Central lung cancer: improving operability assessment through 3D modelling

Abstract

Background

The objective of this study was to develop a novel method for creating highly detailed three-dimensional physical models of lung lobes, incorporating tumour morphology and surrounding structures, with the aim of improving the assessment of operability for central lung tumours.

Case presentation

A method was developed that uses standard computed tomography (CT) scans to mark the desired structures and generate a three-dimensional image for physical model creation. The generated STL files can be seamlessly integrated into virtual reality, allowing the sharing of selected CT scan data. Our approach has been successfully integrated into clinical practice, enabling multidisciplinary teams to make informed decisions for patients with central lung tumours. We have reduced the preparation time of physical models from 100 h to 18 h.

Conclusions

The novel method, which employs 3D printing technology, has enhanced the assessment of operability for central lung tumours, thereby facilitating more precise decisions regarding patient management. This innovative approach has the potential to enhance patient outcomes by reducing complications and optimizing treatment planning.

Background

Lung cancer is the leading cause of cancer-related mortality worldwide, representing a significant medical, social, and economic challenge. The high mortality rate is largely due to late diagnosis, underscoring the urgent need for advances in early detection. Although surgical resection remains the mainstay of treatment, only 20% of cases are currently considered operable [1]. While improvements in adherence to screening will help to identify more early-stage disease and thus improve operability rates, advances in the visualization of central tumours may aid in the determination of operability for tumours considered borderline for an operation. Unfortunately, these central tumours are sometimes deemed inoperable without comprehensive evaluation of all surgical possibilities.

Central lung tumours present unique surgical challenges due to their proximity to vital structures such as the pulmonary artery, main bronchi, superior vena cava, and other mediastinal structures. These tumours frequently involve or abut hilar vessels and airways, creating complex three-dimensional relationships that are difficult to appreciate on conventional imaging. The surgical approach to these tumours requires meticulous planning to achieve complete resection while preserving essential structures. A millimetre-level misjudgement in the spatial relationship between tumour and adjacent critical structures can mean the difference between an achievable, potentially curative resection and an abandoned operation. This critical spatial assessment makes central lung tumours particularly suitable candidates for advanced visualization techniques such as 3D modelling.

Assessing the operability of central lung tumours is a significant challenge, particularly when it comes to accurately determining tumour invasion into vital or difficult-to-remove structures [2]. Using conventional CT scans, the operability decision is sometimes very difficult, and the tumour may appear to be inoperable, often leading the surgeon not to refer the patient for surgery. The determination of operability is contingent upon the surgeon’s capacity to visualize the tumour’s spatial relationship with surrounding structures. However, this is a challenging task, as it necessitates the surgeon’s ability to mentally reconstruct the intricate relationships between the tumour and its surrounding environment.

Computed tomography (CT) remains the primary diagnostic tool for lung cancer and has several well-established advantages. Standard CT is widely available, relatively inexpensive, and provides adequate contrast between the lung and mediastinal structures. However, it is important to recognize that chest CT has limitations and pitfalls, particularly in assessing morphological relationships in central lung tumours [3].

The limitations of conventional CT imaging are particularly evident in the assessment of central lung tumours. While CT scans provide excellent axial views, the three-dimensional relationships between tumours and adjacent critical structures—such as the pulmonary artery, bronchial tree, and mediastinal structures—must be mentally reconstructed by the surgeon from sequential two-dimensional images. This mental reconstruction process is inherently subject to individual interpretation and can lead to inconsistent assessments of operability. Studies across various surgical disciplines have demonstrated that physical 3D models offer superior spatial comprehension compared to traditional imaging methods. Silberstein et al. [4] and Westerman et al. [5] confirmed higher retention of anatomical relationships with physical 3D models in urology compared to original imaging modalities. Similar benefits for preoperative planning and patient education were reported by Panesar et al. [6] in neurosurgery. As documented by Coles-Black et al. [7], these models can also serve as patient-specific phantoms for refining instruments for endovascular interventions. The utility and benefits of 3D printing have been further established by Fidanza et al. in traumatology [8], Perica et al. in hepatobiliary surgery [9], Pontiki et al. in thoracic surgery [10], and Wang et al. in transplantation surgery [11]. This growing body of evidence suggests that three-dimensional physical representations of anatomical structures offer intrinsic advantages in spatial comprehension that may significantly impact surgical decision-making for central lung tumours.

