Advancements in 3D lung models for minimally invasive lung cancer surgery: from static to real-time dynamic modeling

Introduction

Surgery is the standard of care for localized non-small cell lung cancer (NSCLC) (1,2). Following the publication of the JCOG0802 and CALGB140503 randomized controlled trials (3,4) confirming the oncological safety of segmentectomy compared to lobectomy, segmentectomy has been widely recognized and accepted as a standard surgical procedure for early-stage (localized) NSCLC. To perform a segmentectomy, a thorough understanding of the more complex segmental anatomy of bronchi, pulmonary vessels, and anatomical variations is necessary. Hence, the latest expert consensus (5), as well as the Delphi consensus report (6) on minimally invasive segmentectomy by the European Society of Thoracic Surgeons (ESTS), highly recommend the use of three-dimensional (3D) lung models to assist surgeons in addressing these technical difficulties.

A personalized 3D lung model serves as a valuable guidance tool for preoperative planning and intraoperative navigation in lung cancer surgery, relying on accurately segmented pulmonary structures, including the bronchial tree, pulmonary vasculature, lung parenchyma, and tumor. These detailed models allow for improved visualization and better spatial insight of patient-specific anatomical variations, intersegmental planes, and target branches of pulmonary arteries, veins, and bronchi. Moreover, they offer a more precise lung nodule localization and can, therefore, assist in decision-making processes on the extent of lung resection, ensuring an oncological safe resection margin while preserving lung function (7,8).

Unlike prior reviews that primarily focus on technical aspects or selected applications, this review article—part of the special series “Current Advances and Innovations in Surgical Lung Cancer Treatment”—bridges the gap between technological advancements and their practical integration into clinical workflows. It highlights the evolution from static 3D models to real-time dynamic simulations and underscores the expanding role of augmented reality (AR) and virtual reality (VR) technologies in thoracic surgery planning and intraoperative guidance. In addition, it explores the applicability of these models not only for sublobar resections but also for lobar resections. Figure 1 provides a schematic overview of the main aspects discussed in this review.

Figure 1 Schematic overview of the technical aspects, technological advances, clinical integration, and future perspectives of 3D lung models in lung cancer surgery, as discussed in this paper. 3D, three-dimensional; AR, augmented reality; CT, computed tomography.

Development of a 3D model

The development of 3D lung models relies on computed tomography (CT) scans, the gold standard for lung imaging routinely used for diagnostic purposes and surgical planning (1,9), and segmentation techniques to annotate lung structures, aided by various software programs. In this section, we provide an overview of the basics of developing a 3D lung model.

Basics of CT scan

The introduction of multi-detector CT (MDCT), modern-day computers, and the development of imaging processing methods for medical applications around the 1970s has laid the groundwork for 3D reconstructions of many anatomical structures (10,11). In the late 1980s, the MDCT technology was primarily applied to skeletal imaging before moving on to more complex soft tissue and dynamic four-dimensional (4D) reconstructions (i.e., 3D imaging with the added temporal component of time) (11,12). MDCT scans consist of 3D, and sometimes 4D, datasets, containing digital numerical data arrays (Figure 2). Each element within these arrays is referred to as a pixel in two-dimensional (2D) representation or a voxel in 3D representation. The spatial resolution of these pixels or voxels is determined by the slice thickness and field of view of the MDCT, which affect the quality of the MDCT scan and the resulting 3D reconstruction (13). The numerical values assigned to each voxel in the dataset correspond to Hounsfield units (HU), a dimensionless unit expressing the tissue density based on the radiodensity in a standardized scale correlated to the linear transformation of measured attenuation coefficients (14). This scale ranges from −1,000 HU for air to around +1,000 HU for dense bone, with water set at 0 HU as a reference point. The HU values help distinguish different types of tissue based on their density. This differentiation is essential for accurately identifying and separating (segmenting) tissues from CT images.

Figure 2 Schematic overview of the basic elements of a CT scan. Each CT slide is composed of voxel arrays, with spatial resolution determined by the field of view, matrix size, and slice thickness. The radiodensity of tissues is expressed in HU, derived from a linear transformation of the measured attenuation coefficients, with water set at 0 HU and air at −1,000 HU. 2D, two-dimensional; 3D, three-dimensional; CT, computed tomography; HU, Hounsfield units.

Segmentation techniques

To create detailed 3D models from MDCT data, various segmentation techniques can be used, each differing in complexity and labor intensity. The choice of segmentation technique is typically tailored to specific anatomical structures, diseases, or clinical applications, allowing for optimized visualization based on the diagnostic or clinical need. Commonly used segmentation techniques include surface rendering methods (e.g., trachea, main bronchi, lung lobes), volume-rendering methods (e.g., vessels, lesions, lung parenchyma), and advanced artificial intelligence (AI) algorithms (15-17). Surface rendering is a semi-automatic segmentation method, requiring a combination of manual segmentation, which is labor-intensive and time-consuming, with algorithmic assistance to reduce effort. The time to create 3D lung models with semi-automatic segmentation methods could take between 17 and 90 minutes (18,19). Adjusting the settings and type of CT scan can help reduce the reconstruction time by minimizing the need for labor-intensive segmentation methods. Contrast-enhanced arterial-phased CT scans, for example, can be used to effectively discriminate the pulmonary artery from the pulmonary vein, as the intertwining of the pulmonary arteries and veins is often observed with standard-of-care contrast-enhanced CT scans in the early venous portal phase (18). While these scans are diagnostically used for optimal enhancement of the hilar structures for lymph node staging (20), they require subsequent labor-intensive manual segmentation if they are used to develop a 3D lung model. Beyond the CT settings, fully automated segmentation methods—particularly the advanced AI-based algorithms—offer the greatest time efficiency by minimizing manual intervention. As such, these AI-driven segmentation methods can significantly reduce the time required to create a 3D lung model, ranging from around 7 minutes to even less than 20 seconds (19,21), depending on the desired resolution—higher resolutions require more processing time.

Parenchymal segmental boundaries

Accurate delineation of parenchymal segmental boundaries, also known as the intersegmental plane, is fundamental for precise surgical planning. Traditionally, pulmonary segmentation relies on bronchial anatomy. However, the bronchial structures are only reliable up to the fourth bronchial generation, as they diminish beyond this level due to their decreasing size, lack of cartilaginous support in bronchioles, and increased anatomical variability, hindering accurate segmentation (22). An alternative approach involves the pulmonary vasculature, as segmental arteries accompany segmental bronchi, and lung segments are divided by intersegmental veins (23). For example, Gao et al. used a semi-automated segmentation method to delineate intrapulmonary vessels and construct arterio-pulmonary segments (22). They achieved this by marking the segmental artery and bronchus within the same pulmonary segment using similar colors, enabling them to trace pulmonary artery branches up to the seventh order. While the arterial-based model can better visualize higher-order branches compared to the bronchial model, still 10% of the pulmonary segmental arteries in their model were arteries from the adjacent segments, as these arteries entered adjacent pulmonary segments and were closely accompanied by the segmental bronchi. Other studies have shown promising results using the venous intersegmental planes (24,25); however, automatic approaches used in these studies still require labor-intensive manual adjustments (26), and the intersegmental planes can be inaccurate (e.g., underlying diseases or anatomical variations) (27). Given these challenges and significant volume differences between approaches (28), a hybrid approach combining bronchial and vascular information could be most accurate for segmenting the pulmonary lung segments (22).

Software programs

Nowadays, various commercial and open-source software programs are available to create 3D lung reconstructions. Commercial software programs are provided by established companies and are Food and Drug Administration (FDA)-approved or Conformité Européenne (CE)-marked for clinical use. These commercial solutions have a user-friendly interface and have undergone a robust validation process and regulatory compliance to ensure accuracy, reliability, and safety in most medical applications. Furthermore, they offer advanced capabilities for creating precise 3D lung models used in lung surgery, such as VR and AR imaging environments to enhance visualization and surgical planning. Most companies that develop commercial software programs for 3D lung models of medical images offer a so-called online medical analysis laboratory. CT scans are sent to the company via a secured web portal and qualified technologists are in charge of analyzing the images and reconstructing the 3D model. Afterwards, the 3D model of the lung is sent to the physician. Examples include Visible Patient (Ethicon, Johnson & Johnson MedTech, Amersfoort, the Netherlands) (29), Medics 3D (Medics Srl, Turin, Italy) (30,31), Synapse Vincent 3D (Fuji Film Co., Ltd., Tokyo, Japan) (32), IQQA^® (Edda Technology, Inc., Princeton, NJ, USA) (33), Hexa3D (Hexalotus Technology Pte Ltd., Singapore, Singapore) (34), Vitaworks (Vitawork Medical Technology Ltd., Shanghai, China) (35), and DeepInsight (Northeastern University, Shenyang, China) (36). In addition, a stand-alone platform can be used—such as Materialise Mimics (Materialise NV, Leuven, Belgium)—allowing users to develop 3D models in-house without outsourcing the task (37). Moreover, commercial post-processing software can be integrated into the hospital’s Picture Archiving and Communication System (PACS) systems for advanced image analysis or rendering, which typically allows for direct access to stored images without requiring external file transfers. Examples of PACS-integrated systems include Surgical Reality Viewer (formerly PulmoVR, Nieuw Vennep, The Netherlands) (17), Ziostation 2 (Ziosoft, Inc., Tokyo, Japan) (38), and Syngo.via (Siemens Healthineers, Erlangen, Germany) (39). Local privacy regulations might prohibit sending patient-related data to external companies.

Open-source software, on the other hand, such as 3D Slicer (Slicer Community) (15,18), OsiriX (Pixmeo SARL, Geneva, Switzerland) (16), and ITK-SNAP (Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA) (15), is freeware, software that is freely available and developed by a collaborative community of skilled users, developers, contributors and commercial partners around the world. It offers flexibility and customizability, enabling researchers to modify segmentation algorithms for specific needs. Consequently, self-developed applications in other programming and numeric computing platforms (e.g., Matlab, Python) can be integrated to minimize labor-intensive, repetitive segmentation steps and thus the inherent long reconstruction time. However, since segmentation tools can be customized without comprehensive quality and safety oversight, open-source software is not CE-marked or FDA-approved for clinical decision-making. In addition, developing these 3D lung models using open-source software requires technical medical expertise and might be very time-consuming. A surgical department must have a technical physician or technical medical doctor proficient in lung anatomy to create these in-house 3D models according to the surgeon’s needs. Ultimately, the choice between commercial and open-source software depends on the specific needs of the surgical department, balancing factors such as the costs, surgical annual volume, available technical expertise, and the desired level of customization in 3D lung models.

Imaging modalities

Advances in 3D visualizations play a key role in how 3D lung models are visualized and utilized in clinical practice. A range of devices, encompassing computers, tablets, smartphones, smart glasses, or headsets, are nowadays used to view 3D lung models, depending on the selected imaging modality and clinical application, whether pre- or intraoperatively. Although some advanced imaging modalities require a significant initial investment, once acquired, they become easily accessible and can be utilized for a range of lung procedures beyond surgical procedures for lung cancer. In the next section, we will give an overview of the characteristics of the different (advanced) imaging modalities, which can be found in Table 1.

On-screen imaging

All software programs enable the 3D lung models on-screen (e.g., computer, tablet, and/or phone) in 2D. An example of such an on-screen 3D lung model is shown in Figure 3. Several studies demonstrate the advantages of using on-screen 3D lung models over the standard of care CT assessment in shortened surgery duration of around 15 to 30 minutes (35,40-42), improved identification of pulmonary anatomical variations (18,29,43), and allowed for a more accurate lung nodule location (18,29) and demarcation of surgical margins. However, visualization of 3D lung models on a 2D screen suffers from the lack of perception depth, which makes it challenging to envision a graphical representation of the real anatomy with all its variations.

Figure 3 An example of a 3D model showing the bronchial tree (green), pulmonary veins (red), pulmonary arteries (blue), lung nodule in the right upper lobe (yellow) and left lobes (yellow and purple), created in 3D Slicer (Slicer Community). 3D, three-dimensional.

Stereoscopic 3D visualization with 3D glasses

Stereoscopic visualization using 3D monitors and 3D glasses enhances depth perception and spatial understanding, supporting its value in intraoperative assessment of pulmonary anatomy as it allows for more precise dissection and ligation of the target pulmonary vessels and bronchus (44,45). Important to note is that a stand-alone stereoscopic 3D visualization with 3D glasses cannot be integrated with 3D lung models. However, both can be combined in a dual-monitor setup in which one 3D monitor displays the in-depth 3D vision of the surgical field and a second 2D monitor displays the on-screen personalized 3D lung model. Such a setup has shown to be feasible in clinical setting and is helpful for intraoperative guidance (45). The implementation of this technology involves costs related to software licensing, 3D monitors, and compatible 3D glasses.

3D printing

3D printed models may solve these on-screen shortcomings and lack of perception depth, which can decrease the mean surgery duration by 16 minutes for complex segmentectomies, compared to on-screen 3D models (46). Surgeons also report that 3D-printed models provide a superior understanding of segmental anatomy, particularly benefiting less experienced surgeons, whilst more experienced surgeons indicate that on-screen 3D models were sufficient (46). In addition to using 3D-printed lung models in preoperative planning and as an intraoperative roadmap, these models can enhance communication with patients and colleagues, as they improve patient understanding, facilitating clearer discussions among medical professionals (37,47). Personalized 3D-printed models, such as silicon-based models or dynamic phantoms, allow for in situ rehearsing of the surgery, as well as training, respectively (47,48). The use of standard-of-care 3D printing, however, can be hindered by the time to print the model and the associated costs. The printing time can vary between 3 and 5 hours (49). Notwithstanding, the time and costs of 3D printing could be considerably reduced if only a limited part of the 3D lung model is printed (50). Another limitation of 3D printed models is their lack of flexibility in terms of visibility. Unlike software-based models or AR/mixed reality (MR) systems where structures can be toggled “on and off” to enhance visualization and focus on specific areas of interest, 3D printed models provide a static non-adjustable representation.

VR

To make visualization and manipulation of 3D lung models possible in an interactive way, a virtual or hybrid simulated world can be created using extended reality techniques. VR is excellent for preoperative planning and educational purposes (51,52). Furthermore, surgeons can gain enhanced insights into patient-specific anatomy as VR models provide interactive image manipulation, realistic in-depth perception, and improved visualization of complex segmental pulmonary structures. Nevertheless, VR is not intended for intraoperative use, as it involves a complete virtual environment with total occlusion of the surrounding environment (53). Unless the VR is paired with specialized haptic devices (e.g., gloves or controllers with vibration or force feedback), the absence of haptic force makes them less practical. The VR setup requires several investments (typically around €2,000) including software, computers, and VR headsets (17). Notwithstanding, in recent years there has been a notable shift towards utilizing affordable solutions for surgical training and preoperative anatomical exploration in resource-limited settings, providing stand-alone VR headsets of around €300 (54).

MR and AR

3D models can also be superimposed on the surgical field using AR or MR enabling enhanced intraoperative perception and guidance throughout the surgical procedure as a navigational tool to localize anatomical structures of interest or critical landmarks (53). AR places the 3D lung model within the native environment, while MR enables the link between the 3D lung model and real-time surgical field view, allowing for the interaction between the physical and virtual world (55). AR and MR visualizations both necessitate correct registration (i.e., alignment) between the 3D model and the intraoperative setting (53). For lung surgery, however, this alignment is complicated as the lung is a soft, flexible organ that collapses during surgery and is manipulated by the surgeon. As a result, more advanced, deformable 3D modeling, and real-time registration techniques are needed to account for these deformable changes as was recently presented in the field of robotic-assisted lung surgery (56). For future developments and routine clinical use, several clinical and technical challenges must be addressed for the effective use of AR and MR in lung surgery, including real-time and automated workflow optimization to reduce 3D model alignment delays, dynamic registration techniques to account for lung deflation and tissue manipulation, and seamless integration of the graphical user interface into robotic surgical consoles (57). Therefore, AR/MR for surgical navigation in pulmonary surgery remains limited and is still in its infancy. Also, some users might experience nausea based on motion sickness (58). Furthermore, as with other advanced imaging modalities, AR is associated with higher costs, limiting its widespread clinical adoption (49). Lower-cost AR solutions utilizing off-the-shelf hardware range from €1,000 to €5,000, whereas high-end AR systems are priced between €165,000 and €325,000.

Application and clinical value of 3D lung reconstructions in lung cancer surgery

As a promising tool in lung (cancer) surgery, 3D lung models offer valuable support to surgeons in both the preoperative and intraoperative settings. Preoperatively, these models enhance traditional 2D CT-based planning by allowing a more accurate assessment of the required extent of the surgical resection and identifying anatomical variations. This can lead to a change in the initial surgical plan and increase the surgeon’s confidence by providing a clearer expectation of the operative field. Identification of patient-specific anatomical variations in the broncho-vascular structures can serve as a clearer surgical roadmap to preoperatively determine the ligation locations of the broncho-vascular structures and avoid surgical damage to the vascular and bronchial branches. For intraoperative application, 3D lung models can be used for intraoperative nodule localization and intraoperative navigation. In addition, 3D lung models can aid in the education and training of lung surgeries, as well as patient counseling. In the following sections, we will delve deeper into the various (clinical) applications of dynamic 3D lung models.

Preoperative planning

The precise orientation of patient-specific anatomy (i.e., the tumor in relation to the intrathoracic vessels, bronchi, and segmental borders) is essential for decision-making on the appropriate extent of the resection. This ensures an oncological safe resection margin and is particularly important for patients with small-sized lung nodules ≤2 cm who may be eligible for minimally invasive (bi)segmentectomy. Visualizing the parenchymal segmental boundaries and a virtual surgical margin around the lesion of interest contributes to the decision-making on the extent of the surgical resection, providing information on whether sufficient surgical free margin surrounding the tumor is achieved with a lobectomy or sublobar resection (29). Suppose a sublobar resection is to be used, the surgeon is able to assess whether a conventional segmentectomy or another sublobar resection—extended segmentectomy, bi-segmentectomy, or non-anatomical wedge resection—is more appropriate using these visualizations. Several 3D software programs, such as Visible Patient, contain a ‘clipping’ tool that can simulate the dissection of the segmental bronchus or pulmonary artery at arbitrary points (29,59). The virtual dynamic images obtained from these simulations can be utilized to ensure the feasibility of a segmentectomy.

Surgeons agree that 3D lung models are an effective and easy-to-use tool during preoperative planning (18,51), providing an enhanced understanding of the pulmonary (segmental) anatomy (18,46,51). Furthermore, several studies demonstrate that using preoperative 3D lung models can lead to a change of surgical plan preoperatively. For example, Sadeghi et al. [2021] revealed in their pilot study that their jointly developed and manufactured AI-based and immersive 3D-VR platform (PulmoVR) was responsible for changing the surgical procedure in 40% of the cases (4 out of 10 patients) (51). They confirmed these results in a larger prospective study and reported a change of surgical operation in 52% (26 of 50 patients) of the planned minimally invasive segmentectomies after 3D VR planning (17). More specifically, 26% of the patients required an extended segmentectomy and 2% (n=1) an extended lobectomy beyond the initial plan to ensure an adequate resection margin. In addition, in 10% of the patients, fewer segments were resected than initially planned and the lung nodule appeared to be localized in a different segment in 14% of cases. Furthermore, our group has shown that 3D lung models with Visible Patient revealed a more centrally located lung nodule than anticipated based on 2D CT planning alone in three out of five planned uniportal video-assisted thoracic surgery (VATS) segmentectomies (29). To ensure complete resection, the preoperative surgical procedure was consequently changed to a lobectomy in all three cases.

It remains important, however, that estimating the feasibility of a segmentectomy preoperatively remains challenging, and intraoperatively confirming adequate resection margin is essential using real-time intraoperative identification methods to visualize intersegmental planes, including the inflation-deflation method and transbronchial or intravenous indocyanine green with near-infrared-fluorescence imaging (5,60). Even with detailed preoperative 3D lung models, unplanned intraoperative changes in the extent of the resection cannot be prevented, for instance, in the event of a positive or uncertain intraoperative frozen section analysis of the bronchial margin. Nevertheless, the frequency of intraoperative conversion from segmentectomy to lobectomy is expected to be minimized with a preoperative 3D lung model (61), as well as the reduction of intraoperative complications and an increase in local recurrence-free survival, both of which need further study.

