Evidence map›Paper›PMID 41285152›Full record

ArticleZentralblatt fur Chirurgie2025

Interdisciplinary Collaborative Virtual Reality Planning for Chest Wall Resection and Reconstruction for Sarcoma and Other Large Chest Wall Malignancies Enhanced by Automated AI Segmentation: A Retrospective Comparative Analysis.

Philipp Schnorr, Benedetta Bedetti, Jan Wynands, Sebastian Koob, Hruy Menghesha, Jens Buermann, Donatas Zalepugas, Jan Arensmeyer, Joachim Schmidt, Philipp Feodorovici

Abstract readComparative Study
In one paragraph

Article in Zentralblatt fur Chirurgie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Philipp Schnorr *Department of Thoracic Surgery, Helios Hospital Bonn Rhein-Sieg, Bonn, Germany.ORCID 0000-0002-8853-9371
Benedetta Bedetti *Department of Thoracic Surgery, Helios Hospital Bonn Rhein-Sieg, Bonn, Germany.
Jan WynandsDepartment of General, Visceral, Vascular and Transplant Surgery, University Hospital Bonn, Bonn, Germany.
Sebastian KoobDepartment of Orthopedics and Trauma Surgery, University Hospital Bonn, Bonn, