ReviewTranslational lung cancer research2025
Advancements in 3D lung models for minimally invasive lung cancer surgery: from static to real-time dynamic modeling.
Review in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Augmented Reality for Navigation in Minimally Invasive Abdominal and Thoracic Soft-Tissue Surgery: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Three-Dimensional Bronchovascular Modelling in Sublobar Pulmonary Resection: A Tool for Personalised Thoracic Surgery.Journal of personalized medicine · 2026Review
- The future of lung cancer surgery lies between innovations: synergy across the perioperative ERATS pathway.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.