510571006 and 202410571016), and the Project Funded by the School Planning, Construction and Development Center of the Ministry of Education (No. CSDP25LF8C439).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2146/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Affiliated Hospital of Guangdong Medical University (No. PJKT2023-080), and all patients signed informed consent forms.

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


References

  1. Corrales L, Rosell R, Cardona AF, et al. Lung cancer in never smokers: The role of different risk factors other than tobacco smoking. Crit Rev Oncol Hematol 2020;148:102895. [Crossref] [PubMed]
  2. Rusmaully J, Tvardik N, Martin D, et al. Risk of lung cancer among women in relation to lifetime history of tobacco smoking: a population-based case-control study in France (the WELCA study). BMC Cancer 2021;21:711. [Crossref] [PubMed]
  3. Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet 2022;399:1607-17. [Crossref] [PubMed]
  4. Galvez C, Bolufer S, Lirio F, et al. Complex segmentectomies: Comparison with simple and effect of experience on postoperative outcomes Eur J Surg Oncol 2025;51:109748. [Crossref] [PubMed]
  5. Okubo Y, Yoshida Y, Yotsukura M, et al. Complex segmentectomy is not a complex procedure relative to simple segmentectomy. Eur J Cardiothorac Surg 2021;61:100-7. [Crossref] [PubMed]
  6. Agyabeng-Dadzie K, Sarkaria IS, Chan E, et al. Comparison of Robot-Assisted Versus Video-Assisted Thoracoscopic Segmentectomy: A Single-Institution Propensity-Matched Study. Innovations (Phila) 2025;20:265-71. [Crossref] [PubMed]
  7. Melfi FM, Mussi A. Robotically assisted lobectomy: learning curve and complications. Thorac Surg Clin 2008;18:289-95. vi-vii. [Crossref] [PubMed]
  8. Louie BE, Wilson JL, Kim S, et al. Comparison of Video-Assisted Thoracoscopic Surgery and Robotic Approaches for Clinical Stage I and Stage II Non-Small Cell Lung Cancer Using The Society of Thoracic Surgeons Database. Ann Thorac Surg 2016;102:917-24. [Crossref] [PubMed]
  9. Guerrera F, Olland A, Ruffini E, et al. VATS lobectomy vs. open lobectomy for early-stage lung cancer: an endless question-are we close to a definite answer? J Thorac Dis 2019;11:5616-8. [Crossref] [PubMed]
  10. Chen D, Kang P, Tao S, et al. Cost-effectiveness evaluation of robotic-assisted thoracoscopic surgery versus open thoracotomy and video-assisted thoracoscopic surgery for operable non-small cell lung cancer. Lung Cancer 2021;153:99-107. [Crossref] [PubMed]
  11. Oh DS, Reddy RM, Gorrepati ML, et al. Robotic-Assisted, Video-Assisted Thoracoscopic and Open Lobectomy: Propensity-Matched Analysis of Recent Premier Data. Ann Thorac Surg 2017;104:1733-40. [Crossref] [PubMed]
  12. Swanson SJ, Herndon JE 2nd, D'Amico TA, et al. Video-assisted thoracic surgery lobectomy: report of CALGB 39802--a prospective, multi-institution feasibility study. J Clin Oncol 2007;25:4993-7. [Crossref] [PubMed]
  13. Kampman SL, Smalbroek BP, Dijksman LM, et al. Postoperative inflammatory response in colorectal cancer surgery: a meta-analysis. Int J Colorectal Dis 2023;38:233. [Crossref] [PubMed]
  14. Shugaba A, Lambert JE, Bampouras TM, et al. Should All Minimal Access Surgery Be Robot-Assisted? A Systematic Review into the Musculoskeletal and Cognitive Demands of Laparoscopic and Robot-Assisted Laparoscopic Surgery. J Gastrointest Surg 2022;26:1520-30. [Crossref] [PubMed]
  15. Patel AJ, Bille A. Lymph node dissection in lung cancer surgery. Front Surg 2024;11:1389943. [Crossref] [PubMed]
  16. Lamas FM, Lech GE, Maboni LR, et al. Robotic-assisted thoracic surgery versus video-assisted thoracic surgery for patients undergoing lung resection: a systematic review and meta-analysis of randomized controlled trials. Gen Thorac Cardiovasc Surg 2026;74:1-10. [Crossref] [PubMed]
  17. Savonitto E, Yasufuku K, Wallace AM. Robotic segmentectomy for early-stage lung cancer. Front Surg 2023;10:1090080. [Crossref] [PubMed]