Three-dimensional (3D) printing is emerging as a promising modern tool in medical diagnostics, and its rapid development is impacting various medical fields [12]. The objective of our study is to present preliminary results from our ongoing development of a method to produce a highly detailed 3D physical model of the lung lobe, incorporating the morphological proportions of the tumour and surrounding structures. This model greatly improves our understanding of the relationship between the tumour and surrounding structures, allowing a more accurate assessment of operability. The model permits an accurate assessment of the location of the tumour within the lung parenchyma and its relationship to the surrounding structures.

Case presentation

A novel method for creating highly detailed 3D physical models of lung lobes has been developed, incorporating tumour morphology and surrounding structures. The approach utilizes standard CT scans of the lungs, which are then tagged to identify the structures of interest. From these tagged images, a 3D image is generated, which can be used to create a physical model (Fig. 1).

Fig. 1.

Workflow for generating a 3D printable model from CT scans. The figure comprises three sections: (A) Coronal, (B) Axial and (C) Sagittal. Each section displays a color-coded structure. Additionally, sections (D) and (E) present a 3D rendered colour model, which shows the tumour and adjacent structures (pulmonary artery and bronchus). The figure illustrates the process of producing a 3D printable model from CT images, which enables the creation of customized surgical models for pre-operative planning and simulation

For image acquisition, we utilize a 128-row multidetector CT scanner (Somatom Definition AS+) with 0.75 mm slice thickness. This level of detail is essential for accurate rendering of fine anatomical structures; thicker slices (e.g., 2.5 mm) would be insufficient to capture the intricate details necessary for precise surgical planning. While contrast enhancement facilitates optimal visualization and differentiation of vascular structures, particularly for distinguishing between pulmonary veins and arteries beyond third-order branching, it is not an absolute requirement for basic model generation. Standard chest CT protocol with intravenous contrast is employed, without need for specialized acquisition parameters beyond those used in routine diagnostic imaging.

Fused deposition modelling (FDM) technology and polyethylene terephthalate glycol (PETG) material, in conjunction with commercially available printers, are employed to produce accurate models. For clarity, we provide explanations of the key technical terminology used in our 3D printing approach: Fused Deposition Modeling (FDM) refers to an additive manufacturing process where materials are deposited layer by layer to create three-dimensional objects. We selected this technology due to its cost-effectiveness, widespread availability, and ability to produce models with sufficient anatomical detail for surgical planning. Polyethylene Terephthalate Glycol (PETG) is a commonly used thermoplastic polymer in 3D printing, selected for our application due to its durability, transparency options, and biocompatibility. This material, also used in the production of water bottles and food containers, offers an optimal balance between rigidity and flexibility for handling surgical models. STL (STereoLithography) is the standard file format used for 3D models across various printing platforms, allowing for seamless transfer between imaging software and printing hardware. This format efficiently represents complex anatomical surfaces through triangular mesh structures while maintaining reasonable file sizes for processing.

The technical workflow for creating our 3D models begins with importing DICOM data from the CT scanner into 3D Slicer, an open-source software platform for medical image informatics. Within this environment, manual annotation and segmentation of anatomical structures are performed by experienced surgeons according to established institutional protocols. Key structures including the tumour, bronchial tree, pulmonary arteries, and when necessary, pulmonary veins are identified and labelled using the segment editor module. Segmentation is performed primarily through a combination of threshold-based techniques and manual refinement to ensure anatomical accuracy. The segmented structures are then exported as individual STL files, which undergo optimization for printing using mesh processing software to ensure printability while preserving anatomical detail. For the physical printing process, we utilize commercially available FDM printers (Prusa i3 MK3S+) with a build volume of 250 × 210 × 210 mm and layer resolution of 0.05 mm. Print parameters include 0.1–0.2 mm layer height, 15–20% infill density, and printing temperatures between 230 and 245 °C for PETG filament.