In addition to assessing the extent of the resection, 3D lung models provide improved visualization of patient-specific anatomical variations in the broncho-vascular structures (17,18,45,49,62) and other pulmonary structures. Preoperative knowledge of uncommon interindividual anatomical variations could prevent surgical complications such as inaccurate dissection of the broncho-vascular target structures or intraoperative vascular injury (49). Indeed, a meta-analysis by Xiang et al. [2022] showed that minimally invasive segmentectomies with 3D-guided preoperative planning using various imaging modalities (e.g., on-screen, 3D printing) were associated with significantly lower conversion rates [relative risk (RR) =0.12; 95% confidence interval (CI): 0.03–0.48], fewer complications (RR =0.59; 95% CI: 0.43–0.82) and a clinically relevant shorter surgery duration (mean decrease of 13 minutes; 95% CI: 0–25) when compared to standard 2D CT-based planning (63).

A recent advancement in preoperative planning is the use of 3D-printed, silicon-based models to simulate complex surgical cases. These patient-specific in situ simulation models, as demonstrated in cardiothoracic procedures (48), enhance anatomical understanding and allow surgeons to rehearse the operation in advance. This hands-on preparation can reduce intraoperative complications and improve surgical precision by enabling detailed procedural planning, tailored to individual patient anatomy.

Intraoperative navigation

All aspects of preoperative planning—such as the precise localization of non-palpable lung nodules, identification of segmental borders, and visualization of anatomical variations in broncho-vascular structures—are traditionally confirmed intraoperatively, when direct visualization and palpation are possible. However, relying solely on intraoperative assessment can lead to increased uncertainty, prolonged surgery duration, and a higher risk of complications, particularly in minimally invasive or sublobar resections. To address these challenges, 3D lung model visualization and simulation have emerged as valuable tools.

Standard intraoperative localization of lung nodules through palpation can be time-consuming and challenging. If the nodule cannot be successfully located, conversion to open thoracotomy may be required. Several methods can be used to increase the successful retrieval of small lung nodules during lung cancer surgery, with the majority being invasive techniques such as CT-guided hook wire placement, bronchoscopic dye marking via navigational bronchoscopy, and guided localization with metallic micro-coils and fiducial markers (64). 3D lung models can be used as an excellent non-invasive alternative for more precise intraoperative lung nodule localization (49). As such, one study showed that AR can be used as an intraoperative navigation-guided approach to localize small pulmonary nodules that are difficult to palpate during surgery, especially during VATS (42). With a specially designed AR navigation system and a specialized umbrella-shaped metal lung localization marker, the benefits of intraoperative CT-guided methods (i.e., minimizing the potential loss or displacement of the localizer as seen with preoperative CT-guided methods) were preserved while avoiding the need for complicated equipment and infrastructure. Even though this AR-based method may be more beneficial than other invasive localization techniques, such as placing a hookwire, it still requires invasive marker placement. Last year, the same study group explored a markerless approach that used an algorithm that combined a surface-based registration, the most promising registration approach for markerless alignment, with anatomical landmark registration (i.e., critical pulmonary landmarks such as vascular bifurcations) (57). These first results are encouraging, showing invisible structures hidden underneath the surface in an intraoperative setting. Another recent non-invasive virtual localization technique is the development of a CT image-based virtual atelectasis simulation model for noninvasive lung nodule localization (65). This model predicts lung deformation in a deflated state—commonly known as lung collapse or atelactasis—enabling more accurate tracking of nodule positions without the need for invasive markers. By simulating intraoperative lung collapse, the system enhances the precision of surgical targeting and complements other localization techniques currently in use.

The intersegmental plane can be better identified using 3D lung models (49,60). As a result, surgical precision can be increased and more pulmonary parenchyma can be spared, whilst safety is not compromised (i.e., injury to the intersegmental vein) because of the detailed visualization of the blood vessels and bronchi within the target segment (66,67). Similar to the advantages mentioned in the preoperative setting, intraoperative knowledge of uncommon anatomical variations prior to exposing the bronchial and vascular branches contributes to safer and smoother surgery.

3D lung models can also be utilized as an intraoperative guide for the transection planes (49). Some software programs provide the option to identify clipping points on the nodule’s safety margins and surrounding broncho-vascular structures, allowing a delineation of the associated transection planes and an estimation of the lung tissue thickness (68). This can streamline the procedure, facilitating a guide to the transection planes and appropriate stapler type. Clinical outcomes of the intraoperative 3D model showed sufficient surgical margins, lower risk of recurrence, shorter surgeon duration of up to 20 minutes, less intraoperative blood loss (i.e., not clinically relevant mean of 35 mL), and reduction of air leakage by 1 day (36,66).

Education and training purposes

3D lung models can also be useful for clinical teaching and training residents in thoracic surgery. Deng et al. [2021] proposed on-screen 3D lung models to teach complex content to residents and other students within the limited teaching time available (69). Compared to the traditional surgical preparation using 2D CT data, students scored better in the localization of the nodule, planning of the surgery, and identifying segmental broncho-vascular structures utilizing the 3D visualization of the lung reconstruction displayed on the computer. The students also reported that these 3D lung reconstructions deepened their understanding of the segmental structure and surgery, aroused their enthusiasm and interest in learning the content, and improved their clinical thinking. In addition to the on-screen 3D lung models, 3D printed models benefit medical students or residents by improving anatomy learning using a hand-held model (70).

Surgical trainees should be introduced to 3D lung models early in their training, as early exposure may help them gain spatial anatomical insight and become familiar with the software, facilitating easier implementation of 3D technology in preoperative planning and intraoperative navigation later in clinical practice. Aside from being taught the lung anatomy and basics of surgery by using 3D reconstructions, surgical trainees should also learn how to use and interpret 3D lung models for preoperative planning and intraoperative navigation, given their clinical value and growing need for sublobar resections. Moreover, VR simulators are excellent in simulating intraoperative bleeding and anatomical variations, offering automated feedback and instruction modules (52). Even more realistic environments can be created with dynamic 3D printed (phantom) models that can provide tactile and visual feedback, enhancing procedural familiarity and skill development (36). While the most effective type of simulator for lung surgery remains a topic of debate, experts agree that simulation-based learning is essential for surgical training. Nevertheless, more complex real-time decision-making needs a surgical setting by surgical wet lab training (i.e., on cadaver specimens or training on live animals), a realistic simulated environment, or reflections on real-life clinical experience (71).

While 3D lung simulations show promising results for educational purposes, as with each new technique, one should consider an associated learning curve. Liu et al. [2021] and Zhang et al. [2019] evaluated the learning curve of on-screen 3D-simulation-assisted VATS segmentectomy and VATS lobectomy, respectively, both demonstrating that it enables safe and efficient procedures within a learning curve of 30 cases (37,43). This is, however, similar to the learning curve for a simple segmentectomy, namely between 26 and 32 cases and 38 cases for an experienced surgeon and a less experienced surgeon, respectively (72,73), and between 52 and 56 cases for complex segmentectomy (73). Further research is needed to determine whether incorporating 3D lung simulations into surgical training can shorten the learning curve of inexperienced surgeons.