  18. Hattori A, Suzuki K, Takamochi K, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer with radiologically pure-solid appearance in Japan (JCOG0802/WJOG4607L): a post-hoc supplemental analysis of a multicentre, open-label, phase 3 trial. Lancet Respir Med 2024;12:105-16. [Crossref] [PubMed]
  19. Garutti I, De la Gala F, Piñeiro P, et al. Usefulness of combining clinical and biochemical parameters for prediction of postoperative pulmonary complications after lung resection surgery. J Clin Monit Comput 2019;33:1043-54. [Crossref] [PubMed]
  20. Perez C, Weiser L, Watson JJ, et al. VATS Versus Robotic Anatomic Pulmonary Resection in a High-Volume Institution: Cost and Outcomes Analysis. Innovations (Phila) 2025;20:452-7. [Crossref] [PubMed]
  21. Li R, Ma Z, Li Y, et al. Robotic-assisted thoracoscopic surgery improves perioperative outcomes in overweight and obese patients with non-small-cell lung cancer undergoing lobectomy: A propensity score matching analysis. Thorac Cancer 2022;13:2606-15. [Crossref] [PubMed]
  22. Lin G, Li R, Li X, et al. Advances in the Application of Three-Dimensional Reconstruction in Thoracic Surgery: A Comprehensive Review. Thorac Cancer 2025;16:e70159. [Crossref] [PubMed]
  23. Niu Z, Cao Y, Du M, et al. Robotic-assisted versus video-assisted lobectomy for resectable non-small-cell lung cancer: the RVlob randomized controlled trial. EClinicalMedicine 2024;74:102707. [Crossref] [PubMed]
  24. Qsous G, Downes A, Carroll B, et al. A Comparison of the Differences in Postoperative Chronic Pain Between Video-Assisted and Robotic-Assisted Approaches in Thoracic Surgery. Cureus 2022;14:e31688. [Crossref] [PubMed]
  25. Inga-Zapata E, Tito L, Mandadi M, et al. Critical analysis on the assessment of ergonomics in robotic surgery: A scoping review. J Robot Surg 2026;20:185. [Crossref] [PubMed]
  26. Al Zaidi M, Wright GM, Yasufuku K. Suggested robotic-assisted thoracic surgery training curriculum. J Thorac Dis 2023;15:791-8. [Crossref] [PubMed]
  27. Rusch VW, Chansky K, Kindler HL, et al. The IASLC Mesothelioma Staging Project: Proposals for the M Descriptors and for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Mesothelioma. J Thorac Oncol 2016;11:2112-9. [Crossref] [PubMed]
  28. Bertolaccini L, Casiraghi M, Uslenghi C, et al. Advances in lung cancer surgery: the role of segmentectomy in early-stage management. Expert Rev Respir Med 2024;18:669-75. [Crossref] [PubMed]
  29. Rakotoarivelo V, Lacraz G, Mayhue M, et al. Inflammatory Cytokine Profiles in Visceral and Subcutaneous Adipose Tissues of Obese Patients Undergoing Bariatric Surgery Reveal Lack of Correlation With Obesity or Diabetes. EBioMedicine 2018;30:237-47. [Crossref] [PubMed]
  30. Sbeih D, Idkedek M, Abu Akar F. Video-Assisted vs. Robotic-Assisted Thoracoscopic Surgery in Lung Cancer: A Comprehensive Review of Techniques and Outcomes. J Clin Med 2025;14:1598. [Crossref] [PubMed]
  31. Casiraghi M, Galetta D, Borri A, et al. Ten Years' Experience in Robotic-Assisted Thoracic Surgery for Early Stage Lung Cancer. Thorac Cardiovasc Surg 2019;67:564-72. [Crossref] [PubMed]
  32. Chong CC, Fuks D, Lee KF, et al. Propensity Score-Matched Analysis Comparing Robotic and Laparoscopic Right and Extended Right Hepatectomy. JAMA Surg 2022;157:436-44. [Crossref] [PubMed]
  33. Cohen TN, Anger JT, Shamash K, et al. The Application of Human Factors Engineering to Reduce Operating Room Turnover in Robotic Surgery. World J Surg 2022;46:1300-7. [Crossref] [PubMed]
  34. Lai TJ, Roxburgh C, Boyd KA, et al. Clinical effectiveness of robotic versus laparoscopic and open surgery: an overview of systematic reviews. BMJ Open 2024;14:e076750. [Crossref] [PubMed]
  35. Forcada C, Gómez-Hernández MT, Rivas CE, et al. Impact of minimally invasive surgical approach on oncological completeness of resection in lung cancer surgery. Surg Oncol 2026;64:102310. [Crossref] [PubMed]
  36. Xiao Z, Hu X, Deng L, et al. Safety and efficacy of tirofiban versus traditionaldualantiplatelettherapy in endovasculartreatment of intracranialaneurysms: asystematicreview and meta-analysis. J Neurointerv Surg 2026;18:755-62. [Crossref] [PubMed]