The STL files generated can be seamlessly integrated into virtual reality, allowing the sharing of selected CT scan data. This innovative approach has been successfully integrated into clinical practice, enabling multidisciplinary teams to make informed decisions for patients with central lung tumours.

Over the past two years, we have collaborated with a partner university to develop 3D decision support models. Currently, we are utilizing the fourth-generation model, which is fully coloured and highly detailed. In practice, we employ the representation of the tumour and the vascular branches of the pulmonary artery and the bronchial tree (Fig. 2). An important finding is the position of the tumour in relation to these structures, which is essential for performing a lobectomy. In the event that a model with the identification of the lung segments and an estimate of the appropriate resection line for a possible segmentectomy is required, we are able to provide this.

Fig. 2.

A 3D-printed lung model demonstrates the precise location of a central lung tumour. This model was constructed based on the CT scans shown in Fig. 1. This simplified lung model depicts the essential structures without parenchyma or pulmonary veins. The tumour (shown in black) is situated in close proximity to the bifurcation of the pulmonary artery (red) and bronchi (grey), highlighting its critical location within the lung anatomy. This detailed representation allows surgeons to assess the feasibility of surgical procedures, including the precise location and distances required for safe ligation of the pulmonary artery. Such information is crucial in informing surgical decision-making and planning

Our clinical experience with 3D models has demonstrated their value across multiple domains of patient care. In the context of the multidisciplinary team (MDT), these models have facilitated more informed discussions, particularly among non-surgical specialists who may have less experience interpreting complex CT relationships. In five cases initially assessed as borderline operable based on CT imaging alone, the 3D model visualization led to consensus regarding surgical feasibility, resulting in successful resections with negative margins.

Additionally, these models have proven valuable in-patient education and informed consent processes. The tangible nature of the models allows patients to better comprehend the extent of their disease and planned surgical intervention. This aligns with findings from Seok et al. (2021) [13], who demonstrated in a randomized clinical study that personalized 3D models improved the informed consent process for thyroid surgery. In our cohort, surgeons reported that patient understanding of the procedure and potential complications was noticeably enhanced when using the 3D models as educational tools, which anecdotally contributed to improved compliance with postoperative care instructions.

The indications for central lung tumours at our institution are determined by a multidisciplinary team of specialists. The determination of operability for central tumours is based on the results of the computed tomography (CT) scan and the surgeon’s assessment of the tumour’s operability. The introduction of three-dimensional physical models of the lung into this decision-making process has refined the assessment of operability and increased the proportion of patients indicated for surgery.

The time required to create such a model has been reduced from 100 h to 18 h. Further acceleration can be achieved using high-speed 3D printers, such as the Prusa XL with five printheads, which is available at a cost of approximately 4600 EUR. This system allows for significantly faster model production, with material costs remaining below 50 EUR per model. It is unnecessary to supplement the preparation of the physical model with additional examinations. A 3D computer model is generated from a standard CT scan performed with contrast, which is then printed in physical form. We have already achieved an accuracy of 0.75 mm in resolving the position of the tumour and boundary structures on the physical 3D model, which is equivalent to the slice thickness of a standard CT scanner.

To date, 41 detailed physical 3D models have been used in practice (Table 1). Of these models, 19 first- and second-generation prototypes met the criteria for basic test models. They were evaluated for correct printing technique, error avoidance, material suitability and printer calibration.

Table 1.

Focus of the models and their clinical use

#	FOCUS OF MODELS	CLINICAL USE
1–19	General model of the lungs and heart	Test model
20–34	General model of lungs without venous system	Test model
35	Left lung with tumour	Improving surgical decisions
36	Right lung with tumour	Improving surgical decisions
37	Upper left lobe with tumour	Improving surgical decisions
38	Upper right lobe with tumour without parenchyma	Improving surgical decisions
39	Arterial and bronchial tree of the upper right lobe with tumour	Improving surgical decisions
40	Arterial and bronchial tree of the upper left lobe with tumour	Improving surgical decisions
41	Arterial and bronchial tree of the upper left lobe with tumour with reduction of structures	Improving surgical decisions

Subsequently, we developed 15 third-generation models, focusing on print efficiency. We reduced the complexity of certain structures, such as the venous system, parenchyma and surrounding organs like the heart. Testing of these third-generation models resulted in a definitive protocol for printing 3D models that are now utilised in clinical practice for preoperative planning.

To date, seven fourth-generation models have been created and implemented in clinical practice. These models specifically address the surgical challenge at hand and provide the optimal visualisation for the surgeon.

The use of 3D models is expanding at a notable pace globally. The academic and educational use of this technology is the dominant application, and we are unaware of any similar models being used in practice for the diagnostic and therapeutic process.

Discussion

The utilization of three-dimensional printing in the diagnosis and treatment of lung cancer offers several advantages. Primarily, it enables surgeons to visualize the tumour and surrounding structures with greater precision and detail, facilitating a more comprehensive understanding of the tumour’s relationship to vital structures such as the pulmonary artery and trachea. This enhanced comprehension enables surgeons to make more informed decisions regarding patient treatment, including the decision to proceed with surgery.

It is important to acknowledge that, unlike CT imaging which provides detailed internal tissue characterization, 3D physical models primarily represent surface morphology and spatial relationships. This inherent limitation, however, does not significantly impact our specific clinical application. Our models were exclusively utilized for patients with either histologically confirmed malignancy or radiologically highly suspicious lesions requiring surgical intervention. The critical surgical consideration in lung cancer resection is not the internal structure of the tumour—as surgeons do not intentionally dissect through the tumour during resection—but rather its relationship to surrounding vital structures that must be preserved or safely managed during surgery. Tumours are removed with adequate margins, with surgical dissection occurring in the surrounding tissue planes rather than within the lesion itself. Therefore, the spatial representation of the tumour in relation to the pulmonary vasculature and bronchial structures offers more clinically relevant information for surgical planning than internal tumour architecture. The 3D models thus complement rather than replace conventional CT imaging in the preoperative assessment workflow.

Secondly, 3D printing enables the creation of customized surgical models that can be used to plan and rehearse surgical procedures. These models potentially offer advantages in preoperative visualization and surgical strategy development. While it is reasonable to hypothesize that enhanced preoperative planning might contribute to improved surgical outcomes, we acknowledge that definitive evidence demonstrating reduced complications or improved patient outcomes specifically attributable to 3D model utilization is currently limited. Our ongoing prospective study aims to address these questions with appropriate methodological rigor. At present, our findings support the utility of 3D models primarily for feasibility assessment and surgical planning for challenging central lung tumours, without established evidence for downstream clinical benefits. The potential for 3D models to reduce the need for additional imaging studies also remains a theoretical consideration requiring further investigation.

Our current methodology utilizes a fourth-generation three-dimensional physical model, which, based on our experience, provides the most accurate representation of critical structures for assessing surgical feasibility. The evolution of these models has seen a transition from comprehensive lung and heart models to more focused representations, now excluding the venous system and parenchyma. This refinement allows us to concentrate on structures essential for lung lobe resection.

The primary application of 3D models is to evaluate the feasibility of surgical resection and to guide the decision between lobectomy and pneumonectomy. This preoperative evaluation has gained additional significance with our adoption of the Da Vinci robotic system for anatomical resections, which lacks tactile feedback. Enhanced understanding of tumour positioning within the risk terrain enables us to avoid unnecessary preparation, thereby reducing the risk of bleeding.

Our experience to date demonstrates the potential impact of 3D modelling on surgical decision-making and outcomes. In one notable case, examination of the 3D model led to the re-evaluation of a patient initially deemed inoperable, resulting in successful surgical resection. Furthermore, surgeons have consistently reported that the 3D models provide a clearer understanding of tumour location and, crucially, the presence and variations in pulmonary artery branches. This enhanced anatomical insight has proven highly beneficial during both preoperative planning and the surgical procedure itself.

While automated CT image processing software has historically faced limitations in accuracy and information management [14], it is important to acknowledge the significant advancements in artificial intelligence applications for medical imaging. Deep learning algorithms for vascular segmentation have been developed and continue to evolve rapidly. These technologies show promising capabilities in automating the segmentation of complex structures, including components of the pulmonary vasculature and the bronchial tree. However, at the time of our study implementation, the fully automated, high-precision segmentation of all relevant structures—especially in the context of pathological conditions such as central lung tumours—still presented challenges in routine clinical applications. The role of 3D printing in surgical planning was highlighted early on by Blackmon et al. [15], demonstrating its value in complex thoracic procedures.