3D lung models for lobectomy

Preoperative 3D lung models of pulmonary structures are recommended in current guidelines for segmentectomy, as the segmental lung anatomy is more complex and subject to patient-specific anatomical variations (5,6). Notwithstanding, their benefits for lobectomy procedures should not be misjudged. While the lobar anatomy is considered more consistent, various studies have highlighted patient-specific variations in the lobar broncho-vascular anatomy (43,74,75). For example, Polaczek et al. [2020] found notable variations in the pulmonary veins, such as the long common trunk (11%) and venous vascularization variations in the middle (25%), suggesting the need for routine assessment of pulmonary vessels (74). In our pilot study, 7 out of the 12 patients who were planned for a lobectomy had anatomical variations that were not identified by the thoracic surgeons on the CT scan (18). Such insights can lead to preoperative alteration of the planned transection planes, resulting in a smoother and more efficient surgery. Two studies supported these results by showing that 3D lung models were more valuable than conventional 2D CT planning for VATS lobectomy, reducing surgery time by an average of 15 to 20 minutes (42,43). Most likely, the increase in preoperative planning time and reduction in intraoperative time may have a positive affect on both patients and hospitals, though this warrants further study.

Moreover, 3D lung models offer several distinct advantages, particularly in the context of complex procedures such as (sleeve) lobectomy. First, 3D visualization of lung fissure completeness gives useful information for the surgical planning of minimally invasive lung resection, as an incomplete or underdeveloped lung fissure can substantially impact the surgical complexity and significantly prolong the duration of the surgery (76,77). Preoperative knowledge of fissure integrity allows for better estimation of procedure time, improved operating room scheduling, and anticipation of potential complications, such as intraoperative bleeding or postoperative air leakage. Second, in cases involving centrally located tumors where a sleeve lobectomy may be indicated, 3D models enhance the surgeon’s understanding of broncho-vascular involvement and can thus aid the surgeon in determining whether an extended resection (i.e., pneumonectomy) might be the better choice. Furthermore, in patients who have undergone prior thoracic surgery and suffer from dense adhesions, 3D reconstructions help clarify distorted anatomy, providing better insight into the spatial orientation of the lung anatomy that can reduce the risk of surgical bleeding and bronchial injury.

Knowing these advantages, it appears that 3D lung models should not only be considered within the standard of care for the planning of minimally invasive segmentectomy but also lobectomy or more advanced procedures (e.g., sleeve lobectomy). As better preoperative planning with 3D models essentially leads to shorter surgery times and fewer complications, it should be investigated whether its routine use for lobectomy is cost-effective. Especially in complex cases, as highlighted above, it might justify the expenses.

Patient counselling

Aside from the clinical advantages and educational purposes for trainees, 3D lung models can provide patients with interactive information to enhance their understanding of the procedure (58,78), improve doctor-patient communication (46), and support improved shared decision-making (79). For example, a pilot study of 20 stage I lung cancer patients planned for a surgical resection showed that a personalized 3D-printed model significantly increased the patient’s knowledge (78). Moreover, in another study, 88% of the 59 surgeons reported that the 3D-printed models allowed for better doctor-patient communication (46). The limited evidence available also demonstrates positive results for VR and AR to be used in medical communication as well, however, more robust evidence is necessary.

Practical implication

Despite the increasing availability and demonstrated clinical value of 3D modeling tools, their integration into routine thoracic surgical practice remains limited. Key barriers include time-intensive image segmentation processes, the need for specialized software and dedicated personnel, and the lack of standardized workflows across institutions. These factors can introduce delays in preoperative planning and place additional demand on already resource-limited clinical teams. To facilitate broader adoption, future efforts should focus on providing fully automated segmentation software, integrated into existing imaging platforms and streamlined protocols. Overcoming these logistical and technical challenges will be essential to fully realize the potential of 3D modeling in everyday surgical decision-making.

Practical steps for integrating 3D lung modeling into daily thoracic surgical practice would be to collaborate with radiologists or a technical physician (80) to interpret 3D reconstructions, incorporate them into multidisciplinary tumor board discussions, and pilot them in case planning by choosing a few complex segmentectomy cases and compare outcomes or decision changes with and without 3D planning. Furthermore, continued medical education programs, online interactive modules, and hands-on workshops using 3D visualization platforms or VR-based simulations could help normalize their use and improve comfort and proficiency over time. We would recommend to start with user-friendly platforms and practice manipulating models, as well as using tablets or screens intraoperatively as anatomical reference before integrating AR or intraoperative navigation tools that overlay 3D reconstructions.

Future perspective: dynamic 3D lung reconstructions

There is a growing interest in developing real-time deformable lung simulations that can mimic the lung’s behavior during deflation and surgical manipulation (56,59,81-83). Integration of such dynamic 3D lung simulations into clinical practice could offer several potential benefits over static images, including more precise surgical planning and enhanced navigation during procedures. While earlier studies have focused on breathing-induced lung deformations for applications including mechanical ventilation, functional assessment, and radiation therapy (84,85), minimally invasive lung cancer surgery presents unique challenges. In particular, the deflated state makes the intraoperative localization of pulmonary nodules difficult, especially non-palpable lesions. Several promising computational models are reported to mimic the lung in a deflated state. For instance, computational stress-strain models or hybrid approaches combining intensity-based and biomechanical-based image registration show potential for real-time, automated, markerless identification of non-palpable nodules (86,87).

In addition to the deflated state, other factors must be incorporated before real-time deformable lung models next to breathing-induced lung deformations can be used in intraoperative settings. The effect of patient positioning, diaphragm relaxation, and incomplete lung fissures should be considered in real-time deformable lung models, as these factors have been shown to contribute to displacement inaccuracies, with errors reaching up to 40 mm (81). In addition, lung parenchyma deformation due to surgical manipulation should be integrated, as external forces can deform and displace lung parenchyma, potentially affecting the location of lung nodules (82). While several dynamic 3D lung simulations for real-time surgical navigation are paving the way, ongoing research is essential to overcome these limitations.

Conclusions

In conclusion, 3D lung models improve surgical planning by aiding in the clinical decision-making process between segmentectomy and lobectomy, ensuring oncological safety, with evidence showing that in a substantial part of the segmentectomy cases, preoperative 3D planning leads to a change from segmentectomy to lobectomy. In addition, these models enhance intraoperative efficiency, leading to shorter surgery duration, reduced number of conversions to thoracotomy, and less blood loss. Given their benefits, integrating 3D lung models into standard-of-care for both segmentectomy and (more complex) lobectomy procedures could further optimize perioperative outcomes. However, more and larger cohort studies need to be performed to determine whether these advantages indeed extend beyond segmentectomy to lobectomy. Aside from the limitation that most evidence is based on retrospective cohort studies, there is a lack of comparative studies between the different imaging modalities and software programs, and cost-effectiveness needs to be proven. Future studies should also focus on dynamic lung simulations, which better represent intraoperative lung deformation, providing more precise real-time localization of critical pulmonary structures.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Lung Cancer Research for the series “Current Advances and Innovations in Surgical Lung Cancer Treatment”. The article has undergone external peer review.

