Robotic-assisted Optical Navigation System for CT-guided Preoperative Percutaneous Hook-wire Localization of Pulmonary Nodules: a Prospective, Single-center, Single-arm Clinical Study

Robotic-assisted Optical Navigation System for CT-guided Preoperative Percutaneous Hook-wire Localization of Pulmonary Nodules: a Prospective, Single-center, Single-arm Clinical Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robotic-assisted Optical Navigation System for CT-guided Preoperative Percutaneous Hook-wire Localization of Pulmonary Nodules: a Prospective, Single-center, Single-arm Clinical Study Peng Wang, Zhichao Sun, Jiayan Wu, Fengzhou Li, Zhe Sun, Zhuoshi Li, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7297933/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted 12 You are reading this latest preprint version Abstract Background Robotic-assisted navigation systems for the localization of nonvisible and nonpalpable pulmonary nodules have demonstrated feasibility and safety in preclinical animal studies; however, clinical evidence supporting their practical application remains limited. This study aims to evaluate the safety and feasibility of using a robotic-assisted system for computed tomography (CT)-guided percutaneous localization of lung nodules. Methods A total of 137 consecutive patients with 155 nodules were included in the final analysis, all of whom underwent percutaneous hook-wire localization using a novel robotic-assisted optical navigation system. The baseline characteristics of patients and nodules, localization procedure findings, and exploratory outcomes of the correlations between pulmonary nodule features and localization procedure findings were analyzed. Results The localization success rate was 100%. With the assistance of the robotic-assisted optical navigation system, the median number of needle adjustments per target was 0 (ranging from 0 to 2) in this study, with a mean deviation of 1.49 ± 1.93 mm. The mean intervention time was 8.24 ± 1.77 minutes during the robotic-assisted process. Notably, there was no significant change in the accuracy influenced by the location, type, size of nodules, distance to pleura, and decubitus positions. Localization-related complications occurred in 13 (8.39%) out of 155 targets, including 3 (1.94%) minor hemorrhages and 10 (6.45%) minor pneumothoraxes, and no dislodgement was observed in any of the cases. All surgeries were successfully performed with a mean time interval between nodule localization and surgery of 133.67 ± 103.36 minutes. Conclusions This prospective, single-center, single-arm clinical study suggests both feasibility and safety of an innovative robotic-assisted optical navigation system for the CT-guided percutaneous localization of pulmonary nodules using hook-wire technique, as well as satisfactory accuracy during the needle placement. Robotic-assisted CT-guided optical navigation localization pulmonary nodules Figures Figure 1 Figure 2 Figure 3 Introduction Lung cancer remains the predominant cause of incidence and mortality among malignant tumors in China ( 1 ). The widespread utilization of low-dose computed tomography (CT) screening has resulted in an increased detection of small solid nodules or ground-glass nodules in clinical settings ( 2 ). Early detection and surgical removal of these nodules suspected to be lung cancer can significantly reduce mortality associated with the disease ( 3 , 4 ). Video-assisted thoracoscopic surgery (VATS) is widely used for excisional biopsy diagnosis and subsequent radical resection owing to its safety and minimally invasive characteristics ( 5 , 6 ). However, manual palpation is difficult for a portion of small or deeply situated target nodules, especially for ground-glass nodules. Therefore, preoperative precision localization is a crucial step in VATS which can greatly improve the surgical success rates ( 7 – 10 ). Several factors can affect localization accuracy, such as the presence of undetected nodules, manual needle manipulation, respiratory motion, patient movements, changes in target location due to mechanical pressure, and complexity of human anatomy, potentially causing unnecessary radiation exposure, complications, or procedure failures ( 11 , 12 ). Many localization techniques, including CT-guided hook-wire ( 13 – 15 ), microcoil ( 16 – 18 ), fiducial marker ( 19 ), claw-suture ( 20 ), and electromagnetic navigational bronchoscopy ( 10 , 21 , 22 ) have been indicated to be very helpful in clinical practice to enhance localization accuracy. As the innovative technologies development, robot-assisted procedure has been proposed and applied to assist in lung nodule localization. In previous animal studies, a novel CT-guided robot-assisted navigation system demonstrated high accuracy and safety in nodule localization and enabled smaller, more precise resections through CT-based three‑dimensional (3D) reconstruction and optical navigation, facilitated by the process of preoperative planning and intraoperative assistance in needle placement ( 4 , 23 ). Moreover, a prospective pilot study further confirmed the feasibility and safety of this system in clinical practice ( 24 ). This study aimed to combine hook-wire localization with a robotic-assisted optical navigation system for targeting preoperative lung nodules. We shared our findings regarding the safety, feasibility, and accuracy using this novel technique to enhance the evidence in clinical settings. Methods Patient Selection Participants who were candidates for CT-guided preoperative localization of pulmonary nodules at the First Affiliated Hospital of Dalian Medical University from 13 June to 4 December 2024 were enrolled in this study. Inclusion criteria: (a) patients aged 18 or older of any gender, (b) Clinical or imaging diagnosis of solitary or multiple nodules suspected to be malignant requiring localization before surgery, (c) ability to comply with and complete the robotic-assisted CT-guided localization of pulmonary nodules, (d) willingness to participate in this study and provided informed consent. Exclusion criteria: (a) targeted lesions situated in special regions that impeded percutaneous localization, such as the scapular, closing proximity to the mediastinum or major vessels of the heart, (b) participants with severe comorbidities, advanced disease, or deemed unsuitable for surgical intervention. This pilot study was approved by the Ethics Committee for Human Research of the First Affiliated Hospital of Dalian Medical University (No. PJ-KS-KY-2023563). All procedures performed in studies were in accordance with the 1975 Declaration of Helsinki (as revised in 2013), and written informed consent was obtained from all individual participants included in the study. Robotic-assisted Optical Navigation System The robot-assisted optical navigation system (TH-S, TrueHealth Medical Technology Co., Ltd., Hengqin, China) was utilized for this study. This commercially available system, specifically designed for interventional procedures, holds approval from the National Medical Products Administration (NMPA) as a class III medical device, and consists of 3 main components: an optical navigation system, which is utilized to precisely track the actual position and orientation of the patients by detecting the location trackers using wireless technique, also monitors dynamic respiration with a breathing curve on the user interface, indicating the optimal time for needle insertion; a surgical planning system can reconstruct a visualized 3D model encompassing pulmonary nodules, vessels, bronchi, bone structures, and skin, complete the comprehensive planning of needle insertion trajectories as determined by surgeons, and then translate this planning into the physical surgical environment; and a robotic arm positioning and puncture system automatically moved to the target position, following a predetermined trajectory while maintaining stable needle-holding and guiding needle insertion (Fig. 1 ). Robotic-assisted Nodule Localization All robotic-assisted CT-guided localization procedures were performed by 7 (Shilei Zhao, Tao guo, Fengzhou Li, Zhe Sun, Zhuoshi Li, Lei Fang, Xin Shu) associate professors in thoracic surgery who had been engaged in CT-guided intervention for less than 5 years. Patients needed to be well immobilized in a suitable position with a vacuum mattress before the procedure. The physician affixed a positioning tracker to the chest wall of the patient within the intended region. An initial scan CT (Siemens symbia T16 SPECT/CT, SSIEMENS AG, America,130 kV, caredose, slice thickness 1 mm) was acquired and then loaded into the Hisense computer-assisted surgery system (Hisense, Qingdao, China). The Digital Imaging and Communications in Medicine (DICOM) format was required for the CT data. Based on the CT data, the surgical planning system created 3D models and subsequently registered them with the actual position of patients in an automated manner by utilizing the CT image coordinates and the real spatial coordinates from the positioning tracker. Surgeons defined the needle trajectory in the navigation software to reach the target lesions and avoid bones, essential structures, and organs. The robot arm was first simulated moving to the preset position in a 3D virtual scene after rechecking and confirmation of the planned needle path. After a second confirmation of the correct moving path, the robotic arm precisely positioned a specialized needle holder with a proper orientation toward the planned needle insertion trajectory. The hook-wire system (Pulmonary Nodules Localization Needle, SS510-10, Ningbo Senscure Biotechnology Co.,Ltd, Ningbo China) was used for preoperative pulmonary nodule localization. All patients received routine disinfection and local anesthesia. With the assistance of the robotic arm, the surgeon inserted the needle through the skin marker to a predetermined depth and angle, which should normally be within a standard distance measured by the physicians and displayed on the screen. Once the needle was in place, the end effector was loosened to separate it from the needle and withdrew the robotic arm from the target area. A confirmatory CT scan encompassing the entire needle path was conducted to automatically verify the consistency of the actual needle insertion route with the plan and to assess the adequacy of the needle placement when the needle was in place. If the position of the needle exceeded the standard distance of localization requirements, it was recorded as a failure of the robotic-assisted method, and the procedure was transferred into the conventional CT-guided localization without robotic assistance. Subsequently, the hook-wire was delivered around the nodule to anchor the system into the lung, and then the trocar was withdrawn. A final CT scan was performed to determine the existence of localization-related complications and the exact location of the hook-wire. Outcomes Localization success rate, defined as a frequency of the instances in which no exceeding a standard distance from the localizer to the lesion and no dislodgement of anchor occurred, was measured to evaluate the feasibility in the study. The number of needle manipulations required per target until sufficient placement (target reached and intervention feasible) and the deviation of the distance between the needle tip and the target site were recorded to describe and quantify accuracy. Intervention time began with the acquisition of the planning CT scan and ended with the completion of needle placement. Localization-related complications, such as pneumothorax and hemothorax, were monitored and classified as minor or major according to the SIR clinical practice guidelines ( 25 ). Cumulative sum (CUSUM) Learning Curve Analysis The CUSUM method was utilized to thoroughly evaluate the learning curve in the present study using cusum (v0.4.1) R package, which visualized patterns in data by converting raw data into an accumulation of deviations from the average value ( 26 , 27 ). The patients with a single lesion were categorized in chronological order, and the intervention time (performed by the same operator) was collected to calculate the CUSUM value as follows: \(\:{T}_{i}\) and \(\:\stackrel{-}{T}\) indicated individual intervention time and the mean intervention time, respectively. Statistical analysis All statistical analyses were performed using R 4.2.2. Patient and nodule characteristics were summarized using means with standard deviations or medians with ranges for continuous variables. Categorical variables were presented as numbers with percentages. For comparisons of exploratory analysis between two groups or among multiple groups, the Mann-Whitney U-test and Kruskal-Wallis test were used respectively for continuous variables. Spearman rank correlation test was employed to assess correlations between variables, with the corresponding Spearman correlation coefficient (Rs) reported. Jonckheere-Terpstra trend test was utilized to validate the CUSUM learning curve analysis. Statistical significance was set at a P-value less than 0.05. Results Patient and Nodule Baseline Characteristics Between 13 June, 2024 and 31 March, 2025, a total of 142 consecutive patients were initially screened, and after exclusions, 137 patients with 155 targeted nodules were finally included in the study (Figure S1 ). Of all patients, the median age was 61 years (ranging from 26 to 85 years) and 90 (65.69%) were female patients. Most patients [121 (88.32%)] had a single nodule, while 14 (10.22%) and 2 (1.46%) patients had two and three nodules, respectively. The nodules were classified as pure ground-glass nodule (GGN) [92 (59.35%)], mixed GGN [44, (28.39%)], and solid [19, (12.26%)]. The distribution of nodules among the lung lobes was relatively consistent, with 24 (15.48%) in the left lower lobe (LLL), 37 (23.87%) in left upper lobe (LUL), 40 (25.81%) in right lower lobe (RLL), 42 (27.10%) in right upper lobe (RUL), and 12 (7.74%) in right middle lobe (RML). The average diameter of target nodules for localization was 11.14 ± 4.57 mm, and the mean distance from the nodules to the pleura was 11.77 ± 9.30 mm (Table 1 ). Table 1 Baseline Characteristics of Participants and Nodules Characteristics Values Per patient (n = 137) Age (years) Median [Min, Max] 61.00 [26.00, 85.00] Gender (n,%) Female 90 (65.69%) Male 47 (34.31%) Number of nodules (n,%) One nodule 121 (88.32%) Two nodules 14 (10.22%) Three nodules 2 (1.46%) Per lesion (n = 155) Nodule size (mm) Mean (SD) 11.14 ± 4.57 Median [Min, Max] 11.00 [2.00, 38.00] Distance to pleura (mm) Mean (SD) 11.77 ± 9.30 Missing 1 Location of nodiles (n,%) LLL 24 (15.48%) LUL 37 (23.87%) RLL 40 (25.81%) RML 12 (7.74%) RUL 42 (27.10%) Type of nodules (n,%) Solid 19 (12.26%) Pure GGN 92 (59.35%) Mixed GGN 44 (28.39%) Characteristics are shown as median [range], mean ± SD, or number (%). LLL, left lower lobe; LUL, left upper lobe; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; GGN, ground-glass nodule; SD, standard deviation. Localization Procedure Outcomes All 155 nodules were successfully localized using the robot-assisted optical navigation system, resulting in a localization success rate of 100%. The median number of needle replacements was 0 (ranging from 0 to 2), with a mean deviation between the needle tip and the target point of 1.49 ± 1.93 mm. Regarding the procedural efficiency, the mean duration from the initial CT scan to the needle being achieved in place was 8.24 ± 1.77 minutes. Furthermore, 10 (6.45%) cases developed minor pneumothoraxes and 3 (1.94%) cases experienced minor hemorrhage, with no deaths or other severe adverse events being observed during the robotic-assisted localization procedure, and none of the cases with complications required any intervention. Moreover, no dislodgement was reported in any of the cases (Table 2 ). Table 2 Technical Characteristics and Outcomes of Robotic-assisted Localization Procedures Characteristics Values Per lesion (n = 155) Decubitus position (n,%) Lateral 71 (45.81%) Prone 59 (38.06%) Supine 22 (14.19%) LAO 2 (1.29%) RAO 1 (0.65%) Depth (mm) Mean (SD) 74.21 ± 22.74 Localization success (n,%) 155 (100%) Intervention time (min) Mean (SD) 8.24 ± 1.77 Number of needle repositionings Median [Min, Max] 0 [0, 2.00] 0 129 (83.23%) 1 24 (15.48%) 2 2 (1.29%) Deviation (mm) Mean (SD) 1.49 ± 1.93 Complications (n,%) None 142 (91.61%) Minor pneumothorax 10 (6.45%) Minor hemorrhage 3 (1.94%) Characteristics are shown as median [range], mean ± SD, or number (%). LAO, left anterior oblique; RAO, right anterior oblique; SD, standard deviation. Stratified analyses by location, type, size, and distance to the pleura of nodules, as well as various decubitus positions, were conducted to evaluate accuracy as exploratory findings. Notably, the deviation of the localizer was found to have no significant correlation with characteristics of nodules including location of nodules, type of nodules, and decubitus positions, also in size and distance to pleura of nodules (P > 0.05) (Figure S2 , S3, and Table S1 ). In addition, a successful localization case using the robotic-assisted optical navigation system with a high level of localization difficulty was shown in Fig. 2 . Surgery and Pathological Outcomes Following the successful localization of pulmonary nodules, all patients were transferred to the operating room to perform VATS. The mean time interval between nodule localization and surgery was 133.67 ± 103.36 minutes. All targeted nodules were resected successfully and the final definitive pathologic tissue results were obtained. Among the 155 lesions, 115 (74.19%) underwent wedge resection and lymph node sampling, 21 (13.55%) underwent segmentectomy and systemic lymph node dissection, and 19 lesions (12.26%) were later converted to lobectomy due to the determination of an invasive stage based on the intraoperative frozen section analysis with the specific micropapillary and solid histological patterns. Histopathological examination revealed 18 (11.61%) benign nodules, 35 (22.58%) precancerous lesion, and 102 (65.81%) malignant nodules. Of the 35 precancerous lesion, all of them were diagnosed as adenocarcinoma in situ (AIS). Of the 102 malignant nodules, 55 (35.48%) were diagnosed as minimally invasive adenocarcinoma (MIA), 42 (27.10%) as invasive adenocarcinoma (IA), 4 (2.58%) as mucinous pulmonary adenocarcinoma (MPA), and 1 (0.65%) as non-hodgkin lymphoma (NHL) (Table 3 ). Table 3 Surgery and Pathological Outcomes Characteristics Values Per patient (n = 137) Interval_time (min) 133.67 ± 103.36 Per lesion (n = 155) Type of resection (n,%) Wedge resection 115 (74.19%) Segmentectomy 21 (13.55%) Lobectomy 19 (12.26%) Pathology (n,%) Benign lesion 18 (11.61%) AIS 35 (22.58%) MIA 55 (35.48%) IA 42 (27.10%) MPA 4 (2.58%) NHL 1 (0.65%) Characteristics are shown as mean ± SD and number (%). AIS, adenocarcinoma in situ ; MIA, minimally invasive adenocarcinoma; IA, invasive adenocarcinoma; MPA, mucinous pulmonary adenocarcinoma; NHL, non-hodgkin lymphoma. Learning Curve Evaluation A total of 121 patients with a single lesion (performed by the same operator) were categorized in chronological order, and their intervention times were collected to calculate the CUSUM values and conduct further validation analyses (Fig. 3 ). As shown in Fig. 3 A, two pivotal cutoff points emerged at the 48th and 83rd cases, dividing the learning curve into three distinct phases including Phase I (the initial 48 cases), Phase II (cases 48th to 83rd ), and Phase III (after the 83rd case). During Phase I, the intervention time surpassed the average, and the learning curve demonstrated a steep upward trend, indicating a notable increase in preoperative localization procedural duration. In Phase II, the intervention time continued to exceed the average, with the learning curve exhibiting a gradually ascending trend, suggesting a decelerated rate of improvement when compared to the initial phase. Upon reaching the 83rd case, the intervention time transitioned to a state of falling below the average and displayed a consistent downward trend in Phase III. Further validation analyses demonstrated that the intervention time and the localization deviation were significantly decreased across the three phases (Fig. 3 B, 3 C). This visualization illustrates the evolution of localization efficiency and accuracy with increasing experience. Discussion Preoperative precise localization of pulmonary nodules is crucial for the diagnosis and treatment of suspected malignancies. Conversely, inaccurate localization, especially in small or deeply situated target nodules, can contribute to repeated puncture attempts, extended surgical duration, increased incidence of complications, and even positive resection margins or failure of the nodule resection ( 5 , 28 ). This study utilized a novel robotic-assisted optical navigation system that could effectively compensate for the aforementioned deficiencies, achieving successful preoperative localization of 155 nodules in 137 patients and resulting in the successful resection of all nodules through VATS with oncologically safe margins. The present study revealed the safety and feasibility of using the robotic-assisted optical navigation system for CT-guided percutaneous localization of pulmonary nodules by hook-wire technique. Prior research has indicated that the success rate of CT-guided hook-wire localization can range from 64.7–99.4%, depending on the studies ( 29 – 35 ), whereas an encouraging 100% successful localization rate was achieved in this study. The average deviation between the needle tip and target point in this study was 1.49 mm, which was significantly lower than the average deviation of 13.76 mm reported in a study using a freehand hook-wire technique for CT-guided preoperative localization of pulmonary nodules ( 36 ). Another study reported an average deviation of 4.6 mm and a mean number of needle replacements of 2.1 in the freehand hook-wire localization group, both of which exceeded the values in this study, where the mean number of needle replacements was 0.2 and the deviation was as previously mentioned ( 29 ). Furthermore, the conventional freehand hook-wire localization had a much higher rate of pneumothorax and hemorrhage reported in previous studies, with almost 40% and 20% of cases, respectively, whereas the robotic-assisted optical navigation system only had a rate of 6.45% and 1.94% for pneumothorax and hemorrhage in our findings ( 37 – 39 ). These discrepancies might be caused by traditional CT-guided method often presenting significant challenges due to factors including the reliance on experience of surgeons, the precision required, and the complexity of human anatomy, which may necessitate iterative adjustments in needle depth and angle, a higher complication rate, and even failure; conversely, the robot-assisted optical navigation system used in this study can offer several critical advantages, such as the reconstruction of a visualized 3D model of various anatomical components, precise tracking of the actual position and orientation of the patient, comprehensive planning of trajectories, real-time recording of respiratory motion, and stable needle-holding and guiding needle insertion. In recent prospective clinical studies, this robotic-assisted system has demonstrated its ability to enhance puncture accuracy, reduce the need for needle corrections during percutaneous puncture procedures, and lower the incidence of complications compared to traditional manual insertion ( 40 , 41 ). In recent years, a series of robot-assisted systems were developed for CT-guided minimally invasive diagnosis and treatment to enhance the efficiency of medical practitioners, and to ensure high surgical precision and safety; however, these systems face varying degrees of technical challenges or inherent limitations. Zerobot® (Okayama, Japan), a remote-controlled robot guided by real-time CT fluoroscopy, has demonstrated the feasibility of needle insertion, whereas it has significant drawbacks, including being incompatible with most conventional CT scanners due to the robot arm operating inside the gantry and not widely available for routine use of the fluoroscopy module ( 42 – 44 ). MAXIO (Perfint Healthcare, Chennai, India), one of the earliest commercialized robots for percutaneous interventional procedures, can provide mechanical guidance according to the surgical plan, whereas patients need to remain still due to the absence of tracking or compensating for their movements throughout the entire procedure ( 15 , 45 – 47 ). Epione (Quantum Surgical, Montpellier, France), another Food and Drug Administration (FDA)-approved robot-assisted navigation system, is similar to the robot-assisted system utilized in this study, with similar components and operational procedures except for a rigid marker bracket attached to the patient, which may not conform well to body contours and stably track the patients' movements through the optical camera ( 48 – 50 ). Some other robotic-assisted systems are still in the preliminary research stage or have yet to be commercialized ( 11 , 50 ). The robot-assisted optical navigation system used in this study, compared to the existing robotic systems, may potentially be a better choice for lung nodule localization. The clinical trials of percutaneous localization of lung nodules using robotic-assisted systems are still limited ( 15 , 24 ). In a retrospective analysis of 654 cases involving CT-guided percutaneous localization of pulmonary nodules using the MAXIO robot with a modified hook-wire needle, the successful localization rate was 96.64%, with an average deviation of 5.01 mm, a mean marking time of 22.85 minutes, a pneumothorax rate of 27.21%, and a parenchymal hemorrhage rate of 33.94%, indicating a relatively subpar performance in technical characteristics and outcomes of localization procedures compared with this study ( 15 ). Another clinical pilot study employing the same robotic-assisted navigation system as in this study also demonstrated its safety and feasibility for achieving precise percutaneous lung nodule localization using indocyanine green (ICG) technique, with an impressive first-pass success rate of 100%, a median deviation of 6.1 mm, and no significant complications other than 4 cases of asymptomatic pneumothoraxes ( 24 ). It also reported that the accuracy of the robotic-assisted navigation system was not influenced by location of nodules, distance to the pleura, or decubitus positions, consistent with the findings in this study, demonstrating the stability of the robotic-assisted system and its potential for guiding nodule localization with more accuracy, fewer postoperative complications, and shorter procedural duration in various clinical settings of localization techniques. The CUSUM analysis suggested that the learning curve for robotic-assisted CT-guided percutaneous pulmonary nodule localization using hook-wire needles comprised three distinct phases. These phases of the learning curve were identified by the presence of two cutoff points (case 48 and case 83) at which the inclination and the trend of the curves sharply changed. As mentioned above, the CUSUM curve reflected the sequential changes compared to an average value, so the presence of two cutoff points in the CUSUM curve indicates two critical points in terms of preoperative localization procedural duration changes during the learning process. In this study, the Phase I, as an initial learning period, involved gradually acquiring the technical competence and skills to significantly reduce intervention time (1st -48th case). The Phase II, as a consolidation period, could be interpreted as the accumulation of additional experience after the initial learning curve and as the consolidation of the experience gained during the first phase (49th -83rd case). The Phase III represented the mastery period, suggesting the acquisition of a higher competence in the execution of this preoperative localization procedure under robotic guidance (84th -121st case). A prior study on learning processes of CT-guided interventional radiology under robotic assistance showed that two operators, inexperienced initially, cautiously learned preoperative lung nodule localization under a robotic-guided system and almost simultaneously reached phases I and II in the 50th and 90th cases ( 15 ), similar to the findings of the present study. Moreover, the intervention time and localization deviation significantly decreased across the three distinct phases, in line with expectations. These learning curve findings could help to less experienced physicians accurately understand the benefit of using the robotic-assisted optical navigation system in CT-guided percutaneous interventional procedures. The present study still had several limitations that should be acknowledged. First, this is a single center study, which limits the generalizability of the findings. Second, the lack of a freehand control group makes it difficult to accurately determine the superiority derived from this system. Further large-scale randomized studies are warranted for a more comprehensive evaluation of the currently available CT-guided robotic-assisted optical navigation system. Conclusion To our knowledge, this study is the first report of using an optical navigation robotic-assisted system for percutaneous lung nodule localization by hook-wire. The current findings demonstrate the safety, feasibility and promising potential of this technique, while also provides an additional evidence supporting the clinical practice of using this system. Regarding the limitations of this study, further randomized clinical trials are essential to validate its advantages compared to conventional manual localization. Nevertheless, It can be concluded that the robotic-assisted optical navigation system is both safe and effective in clinical practice. Declarations Acknowledgments The authors thank the National Natural Science Foundation of China for invaluable assistance of this study. Data availability statement The data that support the findings of this study are not openly available due to reasons of sensitivity and will require approval from the First Affiliated Hospital of Dalian Medical University. Data are located in controlled access data storage at the department of thoracic surgery from the First Affiliated Hospital of Dalian Medical University. Ethical Statement The study was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (Project identification code: PJ-KS-KY-2023563), and written informed consent was obtained from all participating patients. All procedures performed in studies involving human participants were in accordance with the ethical standards of institutional and/or research committee and with the 1975 Declaration of Helsinki, as revised in 2013. Declaration of competing interest All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. CRediT authorship contribution statement Peng Wang ( [email protected] ): Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Conceptualization. Zhichao Sun ( [email protected] ): Writing – review & editing, Writing – original draft, Validation, Investigation, Data curation. Jiayan Wu ( [email protected] ): Methodology, Investigation. Fengzhou Li ( [email protected] ), Zhe Sun ( [email protected] ), Zhuoshi Li (11515707082qq.com), Changsheng Lv ( [email protected] ), Tao Guo ( [email protected] ), Xin Shu ( [email protected] ), Lei Fang ( [email protected] ), Jiawei Wang ( [email protected] ), Jin Wang ( [email protected] ), Lei Zhao ( [email protected] ), Fachen Zhou ( [email protected] ): Methodology, Investigation. Chundong Gu ( [email protected] ) and Shilei Zhao ( [email protected] ): Writing – review & editing, Writing – original draft, Project administration, Methodology, Formal analysis, Conceptualization. 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Wang L, He J, Zhang L, Chen C, Chen B, Shen W. A novel preoperative image-guided localization for small pulmonary nodule resection using a claw-suture device. Sci Rep. 2023;13(1):18950. Zhang J, He J, Chen J, Zhong Y, He J, Li S. Application of indocyanine green injection guided by electromagnetic navigation bronchoscopy in localization of pulmonary nodules. Transl Lung Cancer Res. 2021;10(12):4414–22. Wang J, Huang H, Xue Q, Geraci TC, Ruan Z, Ma H. Preoperative localization of pulmonary nodules by electromagnetic navigation bronchoscopy combined with methylene blue injection. J Thorac Dis. 2024;16(9):6196–203. Duan X, He R, Jiang Y, et al. Robot-assisted navigation for percutaneous localization of peripheral pulmonary nodule: an in vivo swine study. Quant Imaging Med Surg. 2023;13(12):8020–30. Liu J, Jiang Y, He R, et al. Robotic-assisted navigation system for preoperative lung nodule localization: a pilot study. Transl Lung Cancer Res. 2023;12(11):2283–93. Sacks D, McClenny TE, Cardella JF, Lewis CA. Society of Interventional Radiology clinical practice guidelines. J Vasc Interv Radiol. 2003;14(9 Pt 2):S199–202. Han SH, Kim YC, Kwon TK, Lee DY. Cumulative Sum Analysis of the Learning Curve of Free Flap Reconstruction in Head and Neck Cancer Patients. Clin Exp Otorhinolaryngol. 2022;15(2):177–82. Parisi A, Scrucca L, Desiderio J, et al. Robotic right hemicolectomy: Analysis of 108 consecutive procedures and multidimensional assessment of the learning curve. Surg Oncol. 2017;26(1):28–36. Nakashima S, Watanabe A, Obama T, Yamada G, Takahashi H, Higami T. Need for preoperative computed tomography-guided localization in video-assisted thoracoscopic surgery pulmonary resections of metastatic pulmonary nodules. Ann Thorac Surg. 2010;89(1):212–8. Zhang X, Tsauo J, Tian P, et al. Randomized comparison of the four-hook anchor device and hook-wire use for the preoperative localization of pulmonary nodules. J Thorac Cardiovasc Surg. 2024;167(2):498–e5072. Yin C, Chen Y, Zhang R, et al. Analysis of complication risk factors in preoperative computed tomography-guided hookwire location of pulmonary nodules. Eur J Med Res. 2024;29(1):369. Zhou WJ, Chen G, Huang YY, Peng P, Lv PH, Lv JL. Preoperative computed tomography-guided localization for pulmonary nodules: comparison between hook-wire and anchored needle localization. Wideochir Inne Tech Maloinwazyjne. 2024;19(1):91–9. Han R, Wang LF, Teng F, et al. Presurgical computed tomography-guided localization of lung ground glass nodules: comparing hook-wire and indocyanine green. World J Surg Oncol. 2024;22(1):51. He R, Ming C, Lei Y, et al. Preoperative pulmonary nodule localization: A comparison of hook wire and Lung-pro-guided surgical markers. Clin Respir J. 2024;18(1):e13726. Sun X, Fu J, Ma C, et al. CT-guided microcoil versus hook-wire localization of pulmonary nodule prior to video-assisted thoracoscopic surgery without fluoroscopic guidance. BMC Pulm Med. 2024;24(1):492. Chai J, Chu S, Wei N, et al. Computed tomography-guided hookwire localization and medical glue combined with methylene blue localization for pulmonary nodules before video-assisted thoracoscopic surgery: a single-center, retrospective study. Quant Imaging Med Surg. 2023;13(9):6228–40. Li Z, Zhou Z, Feng K, et al. Comparison of laser guidance and freehand hook-wire for CT-guided preoperative localization of pulmonary nodules. J Cardiothorac Surg. 2024;19(1):182. Kleedehn M, Kim DH, Lee FT, et al. Preoperative Pulmonary Nodule Localization: A Comparison of Methylene Blue and Hookwire Techniques. AJR Am J Roentgenol. 2016;207(6):1334–9. Park CH, Lee SM, Lee JW, et al. Hook-wire localization versus lipiodol localization for patients with pulmonary lesions having ground-glass opacity. J Thorac Cardiovasc Surg. 2020;159(4):1571–e15792. Zhang H, Li Y, Chen X, He Z. Comparison of hook-wire and medical glue for CT-guided preoperative localization of pulmonary nodules. Front Oncol. 2022;12:922573. Jing Y, Zhang J, Jin Y, Bai X. Evaluation of robotic-assisted navigation system for CT-guided thoracic and abdominal lesion puncture: A prospective clinical study. J Cancer Res Ther. 2024;20(4):1350–6. Jing Y, Jing J, Liu J, Zhang J, Jin Y, Bai X. The clinical performance of robotic assisted navigation system versus conventional freehand technique for percutaneous transthoracic needle biopsy. Sci Rep. 2025;15(1):5980. Published 2025 Feb 18. Hiraki T, Kamegawa T, Matsuno T, et al. Robotically Driven CT-guided Needle Insertion: Preliminary Results in Phantom and Animal Experiments. Radiology. 2017;285(2):454–61. Hiraki T, Kamegawa T, Matsuno T, Komaki T, Sakurai J, Kanazawa S. Zerobot®: A Remote-controlled Robot for Needle Insertion in CT-guided Interventional Radiology Developed at Okayama University. Acta Med Okayama. 2018;72(6):539–46. Hiraki T, Kamegawa T, Matsuno T, et al. Robotic needle insertion during computed tomography fluoroscopy-guided biopsy: prospective first-in-human feasibility trial. Eur Radiol. 2020;30(2):927–33. Koethe Y, Xu S, Velusamy G, Wood BJ, Venkatesan AM. Accuracy and efficacy of percutaneous biopsy and ablation using robotic assistance under computed tomography guidance: a phantom study. Eur Radiol. 2014;24(3):723–30. Schaible J, Pregler B, Verloh N, et al. Improvement of the primary efficacy of microwave ablation of malignant liver tumors by using a robotic navigation system. Radiol Oncol. 2020;54(3):295–300. Johnston EW, Basso J, Silva F, et al. Robotic versus freehand CT-guided radiofrequency ablation of pulmonary metastases: a comparative cohort study. Int J Comput Assist Radiol Surg. 2023;18(10):1819–28. Guiu B, De Baère T, Noel G, Ronot M. Author Correction: Feasibility, safety and accuracy of a CT-guided robotic assistance for percutaneous needle placement in a swine liver model. Sci Rep. 2021;11(1):8241. de Baère T, Roux C, Deschamps F, Tselikas L, Guiu B. Evaluation of a New CT-Guided Robotic System for Percutaneous Needle Insertion for Thermal Ablation of Liver Tumors: A Prospective Pilot Study. Cardiovasc Intervent Radiol. 2022;45(11):1701–9. Bonnet B, de Baère T, Beunon P, Feddal A, Tselikas L, Deschamps F. Robotic-assisted CT-guided percutaneous thermal ablation of abdominal tumors: An analysis of 41 patients. Diagn Interv Imaging. 2024;105(6):227–32. Zhang W, Xia P, Liu S, et al. A coordinate positioning puncture method under robot-assisted CT-guidance: phantom and animal experiments. Minim Invasive Ther Allied Technol. 2022;31(2):206–15. Additional Declarations No competing interests reported. Supplementary Files 6.FigureS1.png 7.FigureS2.png 8.FigureS3.png Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviews received at journal 01 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 16 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 06 Aug, 2025 First submitted to journal 05 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7297933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501737485,"identity":"943bec50-ace9-4e7e-aa16-3618ed23af04","order_by":0,"name":"Peng Wang","email":"","orcid":"","institution":"The First Afliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Wang","suffix":""},{"id":501737486,"identity":"d6e6028b-273d-4766-be40-7131266a8d3d","order_by":1,"name":"Zhichao Sun","email":"","orcid":"","institution":"The First Afliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhichao","middleName":"","lastName":"Sun","suffix":""},{"id":501737488,"identity":"ede0a296-f846-4661-a592-96bc1db73c26","order_by":2,"name":"Jiayan Wu","email":"","orcid":"","institution":"True Health Medical Technology Co. 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Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYHACNiBmBuEDEmD+AeK1sCWQrIXHgDgt8hHJzx7z7rCWM+df8/HWzTYGOb4bCYyfC/BoMbyRZm7Meybd2HLG283WuW0MxpI3EpilZ+DTMiOHTZq37XDihhtnt0kDtQAZCWzMPMRpOfMMpKWeoBZ5CZiW8z1sIC0JBoS0GPA8M5Oc25ZubHCDzdg655yE4cwzD5ul8drSnvxM4m2btZzB+cMPb+eU2cjzHU8++BmvLQdgLIkEMAnEjA14NABtgUvzH8CtahSMglEwCkY2AAB1XEqxhyAZgAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Afliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chundong","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2025-08-05 08:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7297933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7297933/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12957-026-04204-x","type":"published","date":"2026-01-21T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89981458,"identity":"b24e7af9-dcc0-44c4-9f72-9f8f0c6f370c","added_by":"auto","created_at":"2025-08-27 06:25:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2710285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRobotic-assisted optical navigation system.\u003c/strong\u003e The optical navigation system allowed for precise tracking of the position of the patient and the robotic arm (yellow arrow). The surgical planning system was used to reconstruct a three-dimensional model and plan the needle insertion trajectory (red arrow). The robotic arm positioning and puncture system was employed for stable needle-holding and guiding needle insertion (green arrow).\u003c/p\u003e","description":"","filename":"2.Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/532e676348c59deb04165a16.png"},{"id":89979158,"identity":"c0e7d364-73dd-4fbe-8a7b-34e6c9f033c1","added_by":"auto","created_at":"2025-08-27 06:17:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2601693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSuccessful localization of a challenging lesion in a 59-year-old female patient. \u003c/strong\u003eBased on the optimal puncture trajectory, the prone position was selected, and the puncture site was located through the scapular region. A diagonal trajectory was chosen to avoid obstruction by the scapula and the dorsal segment of the left lower lobe. (A) Initial CT showed a pulmonary nodule, approximately 15 mm × 11 mm in size, in the left upper lobe that was obscured by the scapula and the dorsal segment tissue of the left lower lobe. (B) 3D reconstruction of the thoracic structure (sagittal view). (C) 3D reconstruction of the thoracic structure (coronal view). (D) Needle trajectory planning based on the initial CT scan (sagittal view). (E) Needle trajectory planning based on the initial CT scan (coronal view). (F) CT scan obtained after localization procedure.\u003c/p\u003e","description":"","filename":"3.Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/a05f80357d2a308a154a02c6.png"},{"id":89979167,"identity":"673a9779-7da5-41a0-948f-7714c020b938","added_by":"auto","created_at":"2025-08-27 06:17:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":190132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLearning Curve Evaluation. \u003c/strong\u003e(A) The CUSUM curve of intervention time. As shown, two cutoff points at the 48\u003csup\u003eth\u003c/sup\u003e and 83\u003csup\u003erd\u003c/sup\u003e cases divided the learning curve into three phases: Phase I (the initial 48 cases), Phase II (cases 48\u003csup\u003eth\u003c/sup\u003e to 83\u003csup\u003erd\u003c/sup\u003e), and Phase III (after the 83\u003csup\u003erd\u003c/sup\u003e case). Validation analyses of (B) intervention time and (C) localization deviation across the three phases based on the CUSUM learning curve.\u003c/p\u003e","description":"","filename":"4.Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/13381d8bbc308bc8b0b75ad4.png"},{"id":101151751,"identity":"5d4f1947-a008-4e77-b6d5-4d3f5e6a9b00","added_by":"auto","created_at":"2026-01-26 16:04:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8033873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/f9891aa1-48fd-46f4-ba47-49d1fade326e.pdf"},{"id":89979157,"identity":"de4c4885-48da-4b08-9f81-361fca50e693","added_by":"auto","created_at":"2025-08-27 06:17:30","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1445168,"visible":true,"origin":"","legend":"","description":"","filename":"6.FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/35b070d8ae902261a11123e0.png"},{"id":89981459,"identity":"b7a39f1d-6175-4737-a9be-cc9c216743ce","added_by":"auto","created_at":"2025-08-27 06:25:30","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":160515,"visible":true,"origin":"","legend":"","description":"","filename":"7.FigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/cba59d8492895d02083447be.png"},{"id":89981460,"identity":"71761604-ff0d-47c7-a22d-57c833f4dbee","added_by":"auto","created_at":"2025-08-27 06:25:30","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":152311,"visible":true,"origin":"","legend":"","description":"","filename":"8.FigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-7297933/v1/235fb57c32fc572fd1adc293.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robotic-assisted Optical Navigation System for CT-guided Preoperative Percutaneous Hook-wire Localization of Pulmonary Nodules: a Prospective, Single-center, Single-arm Clinical Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the predominant cause of incidence and mortality among malignant tumors in China (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The widespread utilization of low-dose computed tomography (CT) screening has resulted in an increased detection of small solid nodules or ground-glass nodules in clinical settings (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Early detection and surgical removal of these nodules suspected to be lung cancer can significantly reduce mortality associated with the disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Video-assisted thoracoscopic surgery (VATS) is widely used for excisional biopsy diagnosis and subsequent radical resection owing to its safety and minimally invasive characteristics (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, manual palpation is difficult for a portion of small or deeply situated target nodules, especially for ground-glass nodules. Therefore, preoperative precision localization is a crucial step in VATS which can greatly improve the surgical success rates (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral factors can affect localization accuracy, such as the presence of undetected nodules, manual needle manipulation, respiratory motion, patient movements, changes in target location due to mechanical pressure, and complexity of human anatomy, potentially causing unnecessary radiation exposure, complications, or procedure failures (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Many localization techniques, including CT-guided hook-wire (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), microcoil (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), fiducial marker (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), claw-suture (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), and electromagnetic navigational bronchoscopy (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) have been indicated to be very helpful in clinical practice to enhance localization accuracy.\u003c/p\u003e\u003cp\u003e As the innovative technologies development, robot-assisted procedure has been proposed and applied to assist in lung nodule localization. In previous animal studies, a novel CT-guided robot-assisted navigation system demonstrated high accuracy and safety in nodule localization and enabled smaller, more precise resections through CT-based three‑dimensional (3D) reconstruction and optical navigation, facilitated by the process of preoperative planning and intraoperative assistance in needle placement (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Moreover, a prospective pilot study further confirmed the feasibility and safety of this system in clinical practice (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This study aimed to combine hook-wire localization with a robotic-assisted optical navigation system for targeting preoperative lung nodules. We shared our findings regarding the safety, feasibility, and accuracy using this novel technique to enhance the evidence in clinical settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePatient Selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Participants who were candidates for CT-guided preoperative localization of pulmonary nodules at the First Affiliated Hospital of Dalian Medical University from 13 June to 4 December 2024 were enrolled in this study. Inclusion criteria: (a) patients aged 18 or older of any gender, (b) Clinical or imaging diagnosis of solitary or multiple nodules suspected to be malignant requiring localization before surgery, (c) ability to comply with and complete the robotic-assisted CT-guided localization of pulmonary nodules, (d) willingness to participate in this study and provided informed consent. Exclusion criteria: (a) targeted lesions situated in special regions that impeded percutaneous localization, such as the scapular, closing proximity to the mediastinum or major vessels of the heart, (b) participants with severe comorbidities, advanced disease, or deemed unsuitable for surgical intervention.\u003c/p\u003e\u003cp\u003e This pilot study was approved by the Ethics Committee for Human Research of the First Affiliated Hospital of Dalian Medical University (No. PJ-KS-KY-2023563). All procedures performed in studies were in accordance with the 1975 Declaration of Helsinki (as revised in 2013), and written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRobotic-assisted Optical Navigation System\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe robot-assisted optical navigation system (TH-S, TrueHealth Medical Technology Co., Ltd., Hengqin, China) was utilized for this study. This commercially available system, specifically designed for interventional procedures, holds approval from the National Medical Products Administration (NMPA) as a class III medical device, and consists of 3 main components: an optical navigation system, which is utilized to precisely track the actual position and orientation of the patients by detecting the location trackers using wireless technique, also monitors dynamic respiration with a breathing curve on the user interface, indicating the optimal time for needle insertion; a surgical planning system can reconstruct a visualized 3D model encompassing pulmonary nodules, vessels, bronchi, bone structures, and skin, complete the comprehensive planning of needle insertion trajectories as determined by surgeons, and then translate this planning into the physical surgical environment; and a robotic arm positioning and puncture system automatically moved to the target position, following a predetermined trajectory while maintaining stable needle-holding and guiding needle insertion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRobotic-assisted Nodule Localization\u003c/b\u003e\u003c/p\u003e\u003cp\u003e All robotic-assisted CT-guided localization procedures were performed by 7 (Shilei Zhao, Tao guo, Fengzhou Li, Zhe Sun, Zhuoshi Li, Lei Fang, Xin Shu) associate professors in thoracic surgery who had been engaged in CT-guided intervention for less than 5 years. Patients needed to be well immobilized in a suitable position with a vacuum mattress before the procedure. The physician affixed a positioning tracker to the chest wall of the patient within the intended region. An initial scan CT (Siemens symbia T16 SPECT/CT, SSIEMENS AG, America,130 kV, caredose, slice thickness 1 mm) was acquired and then loaded into the Hisense computer-assisted surgery system (Hisense, Qingdao, China). The Digital Imaging and Communications in Medicine (DICOM) format was required for the CT data. Based on the CT data, the surgical planning system created 3D models and subsequently registered them with the actual position of patients in an automated manner by utilizing the CT image coordinates and the real spatial coordinates from the positioning tracker. Surgeons defined the needle trajectory in the navigation software to reach the target lesions and avoid bones, essential structures, and organs. The robot arm was first simulated moving to the preset position in a 3D virtual scene after rechecking and confirmation of the planned needle path. After a second confirmation of the correct moving path, the robotic arm precisely positioned a specialized needle holder with a proper orientation toward the planned needle insertion trajectory.\u003c/p\u003e\u003cp\u003e The hook-wire system (Pulmonary Nodules Localization Needle, SS510-10, Ningbo Senscure Biotechnology Co.,Ltd, Ningbo China) was used for preoperative pulmonary nodule localization. All patients received routine disinfection and local anesthesia. With the assistance of the robotic arm, the surgeon inserted the needle through the skin marker to a predetermined depth and angle, which should normally be within a standard distance measured by the physicians and displayed on the screen. Once the needle was in place, the end effector was loosened to separate it from the needle and withdrew the robotic arm from the target area. A confirmatory CT scan encompassing the entire needle path was conducted to automatically verify the consistency of the actual needle insertion route with the plan and to assess the adequacy of the needle placement when the needle was in place. If the position of the needle exceeded the standard distance of localization requirements, it was recorded as a failure of the robotic-assisted method, and the procedure was transferred into the conventional CT-guided localization without robotic assistance. Subsequently, the hook-wire was delivered around the nodule to anchor the system into the lung, and then the trocar was withdrawn. A final CT scan was performed to determine the existence of localization-related complications and the exact location of the hook-wire.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLocalization success rate, defined as a frequency of the instances in which no exceeding a standard distance from the localizer to the lesion and no dislodgement of anchor occurred, was measured to evaluate the feasibility in the study. The number of needle manipulations required per target until sufficient placement (target reached and intervention feasible) and the deviation of the distance between the needle tip and the target site were recorded to describe and quantify accuracy. Intervention time began with the acquisition of the planning CT scan and ended with the completion of needle placement. Localization-related complications, such as pneumothorax and hemothorax, were monitored and classified as minor or major according to the SIR clinical practice guidelines (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCumulative sum (CUSUM) Learning Curve Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe CUSUM method was utilized to thoroughly evaluate the learning curve in the present study using cusum (v0.4.1) R package, which visualized patterns in data by converting raw data into an accumulation of deviations from the average value (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The patients with a single lesion were categorized in chronological order, and the intervention time (performed by the same operator) was collected to calculate the CUSUM value as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"161\" height=\"27\"\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{T}\\)\u003c/span\u003e\u003c/span\u003e indicated individual intervention time and the mean intervention time, respectively.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R 4.2.2. Patient and nodule characteristics were summarized using means with standard deviations or medians with ranges for continuous variables. Categorical variables were presented as numbers with percentages. For comparisons of exploratory analysis between two groups or among multiple groups, the \u003cem\u003eMann-Whitney U-test\u003c/em\u003e and \u003cem\u003eKruskal-Wallis test\u003c/em\u003e were used respectively for continuous variables. \u003cem\u003eSpearman rank correlation test\u003c/em\u003e was employed to assess correlations between variables, with the corresponding Spearman correlation coefficient (Rs) reported. \u003cem\u003eJonckheere-Terpstra trend test\u003c/em\u003e was utilized to validate the CUSUM learning curve analysis. Statistical significance was set at a \u003cem\u003eP-value\u003c/em\u003e less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePatient and Nodule Baseline Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBetween 13 June, 2024 and 31 March, 2025, a total of 142 consecutive patients were initially screened, and after exclusions, 137 patients with 155 targeted nodules were finally included in the study (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Of all patients, the median age was 61 years (ranging from 26 to 85 years) and 90 (65.69%) were female patients. Most patients [121 (88.32%)] had a single nodule, while 14 (10.22%) and 2 (1.46%) patients had two and three nodules, respectively. The nodules were classified as pure ground-glass nodule (GGN) [92 (59.35%)], mixed GGN [44, (28.39%)], and solid [19, (12.26%)]. The distribution of nodules among the lung lobes was relatively consistent, with 24 (15.48%) in the left lower lobe (LLL), 37 (23.87%) in left upper lobe (LUL), 40 (25.81%) in right lower lobe (RLL), 42 (27.10%) in right upper lobe (RUL), and 12 (7.74%) in right middle lobe (RML). The average diameter of target nodules for localization was 11.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57 mm, and the mean distance from the nodules to the pleura was 11.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30 mm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of Participants and Nodules\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValues\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePer patient (n\u0026thinsp;=\u0026thinsp;137)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian [Min, Max]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.00 [26.00, 85.00]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (65.69%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (34.31%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of nodules (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne nodule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (88.32%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo nodules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (10.22%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree nodules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.46%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePer lesion (n\u0026thinsp;=\u0026thinsp;155)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodule size (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian [Min, Max]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.00 [2.00, 38.00]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to pleura (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation of nodiles (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLLL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (15.48%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (23.87%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRLL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (25.81%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (7.74%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRUL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (27.10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of nodules (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (12.26%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePure GGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (59.35%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed GGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (28.39%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eCharacteristics are shown as median [range], mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or number (%). LLL, left lower lobe; LUL, left upper lobe; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; GGN, ground-glass nodule; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLocalization Procedure Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll 155 nodules were successfully localized using the robot-assisted optical navigation system, resulting in a localization success rate of 100%. The median number of needle replacements was 0 (ranging from 0 to 2), with a mean deviation between the needle tip and the target point of 1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 mm. Regarding the procedural efficiency, the mean duration from the initial CT scan to the needle being achieved in place was 8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77 minutes. Furthermore, 10 (6.45%) cases developed minor pneumothoraxes and 3 (1.94%) cases experienced minor hemorrhage, with no deaths or other severe adverse events being observed during the robotic-assisted localization procedure, and none of the cases with complications required any intervention. Moreover, no dislodgement was reported in any of the cases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTechnical Characteristics and Outcomes of Robotic-assisted Localization Procedures\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValues\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePer lesion (n\u0026thinsp;=\u0026thinsp;155)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecubitus position (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (45.81%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (38.06%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (14.19%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.29%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.65%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepth (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.21\u0026thinsp;\u0026plusmn;\u0026thinsp;22.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocalization success (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of needle repositionings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian [Min, Max]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 [0, 2.00]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129 (83.23%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (15.48%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.29%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviation (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplications (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (91.61%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinor pneumothorax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (6.45%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinor hemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.94%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eCharacteristics are shown as median [range], mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or number (%). LAO, left anterior oblique; RAO, right anterior oblique; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStratified analyses by location, type, size, and distance to the pleura of nodules, as well as various decubitus positions, were conducted to evaluate accuracy as exploratory findings. Notably, the deviation of the localizer was found to have no significant correlation with characteristics of nodules including location of nodules, type of nodules, and decubitus positions, also in size and distance to pleura of nodules (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, S3, and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, a successful localization case using the robotic-assisted optical navigation system with a high level of localization difficulty was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurgery and Pathological Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing the successful localization of pulmonary nodules, all patients were transferred to the operating room to perform VATS. The mean time interval between nodule localization and surgery was 133.67\u0026thinsp;\u0026plusmn;\u0026thinsp;103.36 minutes. All targeted nodules were resected successfully and the final definitive pathologic tissue results were obtained. Among the 155 lesions, 115 (74.19%) underwent wedge resection and lymph node sampling, 21 (13.55%) underwent segmentectomy and systemic lymph node dissection, and 19 lesions (12.26%) were later converted to lobectomy due to the determination of an invasive stage based on the intraoperative frozen section analysis with the specific micropapillary and solid histological patterns. Histopathological examination revealed 18 (11.61%) benign nodules, 35 (22.58%) precancerous lesion, and 102 (65.81%) malignant nodules. Of the 35 precancerous lesion, all of them were diagnosed as adenocarcinoma \u003cem\u003ein situ\u003c/em\u003e (AIS). Of the 102 malignant nodules, 55 (35.48%) were diagnosed as minimally invasive adenocarcinoma (MIA), 42 (27.10%) as invasive adenocarcinoma (IA), 4 (2.58%) as mucinous pulmonary adenocarcinoma (MPA), and 1 (0.65%) as non-hodgkin lymphoma (NHL) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSurgery and Pathological Outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValues\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePer patient (n\u0026thinsp;=\u0026thinsp;137)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterval_time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133.67\u0026thinsp;\u0026plusmn;\u0026thinsp;103.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePer lesion (n\u0026thinsp;=\u0026thinsp;155)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of resection (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWedge resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (74.19%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegmentectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (13.55%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (12.26%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathology (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign lesion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (11.61%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (22.58%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (35.48%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (27.10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.58%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.65%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eCharacteristics are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and number (%). AIS, adenocarcinoma \u003cem\u003ein situ\u003c/em\u003e; MIA, minimally invasive adenocarcinoma; IA, invasive adenocarcinoma; MPA, mucinous pulmonary adenocarcinoma; NHL, non-hodgkin lymphoma.