  37. Nakajima T. Robotic Bronchoscopy:Current Status and Future Perspectives. Kyobu Geka 2025;78:860-5.
  38. Salama M, Mueller MR. Enhanced recovery in lung surgery: coaxial versus conventional chest drains following video-assisted thoracoscopic surgery lobectomy-a prospective randomized trial. J Thorac Dis 2025;17:10262-71. [Crossref] [PubMed]
  39. Paglialunga PL, Molins L, Guzmán R, et al. Robotic Lobectomy Learning Curve Has Better Clinical Outcomes than Videothoracoscopic Lobectomy. J Clin Med 2024;13:1653. [Crossref] [PubMed]
  40. Woo W, Lee J, Jin DH, et al. Segmentectomy quality remains important in ground-glass-dominant stage I lung cancer. Thorac Cancer 2024;15:57-65. [Crossref] [PubMed]
  41. Welcker K, Kesieme EB, Internullo E, et al. Ergonomics in thoracoscopic surgery: results of a survey among thoracic surgeons. Interact Cardiovasc Thorac Surg 2012;15:197-200. [Crossref] [PubMed]
  42. Meng YQ, Li B, Wang C, et al. Short-term outcomes of robot-assisted versus thoracoscopic-assisted Mckeown esophagectomy. Int J Med Robot 2023;19:e2538. [Crossref] [PubMed]
  43. Kong XL, Lu J, Li PJ, et al. Technical aspects and early results of uniportal video-assisted thoracoscopic complex segmentectomy: a 30 case-series study. J Cardiothorac Surg 2022;17:63. [Crossref] [PubMed]
  44. Donington J, Schumacher L, Yanagawa J. Surgical Issues for Operable Early-Stage Non-Small-Cell Lung Cancer. J Clin Oncol 2022;40:530-8. [Crossref] [PubMed]
  45. Wu R, Robayo V, Nguyen DT, et al. Enhanced recovery after surgery may mitigate the risks associated with robotic-assisted fundoplication in lung transplant patients. Surg Endosc 2024;38:2134-41. [Crossref] [PubMed]
  46. Neshan M, Padmanaban V, Chick RC, et al. Open vs robotic-assisted pancreaticoduodenectomy, cost-effectiveness and long-term oncologic outcomes: a systematic review and meta-analysis. J Gastrointest Surg 2024;28:1933-42. [Crossref] [PubMed]
  47. Kato H, Oizumi H, Suzuki J, et al. Roles and outcomes of thoracoscopic anatomic lung subsegmentectomy for lung cancer. Interact Cardiovasc Thorac Surg 2022;34:81-90. [Crossref] [PubMed]
  48. Kato H, Oizumi H, Inoue T, et al. Port-access thoracoscopic anatomical lung subsegmentectomy. Interact Cardiovasc Thorac Surg 2013;16:824-9. [Crossref] [PubMed]
  49. Deceuninck A, Thiebaut PA, Bubenheim M, et al. Quality of Lymph Node Dissection in Lung Cancer Surgery: A Comparative Analysis of Robotic-Assisted Versus Video-Assisted Thoracic Surgery Using Novel Pathological Criteria. Int J Med Robot 2025;21:e70112. [Crossref] [PubMed]
  50. Bush B, Nifong LW, Chitwood WR Jr. Robotics in cardiac surgery: past, present, and future. Rambam Maimonides Med J 2013;4:e0017. [Crossref] [PubMed]
  51. Bao F, Zhang C, Yang Y, et al. Comparison of robotic and video-assisted thoracic surgery for lung cancer: a propensity-matched analysis. J Thorac Dis 2016;8:1798-803. [Crossref] [PubMed]
  52. Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod 2021;47:1352-7. [Crossref] [PubMed]