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-460/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-460/coif). The series “Current Advances and Innovations in Surgical Lung Cancer Treatment” was commissioned by the editorial office without any funding or sponsorship. E.R.d.L. and A.J.P.M.F. served as the unpaid Guest Editors of the series. F.R.H. has unpaid international simulation-based professional job roles. A.H.S. is a co-inventor of PulmoVR. K.W.E.H., Y.L.J.V., and E.R.d.L. received consulting fees from Johnson & Johnson for education in uniportal VATS lobectomy. K.W.E.H. is a board member of the Dutch Federation of Medical Specialists. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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

References

1. Postmus PE, Kerr KM, Oudkerk M, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2017;28:iv1-iv21. [Crossref] [PubMed]
2. Riely GJ, Wood DE, Ettinger DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2024;22:249-74. [Crossref] [PubMed]
3. Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet 2022;399:1607-17. [Crossref] [PubMed]
4. Altorki N, Wang X, Kozono D, et al. Lobar or Sublobar Resection for Peripheral Stage IA Non-Small-Cell Lung Cancer. N Engl J Med 2023;388:489-98. [Crossref] [PubMed]
5. Brunelli A, Decaluwe H, Gonzalez M, et al. European Society of Thoracic Surgeons expert consensus recommendations on technical standards of segmentectomy for primary lung cancer. Eur J Cardiothorac Surg 2023;63:ezad224. [Crossref] [PubMed]
6. Bertolaccini L, Abu Akar F, Aigner C, et al. Optimal planning and management strategies for minimally invasive lung segmentectomies: an international Delphi consensus report. Eur J Cardiothorac Surg 2024;66:ezae351. [Crossref] [PubMed]
7. Dai J, Sun F, Bao M, et al. Pulmonary Function Recovery and Displacement Patterns After Anatomic Segmentectomy vs Lobectomy. Ann Thorac Surg 2024;118:365-74. [Crossref] [PubMed]
8. Xu Y, Qin Y, Ma D, et al. The impact of segmentectomy versus lobectomy on pulmonary function in patients with non-small-cell lung cancer: a meta-analysis. J Cardiothorac Surg 2022;17:107. [Crossref] [PubMed]
9. Mikhail Lette MN, Paez D, Shulman LN, et al. Toward Improved Outcomes for Patients With Lung Cancer Globally: The Essential Role of Radiology and Nuclear Medicine. JCO Glob Oncol 2022;8:e2100100. [Crossref] [PubMed]
10. Robb RA, Heffernan PB, Camp JJ, et al. A workstation for multi-dimensional display and analysis of biomedical images. Comput Methods Programs Biomed 1987;25:169-84. [Crossref] [PubMed]
11. Robb R. Biomedical imaging: past, present and predictions. Medical Imaging Technology 2006;24:25.
12. Pate D, Resnick D, Andre M, et al. Perspective: three-dimensional imaging of the musculoskeletal system. AJR Am J Roentgenol 1986;147:545-51. [Crossref] [PubMed]
13. Mahesh M. The Essential Physics of Medical Imaging, Third Edition. Med Phys 2013. doi: 10.1118/1.4811156.
14. Hounsfield GN. Nobel Award address. Computed medical imaging. Med Phys 1980;7:283-90. [Crossref] [PubMed]
15. Alnaser A, Gong B, Moeller K. Evaluation of open-source software for the lung segmentation. Curr Dir Biomed Eng 2016;2:515-8.
16. Matsumoto T, Kanzaki M, Amiki M, et al. Comparison of three software programs for three-dimensional graphic imaging as contrasted with operative findings. Eur J Cardiothorac Surg 2012;41:1098-103. [Crossref] [PubMed]
17. Bakhuis W, Sadeghi AH, Moes I, et al. Essential Surgical Plan Modifications After Virtual Reality Planning in 50 Consecutive Segmentectomies. Ann Thorac Surg 2023;115:1247-55. [Crossref] [PubMed]
18. Laven IEWG, Oosterhoff VPS, Franssen AJPM, et al. Evaluating three-dimensional lung reconstructions for thoracoscopic lung resections using open-source software: a pilot study. Transl Lung Cancer Res 2024;13:1595-608. [Crossref] [PubMed]
19. Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study. EBioMedicine 2023;87:104422. [Crossref] [PubMed]
20. Larici AR, Franchi P, Del Ciello A, et al. Role of delayed phase contrast-enhanced CT in the intra-thoracic staging of non-small cell lung cancer (NSCLC): What does it add? Eur J Radiol 2021;144:109983. [Crossref] [PubMed]
21. Maiello L, Ball L, Micali M, et al. Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning. Front Physiol 2021;12:725865. [Crossref] [PubMed]
22. Gao H, Liu C. Demarcation of arteriopulmonary segments: a novel and effective method for the identification of pulmonary segments. J Int Med Res 2021;49:3000605211014383. [Crossref] [PubMed]
23. Agur AMR, Dalley AF. Grant’s atlas of anatomy. 15th ed. Philadelphia: Wolters Kluwer; 2021.
24. Yao F, Wang J, Yao J, et al. Three-dimensional image reconstruction with free open-source OsiriX software in video-assisted thoracoscopic lobectomy and segmentectomy. Int J Surg 2017;39:16-22. [Crossref] [PubMed]
25. Oizumi H, Endoh M, Takeda S, et al. Anatomical lung segmentectomy simulated by computed tomographic angiography. Ann Thorac Surg 2010;90:1382-3. [Crossref] [PubMed]
26. van Rikxoort EM, de Hoop B, van de Vorst S, et al. Automatic segmentation of pulmonary segments from volumetric chest CT scans. IEEE Trans Med Imaging 2009;28:621-30. [Crossref] [PubMed]
27. Mimae T, Miyata Y, Kumada T, et al. The intersegmental pulmonary vein is not always located on the intersegmental plane of the lung: Evaluation with 3-dimensional volume-rendering image reconstruction. JTCVS Tech 2022;16:132-8. [Crossref] [PubMed]
28. Sarsam M, Glorion M, de Wolf J, et al. The role of three-dimensional reconstructions in understanding the intersegmental plane: an anatomical study of segment 6. Eur J Cardiothorac Surg 2020;58:763-7. [Crossref] [PubMed]
29. Laven IEWG, Verkoulen GHJM, Verkoulen KCHA, et al. Three-Dimensional Lung Reconstructions for Preoperative Planning of Uniportal Video-Assisted Thoracoscopic Segmentectomies Using Visible Patient Software. Innovations (Phila) 2025;20:87-95. [Crossref] [PubMed]
30. Guerrera F, Nicosia S, Costardi L, et al. Proctor-guided virtual reality-enhanced three-dimensional video-assisted thoracic surgery: an excellent tutoring model for lung segmentectomy. Tumori 2021;107:NP1-4. [Crossref] [PubMed]