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLearning Curve Evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 121 patients with a single lesion (performed by the same operator) were categorized in chronological order, and their intervention times were collected to calculate the CUSUM values and conduct further validation analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, two pivotal cutoff points emerged at the 48th and 83rd cases, dividing the learning curve into three distinct phases including Phase I (the initial 48 cases), Phase II (cases 48th to 83rd ), and Phase III (after the 83rd case). During Phase I, the intervention time surpassed the average, and the learning curve demonstrated a steep upward trend, indicating a notable increase in preoperative localization procedural duration. In Phase II, the intervention time continued to exceed the average, with the learning curve exhibiting a gradually ascending trend, suggesting a decelerated rate of improvement when compared to the initial phase. Upon reaching the 83rd case, the intervention time transitioned to a state of falling below the average and displayed a consistent downward trend in Phase III. Further validation analyses demonstrated that the intervention time and the localization deviation were significantly decreased across the three phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This visualization illustrates the evolution of localization efficiency and accuracy with increasing experience.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePreoperative precise localization of pulmonary nodules is crucial for the diagnosis and treatment of suspected malignancies. Conversely, inaccurate localization, especially in small or deeply situated target nodules, can contribute to repeated puncture attempts, extended surgical duration, increased incidence of complications, and even positive resection margins or failure of the nodule resection (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This study utilized a novel robotic-assisted optical navigation system that could effectively compensate for the aforementioned deficiencies, achieving successful preoperative localization of 155 nodules in 137 patients and resulting in the successful resection of all nodules through VATS with oncologically safe margins. The present study revealed the safety and feasibility of using the robotic-assisted optical navigation system for CT-guided percutaneous localization of pulmonary nodules by hook-wire technique.\u003c/p\u003e\u003cp\u003ePrior research has indicated that the success rate of CT-guided hook-wire localization can range from 64.7\u0026ndash;99.4%, depending on the studies (\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), whereas an encouraging 100% successful localization rate was achieved in this study. The average deviation between the needle tip and target point in this study was 1.49 mm, which was significantly lower than the average deviation of 13.76 mm reported in a study using a freehand hook-wire technique for CT-guided preoperative localization of pulmonary nodules (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Another study reported an average deviation of 4.6 mm and a mean number of needle replacements of 2.1 in the freehand hook-wire localization group, both of which exceeded the values in this study, where the mean number of needle replacements was 0.2 and the deviation was as previously mentioned (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, the conventional freehand hook-wire localization had a much higher rate of pneumothorax and hemorrhage reported in previous studies, with almost 40% and 20% of cases, respectively, whereas the robotic-assisted optical navigation system only had a rate of 6.45% and 1.94% for pneumothorax and hemorrhage in our findings (\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These discrepancies might be caused by traditional CT-guided method often presenting significant challenges due to factors including the reliance on experience of surgeons, the precision required, and the complexity of human anatomy, which may necessitate iterative adjustments in needle depth and angle, a higher complication rate, and even failure; conversely, the robot-assisted optical navigation system used in this study can offer several critical advantages, such as the reconstruction of a visualized 3D model of various anatomical components, precise tracking of the actual position and orientation of the patient, comprehensive planning of trajectories, real-time recording of respiratory motion, and stable needle-holding and guiding needle insertion. In recent prospective clinical studies, this robotic-assisted system has demonstrated its ability to enhance puncture accuracy, reduce the need for needle corrections during percutaneous puncture procedures, and lower the incidence of complications compared to traditional manual insertion (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, a series of robot-assisted systems were developed for CT-guided minimally invasive diagnosis and treatment to enhance the efficiency of medical practitioners, and to ensure high surgical precision and safety; however, these systems face varying degrees of technical challenges or inherent limitations. Zerobot\u0026reg; (Okayama, Japan), a remote-controlled robot guided by real-time CT fluoroscopy, has demonstrated the feasibility of needle insertion, whereas it has significant drawbacks, including being incompatible with most conventional CT scanners due to the robot arm operating inside the gantry and not widely available for routine use of the fluoroscopy module (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). MAXIO (Perfint Healthcare, Chennai, India), one of the earliest commercialized robots for percutaneous interventional procedures, can provide mechanical guidance according to the surgical plan, whereas patients need to remain still due to the absence of tracking or compensating for their movements throughout the entire procedure (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Epione (Quantum Surgical, Montpellier, France), another Food and Drug Administration (FDA)-approved robot-assisted navigation system, is similar to the robot-assisted system utilized in this study, with similar components and operational procedures except for a rigid marker bracket attached to the patient, which may not conform well to body contours and stably track the patients' movements through the optical camera (\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Some other robotic-assisted systems are still in the preliminary research stage or have yet to be commercialized (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The robot-assisted optical navigation system used in this study, compared to the existing robotic systems, may potentially be a better choice for lung nodule localization.\u003c/p\u003e\u003cp\u003eThe clinical trials of percutaneous localization of lung nodules using robotic-assisted systems are still limited (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In a retrospective analysis of 654 cases involving CT-guided percutaneous localization of pulmonary nodules using the MAXIO robot with a modified hook-wire needle, the successful localization rate was 96.64%, with an average deviation of 5.01 mm, a mean marking time of 22.85 minutes, a pneumothorax rate of 27.21%, and a parenchymal hemorrhage rate of 33.94%, indicating a relatively subpar performance in technical characteristics and outcomes of localization procedures compared with this study (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Another clinical pilot study employing the same robotic-assisted navigation system as in this study also demonstrated its safety and feasibility for achieving precise percutaneous lung nodule localization using indocyanine green (ICG) technique, with an impressive first-pass success rate of 100%, a median deviation of 6.1 mm, and no significant complications other than 4 cases of asymptomatic pneumothoraxes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). It also reported that the accuracy of the robotic-assisted navigation system was not influenced by location of nodules, distance to the pleura, or decubitus positions, consistent with the findings in this study, demonstrating the stability of the robotic-assisted system and its potential for guiding nodule localization with more accuracy, fewer postoperative complications, and shorter procedural duration in various clinical settings of localization techniques.\u003c/p\u003e\u003cp\u003eThe CUSUM analysis suggested that the learning curve for robotic-assisted CT-guided percutaneous pulmonary nodule localization using hook-wire needles comprised three distinct phases. These phases of the learning curve were identified by the presence of two cutoff points (case 48 and case 83) at which the inclination and the trend of the curves sharply changed. As mentioned above, the CUSUM curve reflected the sequential changes compared to an average value, so the presence of two cutoff points in the CUSUM curve indicates two critical points in terms of preoperative localization procedural duration changes during the learning process. In this study, the Phase I, as an initial learning period, involved gradually acquiring the technical competence and skills to significantly reduce intervention time (1st -48th case). The Phase II, as a consolidation period, could be interpreted as the accumulation of additional experience after the initial learning curve and as the consolidation of the experience gained during the first phase (49th -83rd case). The Phase III represented the mastery period, suggesting the acquisition of a higher competence in the execution of this preoperative localization procedure under robotic guidance (84th -121st case). A prior study on learning processes of CT-guided interventional radiology under robotic assistance showed that two operators, inexperienced initially, cautiously learned preoperative lung nodule localization under a robotic-guided system and almost simultaneously reached phases I and II in the 50th and 90th cases (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), similar to the findings of the present study. Moreover, the intervention time and localization deviation significantly decreased across the three distinct phases, in line with expectations. These learning curve findings could help to less experienced physicians accurately understand the benefit of using the robotic-assisted optical navigation system in CT-guided percutaneous interventional procedures.\u003c/p\u003e\u003cp\u003eThe present study still had several limitations that should be acknowledged. First, this is a single center study, which limits the generalizability of the findings. Second, the lack of a freehand control group makes it difficult to accurately determine the superiority derived from this system. Further large-scale randomized studies are warranted for a more comprehensive evaluation of the currently available CT-guided robotic-assisted optical navigation system.