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

Three-dimensional (3D) lung models have become a valuable tool in surgical planning and intraoperative navigation by providing detailed visualizations of pulmonary structures (e.g., bronchial tree, pulmonary vasculature, lung parenchyma, and tumor). These models are useful in minimally invasive lung cancer surgery, particularly for more complex segmentectomy procedures, where precise anatomical understanding is important. Enhancing the spatial insight of patient-specific anatomical variations facilitates precise lung nodule localization, supports decision-making regarding the extent of lung resection, and contributes to a smoother surgical procedure and intraoperative efficiency. In contrast to earlier reports that have addressed specific technologies, this review article provides a thorough overview of the advancements in 3D lung modeling, covering various technologies and their clinical applications—from conventional on-screen visualizations to advanced imaging modalities such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and insights into the future of real-time dynamic lung simulations in minimally invasive lung cancer surgery. In addition, this review touches upon the various segmentation methods, such as surface rendering, volume rendering, and artificial intelligence (AI) algorithms, and different types of software programs (i.e., commercial and open-source software programs) for developing these 3D lung models. It emphasizes its practical integration in thoracic surgical practice, highlighting the clinical value in preoperative planning and intraoperative guidance, with evidence showing improved surgical outcomes and reduced surgery duration for both segmentectomy and lobectomy, along with the added value for education, training purposes, and enhanced patient counseling. Evidence should be strengthened through more robust comparative studies evaluating different (advanced) imaging modalities and software programs to demonstrate their cost-effectiveness. Moreover, technical challenges in the integration of these 3D modeling tools must be overcome to limit the need for specialized software and personnel.