31. Sandri A, Gagliasso M, Veltri A, et al. Report of an interactive three-dimensional anatomical model to be used as an intraoperative aid in lung anatomical resections for non-small lung cancer. Interact Cardiovasc Thorac Surg 2021;33:316-8. [Crossref] [PubMed]
32. Ikeda N, Yoshimura A, Hagiwara M, et al. Three dimensional computed tomography lung modeling is useful in simulation and navigation of lung cancer surgery. Ann Thorac Cardiovasc Surg 2013;19:1-5. [Crossref] [PubMed]
33. Chen Y, Zhang J, Chen Q, et al. Three-dimensional printing technology for localised thoracoscopic segmental resection for lung cancer: a quasi-randomised clinical trial. World J Surg Oncol 2020;18:223. [Crossref] [PubMed]
34. Ong BH. Video-assisted thoracoscopic left S8 segmentectomy guided by pre-operative 3D reconstruction in a patient with synchronous bilateral primary lung cancer: a case report. J Vis Surg 2023;9:45.
35. Wang X, Wang Q, Zhang X, et al. Application of three-dimensional (3D) reconstruction in the treatment of video-assisted thoracoscopic complex segmentectomy of the lower lung lobe: A retrospective study. Front Surg 2022;9:968199. [Crossref] [PubMed]
36. She XW, Gu YB, Xu C, et al. Three-dimensional (3D)- computed tomography bronchography and angiography combined with 3D-video-assisted thoracic surgery (VATS) versus conventional 2D-VATS anatomic pulmonary segmentectomy for the treatment of non-small cell lung cancer. Thorac Cancer 2018;9:305-9. [Crossref] [PubMed]
37. Liu Y, Zhang S, Liu C, et al. Three-dimensional reconstruction facilitates thoracoscopic anatomical partial lobectomy by an inexperienced surgeon: a single-institution retrospective review. J Thorac Dis 2021;13:5986-95. [Crossref] [PubMed]
38. Matsuura N, Igai H, Ohsawa F, et al. Novel thoracoscopic segmentectomy combining preoperative three-dimensional image simulation and intravenous administration of indocyanine green. Interact Cardiovasc Thorac Surg 2022;35:ivac064. [Crossref] [PubMed]
39. Smelt JLC, Suri T, Valencia O, et al. Operative Planning in Thoracic Surgery: A Pilot Study Comparing Imaging Techniques and Three-Dimensional Printing. Ann Thorac Surg 2019;107:401-6. [Crossref] [PubMed]
40. Liu X, Zhao Y, Xuan Y, et al. Three-dimensional printing in the preoperative planning of thoracoscopic pulmonary segmentectomy. Transl Lung Cancer Res 2019;8:929-37. [Crossref] [PubMed]
41. Xue L, Fan H, Shi W, et al. Preoperative 3-dimensional computed tomography lung simulation before video-assisted thoracoscopic anatomic segmentectomy for ground glass opacity in lung. J Thorac Dis 2018;10:6598-605. [Crossref] [PubMed]
42. Zhu XY, Yao FR, Xu C, et al. Utility of preoperative three-dimensional CT bronchography and angiography in uniportal video-assisted thoracoscopic anatomical lobectomy: a retrospective propensity score-matched analysis. Ann Transl Med 2021;9:480. [Crossref] [PubMed]
43. Zhang M, Liu D, Wu W, et al. Preoperative 3D-CT bronchography and angiography facilitates single-direction uniportal thoracoscopic anatomic lobectomy. Ann Transl Med 2019;7:526. [Crossref] [PubMed]
44. Kanzaki M, Isaka T, Kikkawa T, et al. Binocular stereo-navigation for three-dimensional thoracoscopic lung resection. BMC Surg 2015;15:56. [Crossref] [PubMed]
45. Sardari Nia P, Olsthoorn JR, Heuts S, et al. Interactive 3D Reconstruction of Pulmonary Anatomy for Preoperative Planning, Virtual Simulation, and Intraoperative Guiding in Video-Assisted Thoracoscopic Lung Surgery. Innovations (Phila) 2019;14:17-26. [Crossref] [PubMed]
46. Qiu B, Ji Y, He H, et al. Three-dimensional reconstruction/personalized three-dimensional printed model for thoracoscopic anatomical partial-lobectomy in stage I lung cancer: a retrospective study. Transl Lung Cancer Res 2020;9:1235-46. [Crossref] [PubMed]
47. Lustermans D, Abdulrahim R, Taasti VT, et al. Development of a novel 3D-printed dynamic anthropomorphic thorax phantom for evaluation of four-dimensional computed tomography. Phys Imaging Radiat Oncol 2024;32:100656. [Crossref] [PubMed]
48. Smits KC, Speekenbrink RGH, Hekman EEG, et al. Three-Dimensional Heart Modeling of Hypertrophic Obstructive Cardiomyopathy for In Situ Patient-Specific Simulation to Optimize Septal Myectomy. Innovations (Phila) 2024;19:532-40. [Crossref] [PubMed]
49. Zhang X, Yang D, Li L, et al. Application of three-dimensional technology in video-assisted thoracoscopic surgery sublobectomy. Front Oncol 2024;14:1280075. [Crossref] [PubMed]
50. E H. Three-dimensionally printed navigational template: a promising guiding approach for lung biopsy. Transl Lung Cancer Res 2022;11:393-403. [Crossref] [PubMed]
51. Sadeghi AH, Maat APWM, Taverne YJHJ, et al. Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies. JTCVS Tech 2021;7:309-21. [Crossref] [PubMed]
52. Grossi S, Cattoni M, Rotolo N, et al. Video-assisted thoracoscopic surgery simulation and training: a comprehensive literature review. BMC Med Educ 2023;23:535. [Crossref] [PubMed]
53. Doornbos MJ, Peek JJ, Maat APWM, et al. Augmented Reality Implementation in Minimally Invasive Surgery for Future Application in Pulmonary Surgery: A Systematic Review. Surg Innov 2024;31:646-58. [Crossref] [PubMed]
54. Piazzolla P, Gribaudo M, Buttiglione MD, et al. Lung operation training in low-cost virtual reality simulation environments. In: Proceedings - European Council for Modelling and Simulation. 2024:536-42.
55. Sadeghi AH, Mathari SE, Abjigitova D, et al. Current and Future Applications of Virtual, Augmented, and Mixed Reality in Cardiothoracic Surgery. Ann Thorac Surg 2022;113:681-91. [Crossref] [PubMed]
56. Sadeghi AH, Mank Q, Tuzcu AS, et al. Artificial intelligence-assisted augmented reality robotic lung surgery: Navigating the future of thoracic surgery. JTCVS Tech 2024;26:121-5. [Crossref] [PubMed]
57. Peek JJ, Zhang X, Hildebrandt K, et al. A novel 3D image registration technique for augmented reality vision in minimally invasive thoracoscopic pulmonary segmentectomy. Int J Comput Assist Radiol Surg 2025;20:787-95. [Crossref] [PubMed]