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo our knowledge, this study is the first report of using an optical navigation robotic-assisted system for percutaneous lung nodule localization by hook-wire. The current findings demonstrate the safety, feasibility and promising potential of this technique, while also provides an additional evidence supporting the clinical practice of using this system. Regarding the limitations of this study, further randomized clinical trials are essential to validate its advantages compared to conventional manual localization. Nevertheless, It can be concluded that the robotic-assisted optical navigation system is both safe and effective in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the National Natural Science Foundation of China for invaluable assistance of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and will require approval from the First Affiliated Hospital of Dalian Medical University. Data are located in controlled access data storage at the department of thoracic surgery from the First Affiliated Hospital of Dalian Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (Project identification code: PJ-KS-KY-2023563), and written informed consent was obtained from all participating patients. All procedures performed in studies involving human participants were in accordance with the ethical standards of institutional and/or research committee and with the 1975 Declaration of Helsinki, as revised in 2013.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeng Wang ([email protected]): Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Supervision, Project administration, Methodology, Conceptualization. Zhichao Sun ([email protected]): Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Validation, Investigation, Data curation. Jiayan Wu ([email protected]): Methodology, Investigation. Fengzhou Li ([email protected]), Zhe Sun ([email protected]), Zhuoshi Li (11515707082qq.com), Changsheng Lv ([email protected]), Tao Guo ([email protected]), Xin Shu ([email protected]), Lei Fang ([email protected]), Jiawei Wang ([email protected]), Jin Wang ([email protected]), Lei Zhao ([email protected]), Fachen Zhou ([email protected]): Methodology, Investigation. Chundong Gu ([email protected]) and Shilei Zhao ([email protected]): Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Project administration, Methodology, Formal analysis, Conceptualization. All authors contributed to manuscript revision, read, and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [No.81774078]; Dalian Science and Technology Innovation Fund Project [No. 2024JJ13PT067].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHo KT, Min PC, Min LS, et al. Percutaneous transthoracic localization of pulmonary nodules under C-arm cone-beam CT virtual navigation guidance. Diagn Interv Radiol. 2016;22(3):224\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. 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AJR Am J Roentgenol. 2016;207(6):1334\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark CH, Lee SM, Lee JW, et al. Hook-wire localization versus lipiodol localization for patients with pulmonary lesions having ground-glass opacity. J Thorac Cardiovasc Surg. 2020;159(4):1571\u0026ndash;e15792.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang H, Li Y, Chen X, He Z. Comparison of hook-wire and medical glue for CT-guided preoperative localization of pulmonary nodules. Front Oncol. 2022;12:922573.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJing Y, Zhang J, Jin Y, Bai X. Evaluation of robotic-assisted navigation system for CT-guided thoracic and abdominal lesion puncture: A prospective clinical study. J Cancer Res Ther. 2024;20(4):1350\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJing Y, Jing J, Liu J, Zhang J, Jin Y, Bai X. The clinical performance of robotic assisted navigation system versus conventional freehand technique for percutaneous transthoracic needle biopsy. Sci Rep. 2025;15(1):5980. Published 2025 Feb 18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiraki T, Kamegawa T, Matsuno T, et al. Robotically Driven CT-guided Needle Insertion: Preliminary Results in Phantom and Animal Experiments. Radiology. 2017;285(2):454\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiraki T, Kamegawa T, Matsuno T, Komaki T, Sakurai J, Kanazawa S. Zerobot\u0026reg;: A Remote-controlled Robot for Needle Insertion in CT-guided Interventional Radiology Developed at Okayama University. Acta Med Okayama. 2018;72(6):539\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiraki T, Kamegawa T, Matsuno T, et al. Robotic needle insertion during computed tomography fluoroscopy-guided biopsy: prospective first-in-human feasibility trial. Eur Radiol. 2020;30(2):927\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoethe Y, Xu S, Velusamy G, Wood BJ, Venkatesan AM. Accuracy and efficacy of percutaneous biopsy and ablation using robotic assistance under computed tomography guidance: a phantom study. Eur Radiol. 2014;24(3):723\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchaible J, Pregler B, Verloh N, et al. Improvement of the primary efficacy of microwave ablation of malignant liver tumors by using a robotic navigation system. Radiol Oncol. 2020;54(3):295\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnston EW, Basso J, Silva F, et al. Robotic versus freehand CT-guided radiofrequency ablation of pulmonary metastases: a comparative cohort study. Int J Comput Assist Radiol Surg. 2023;18(10):1819\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuiu B, De Ba\u0026egrave;re T, Noel G, Ronot M. Author Correction: Feasibility, safety and accuracy of a CT-guided robotic assistance for percutaneous needle placement in a swine liver model. Sci Rep. 2021;11(1):8241.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Ba\u0026egrave;re T, Roux C, Deschamps F, Tselikas L, Guiu B. Evaluation of a New CT-Guided Robotic System for Percutaneous Needle Insertion for Thermal Ablation of Liver Tumors: A Prospective Pilot Study. Cardiovasc Intervent Radiol. 2022;45(11):1701\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBonnet B, de Ba\u0026egrave;re T, Beunon P, Feddal A, Tselikas L, Deschamps F. Robotic-assisted CT-guided percutaneous thermal ablation of abdominal tumors: An analysis of 41 patients. Diagn Interv Imaging. 2024;105(6):227\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang W, Xia P, Liu S, et al. A coordinate positioning puncture method under robot-assisted CT-guidance: phantom and animal experiments. Minim Invasive Ther Allied Technol. 2022;31(2):206\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Robotic-assisted, CT-guided, optical navigation, localization, pulmonary nodules","lastPublishedDoi":"10.21203/rs.3.rs-7297933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7297933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRobotic-assisted navigation systems for the localization of nonvisible and nonpalpable pulmonary nodules have demonstrated feasibility and safety in preclinical animal studies; however, clinical evidence supporting their practical application remains limited. This study aims to evaluate the safety and feasibility of using a robotic-assisted system for computed tomography (CT)-guided percutaneous localization of lung nodules.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 137 consecutive patients with 155 nodules were included in the final analysis, all of whom underwent percutaneous hook-wire localization using a novel robotic-assisted optical navigation system. The baseline characteristics of patients and nodules, localization procedure findings, and exploratory outcomes of the correlations between pulmonary nodule features and localization procedure findings were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe localization success rate was 100%. With the assistance of the robotic-assisted optical navigation system, the median number of needle adjustments per target was 0 (ranging from 0 to 2) in this study, with a mean deviation of 1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 mm. The mean intervention time was 8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77 minutes during the robotic-assisted process. Notably, there was no significant change in the accuracy influenced by the location, type, size of nodules, distance to pleura, and decubitus positions. Localization-related complications occurred in 13 (8.39%) out of 155 targets, including 3 (1.94%) minor hemorrhages and 10 (6.45%) minor pneumothoraxes, and no dislodgement was observed in any of the cases. All surgeries were successfully performed with a mean time interval between nodule localization and surgery of 133.67\u0026thinsp;\u0026plusmn;\u0026thinsp;103.36 minutes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis prospective, single-center, single-arm clinical study suggests both feasibility and safety of an innovative robotic-assisted optical navigation system for the CT-guided percutaneous localization of pulmonary nodules using hook-wire technique, as well as satisfactory accuracy during the needle placement.\u003c/p\u003e","manuscriptTitle":"Robotic-assisted Optical Navigation System for CT-guided Preoperative Percutaneous Hook-wire Localization of Pulmonary Nodules: a Prospective, Single-center, Single-arm Clinical Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:17:25","doi":"10.21203/rs.3.rs-7297933/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-14T03:00:43+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"75205864278024858266004215892343965225","date":"2025-11-08T16:58:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T16:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287627073759743908434323790793217460923","date":"2025-11-03T21:21:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-01T21:44:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29160947177575800633861373376957445953","date":"2025-10-30T21:06:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157741789928245373966788474452786582138","date":"2025-10-28T21:47:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182873777852495394460288946950657270911","date":"2025-10-13T17:33:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-16T20:33:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T14:58:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T06:37:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-08-05T08:00:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9432651-025e-4877-b1cd-b2ef4db3671f","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:01:14+00:00","versionOfRecord":{"articleIdentity":"rs-7297933","link":"https://doi.org/10.1186/s12957-026-04204-x","journal":{"identity":"world-journal-of-surgical-oncology","isVorOnly":false,"title":"World Journal of Surgical Oncology"},"publishedOn":"2026-01-21 15:58:02","publishedOnDateReadable":"January 21st, 2026"},"versionCreatedAt":"2025-08-27 06:17:25","video":"","vorDoi":"10.1186/s12957-026-04204-x","vorDoiUrl":"https://doi.org/10.1186/s12957-026-04204-x","workflowStages":[]},"version":"v1","identity":"rs-7297933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7297933","identity":"rs-7297933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}