The field of AI-assisted medical image segmentation is advancing at an extraordinary pace, with improvements in both accuracy and accessibility emerging continuously. As these technologies mature and become more widely integrated into clinical workflows, the human intervention currently required in our image processing pipeline may be significantly reduced. This evolution represents an exciting opportunity to streamline the creation of detailed anatomical models for preoperative planning, potentially making this approach more broadly accessible in thoracic surgical practice.

The introduction of 3D models into clinical practice presents challenges, including a steep learning curve and the need for multidisciplinary expertise in model creation. Although these models increase diagnostic and therapeutic costs, their relative affordability is promising. Our presented central lung tumour model costs approximately 90 euros (Fig. 2). Given this favourable price point and the potential benefits in surgical planning, we anticipate successfully incorporating 3D models into our practice in the coming years. However, ongoing evaluation of their cost-effectiveness and clinical impact will be essential to optimize their use.

In the context of healthcare resource constraints, judicious selection of cases for 3D model creation is prudent. Currently, our implementation remains experimental, with costs covered by departmental research funds rather than patient billing or insurance reimbursement. Based on our preliminary experience, we recommend prioritizing 3D modelling for specific clinical scenarios: (1) central tumours with equivocal relationships to critical vasculature or bronchial structures on conventional imaging; (2) cases where multidisciplinary opinions diverge regarding resectability; (3) planning of complex anatomical segmentectomies requiring precise identification of intersegmental planes; and (4) cases selected for robotic-assisted resection where tactile feedback is absent. Institutions considering implementing similar approaches should establish clear selection criteria based on their specific patient population, surgical expertise, and financial constraints. As this technology continues to evolve and potentially becomes more cost-effective, broader application may become feasible. Future research should include formal cost-effectiveness analyses incorporating not only the direct costs of model production but also potential savings from avoided unnecessary surgeries, reduced operative time, and improved patient outcomes.

This study provides initial insights into the implementation of 3D printing technology in thoracic oncology, but several limitations should be noted. The relatively small sample size of 41 3D models, including only seven models used clinically, limits the generalisability of our findings. While we observed promising results in surgical decision-making, quantitative data on long-term patient outcomes are currently lacking. The creation of these models still requires significant human intervention in image processing, introducing potential variability. Additionally, the study was conducted at a single institution, which may limit the transferability of our findings to other healthcare settings.

Despite these limitations, our work represents an important first step in evaluating the potential of 3D printing in lung cancer management. While focusing primarily on technical implementation, we have initiated a prospective study evaluating quantitative surgical outcomes in patients with central lung tumors who undergo surgery with 3D model-assisted planning. A comprehensive assessment of the technology’s impact, including a thorough cost-effectiveness analysis will be provided in the coming years as we continue to gather data and refine our approach.

In conclusion, the application of three-dimensional printing technology in the diagnosis and treatment of lung cancer represents a promising advancement in thoracic oncology. Particularly, its potential in treatment planning for central and borderline operable lung cancers suggests the possibility of improved patient outcomes. This technology may facilitate more informed decision-making by surgeons regarding patient management through enhanced preoperative visualisation. Consequently, it may contribute to reducing the risk of intraoperative and postoperative complications. Further research is warranted to quantify the long-term impact of 3D printing technology on surgical outcomes and patient prognosis in lung cancer management.

Author contributions

LT and JH contributed equally to this work and share first authorship. LT conceptualized the study, developed the methodology, led the investigation, wrote the original draft, reviewed and edited the manuscript, prepared visual materials, and administered the project. JH was responsible for the preparation of 3D models, including technical aspects and data curation. MP provided technical consultation and contributed to the preparation of 3D models, including software development and validation. All authors have read and agreed to the published version of the manuscript.

Funding

Supported by MH CZ — DRO (FNOs/2025).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of University Hospital Ostrava and was conducted in accordance with the Declaration of Helsinki. All patients provided written informed consent to participate in the study and to the use of their data for publication.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.