58. Aliwi I, Schot V, Carrabba M, et al. The Role of Immersive Virtual Reality and Augmented Reality in Medical Communication: A Scoping Review. J Patient Exp 2023;10:23743735231171562. [Crossref] [PubMed]
59. Tokuno J, Chen-Yoshikawa TF, Nakao M, et al. Resection Process Map: A novel dynamic simulation system for pulmonary resection. J Thorac Cardiovasc Surg 2020;159:1130-8. [Crossref] [PubMed]
60. Andolfi M, Potenza R, Seguin-Givelet A, et al. Identification of the intersegmental plane during thoracoscopic segmentectomy: state of the art. Interact Cardiovasc Thorac Surg 2020;30:329-36. [Crossref] [PubMed]
61. Hojski A, Hassan M, Mallaev M, et al. Planning thoracoscopic segmentectomies with 3-dimensional reconstruction software improves outcomes. Interdiscip Cardiovasc Thorac Surg 2025;40:ivaf043. [Crossref] [PubMed]
62. Vervoorn MT, Wulfse M, Mohamed Hoesein FAA, et al. Application of three-dimensional computed tomography imaging and reconstructive techniques in lung surgery: A mini-review. Front Surg 2022;9:1079857. [Crossref] [PubMed]
63. Xiang Z, Wu B, Zhang X, et al. Preoperative Three-Dimensional Lung Simulation Before Thoracoscopic Anatomical Segmentectomy for Lung Cancer: A Systematic Review and Meta-Analysis. Front Surg 2022;9:856293. [Crossref] [PubMed]
64. Lin MW, Chen JS. Image-guided techniques for localizing pulmonary nodules in thoracoscopic surgery. J Thorac Dis 2016;8:S749-55. [Crossref] [PubMed]
65. Hwang I, Ham S, Kim C, et al. Development of a CT image-based virtual atelectasis simulation model and noninvasive lung nodule localization system. J Thorac Dis 2024;16:7651-62. [Crossref] [PubMed]
66. Wu X, Li T, Zhang C, et al. Comparison of Perioperative Outcomes Between Precise and Routine Segmentectomy for Patients With Early-Stage Lung Cancer Presenting as Ground-Glass Opacities: A Propensity Score-Matched Study. Front Oncol 2021;11:661821. [Crossref] [PubMed]
67. Wu WB, Xia Y, Pan XL, et al. Three-dimensional navigation-guided thoracoscopic combined subsegmentectomy for intersegmental pulmonary nodules. Thorac Cancer 2019;10:41-6. [Crossref] [PubMed]
68. Ueda K, Aoki M, Kamimura G, et al. Intraoperative cone-beam computed tomography to secure the surgical margin in pulmonary wedge resection for indistinct intrapulmonary lesions. JTCVS Tech 2022;13:219-28. [Crossref] [PubMed]
69. Deng X, Liu Y, Chen H. Three-dimensional image reconstruction based on improved U-net network for anatomy of pulmonary segmentectomy. Math Biosci Eng 2021;18:3313-22. [Crossref] [PubMed]
70. Li C, Zheng B, Yu Q, et al. Augmented Reality and 3-Dimensional Printing Technologies for Guiding Complex Thoracoscopic Surgery. Ann Thorac Surg 2021;112:1624-31. [Crossref] [PubMed]
71. Roussin CJ, Weinstock P. SimZones: An Organizational Innovation for Simulation Programs and Centers. Acad Med 2017;92:1114-20. [Crossref] [PubMed]
72. Hamada A, Oizumi H, Kato H, et al. Learning curve for port-access thoracoscopic anatomic lung segmentectomy. J Thorac Cardiovasc Surg 2018;156:1995-2003. [Crossref] [PubMed]
73. Li S, Wu J, Wan Z, et al. The learning curve for uniportal video-assisted thoracoscopic anatomical segmentectomy. J Surg Oncol 2021;124:441-52. [Crossref] [PubMed]
74. Polaczek M, Szaro P, Jakubowska L, et al. Pulmonary veins variations with potential impact in thoracic surgery: a computed-tomography-based atlas. J Thorac Dis 2020;12:383-93. [Crossref] [PubMed]
75. Xie Z, Zhu X, Li F, et al. Pulmonary Arterial Anatomical Patterns: A Classification Scheme Based on Lobectomy and 3D-CTBA. Thorac Cardiovasc Surg 2024;72:557-67. [Crossref] [PubMed]
76. Li S, Zhou K, Wang M, et al. Degree of pulmonary fissure completeness can predict postoperative cardiopulmonary complications and length of hospital stay in patients undergoing video-assisted thoracoscopic lobectomy for early-stage lung cancer. Interact Cardiovasc Thorac Surg 2018;26:25-33. [Crossref] [PubMed]
77. Kuo CFJ, Lin KH, Weng WH, et al. Complete fully automatic segmentation and 3-dimensional measurement of mediastinal lymph nodes for a new response evaluation criteria for solid tumors. Biocybernetics and Biomedical Engineering 2021;41:617-35.
78. Yoon SH, Park S, Kang CH, et al. Personalized 3D-Printed Model for Informed Consent for Stage I Lung Cancer: A Randomized Pilot Trial. Semin Thorac Cardiovasc Surg 2019;31:316-8. [Crossref] [PubMed]
79. KhanASellynGEAliDImpact of 3D Printed Models on Shared Decision Making: A Cluster Randomized Controlled Trial.medRxiv:2025.01.27.25321192 [Preprint]. 2025. Available online: https://www.medrxiv.org/content/10.1101/2025.01.27.25321192.abstract
80. Groenier M, Spijkerboer K, Venix L, et al. Evaluation of the impact of technical physicians on improving individual patient care with technology. BMC Med Educ 2023;23:181. [Crossref] [PubMed]
81. Alvarez P, Chabanas M, Sikora S, et al. Measurement and Analysis of Lobar Lung Deformation After a Change of Patient Position During Video-Assisted Thoracoscopic Surgery. IEEE Trans Biomed Eng 2023;70:931-40. [Crossref] [PubMed]
82. Zhang X, Wang Z, Sun W, et al. Real-time non-uniform surface refinement model for lung adenocarcinoma surgery. Med Biol Eng Comput 2024;62:183-93. [Crossref] [PubMed]
83. Bakhuis W, Max SA, Nader M, et al. Video-assisted thoracic surgery S7 segmentectomy: use of virtual reality surgical planning and simulated reality intraoperative modelling. Multimed Man Cardiothorac Surg 2023; [Crossref]
84. Jafari P, Dempsey S, Hoover DA, et al. In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy. Comput Biol Med 2021;130:104231. [Crossref] [PubMed]
85. Jiang F, Hirano T, Liang C, et al. Multi-scale simulations of pulmonary airflow based on a coupled 3D-1D-0D model. Comput Biol Med 2024;171:108150. [Crossref] [PubMed]
86. Courreges F, Melloni B, Absi J. Design and comparison of computationally efficient uniaxial stress-strain models of the lung parenchyma for real-time applications. Comput Biol Med 2024;180:108928. [Crossref] [PubMed]
87. Alvarez P, Rouzé S, Miga MI, et al. A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery. Med Image Anal 2021;69:101983. [Crossref] [PubMed]