  53. Wong SW, Crowe P. Cognitive ergonomics and robotic surgery. J Robot Surg 2024;18:110. [Crossref] [PubMed]
  54. Zhou N, Corsini EM, Antonoff MB, et al. Robotic Surgery and Anatomic Segmentectomy: An Analysis of Trends, Patient Selection, and Outcomes. Ann Thorac Surg 2022;113:975-83. [Crossref] [PubMed]
  55. Shugaba A, Subar DA, Slade K, et al. Surgical stress: the muscle and cognitive demands of robotic and laparoscopic surgery. Ann Surg Open 2023;4:e284. [Crossref] [PubMed]
  56. Mazzella A, Casiraghi M, Galetta D, et al. How Much Stress Does a Surgeon Endure? The Effects of the Robotic Approach on the Autonomic Nervous System of a Surgeon in the Modern Era of Thoracic Surgery. Cancers (Basel) 2023;15:1207. [Crossref] [PubMed]
  57. Heiden BT, Mitchell JD, Rome E, et al. Cost-Effectiveness Analysis of Robotic-assisted Lobectomy for Non-Small Cell Lung Cancer. Ann Thorac Surg 2022;114:265-72. [Crossref] [PubMed]
  58. Spinelli A, Carrano FM, Laino ME, et al. Artificial intelligence in colorectal surgery: an AI-powered systematic review. Tech Coloproctol 2023;27:615-29. [Crossref] [PubMed]
  59. Katsimperis S, Tzelves L, Feretzakis G, et al. Beyond Da Vinci: Comparative Review of Next-Generation Robotic Platforms in Urologic Surgery. J Clin Med 2025;14:6775. [Crossref] [PubMed]
  60. He B, de Smet MD, Sodhi M, et al. A review of robotic surgical training: establishing a curriculum and credentialing process in ophthalmology. Eye (Lond) 2021;35:3192-201. [Crossref] [PubMed]
  61. Zhang Y, Chen C, Hu J, et al. Early outcomes of robotic versus thoracoscopic segmentectomy for early-stage lung cancer: A multi-institutional propensity score-matched analysis. J Thorac Cardiovasc Surg 2020;160:1363-72. [Crossref] [PubMed]
  62. Leivaditis V, Maniatopoulos AA, Lausberg H, et al. Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care. J Clin Med 2025;14:2729. [Crossref] [PubMed]
  63. Lai TJ, Heggie R, Kamaruzaman HF, et al. Economic Evaluations of Robotic-Assisted Surgery: Methods, Challenges and Opportunities. Appl Health Econ Health Policy 2025;23:35-49. [Crossref] [PubMed]
  64. Catelli C, Corzani R, Zanfrini E, et al. RoboticAssisted (RATS) versus Video-Assisted (VATS) lobectomy: A monocentric prospective randomized trial. Eur J Surg Oncol 2023;49:107256. [Crossref] [PubMed]
  65. Casiraghi M, Orlandi R, Mazzella A, et al. 10-Year Long-Term Outcomes of Robotic-Assisted Segmentectomy for Early-Stage Non-Small-Cell Lung Cancer. J Clin Med 2025;14:5608. [Crossref] [PubMed]
  66. Seetohul J, Shafiee M, Sirlantzis K. Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions. Sensors (Basel) 2023;23:6202. [Crossref] [PubMed]
Cite this article as: Xu Y, Wang X, Deng B, Liang Z, Wang Z. Comparative analysis of short-term outcomes, inflammatory markers, and complications in robotic-assisted vs. traditional thoracoscopic surgery for complex pulmonary segmentectomy. J Thorac Dis 2026;18(3):222. doi: 10.21037/jtd-2025-aw-2146

Download Citation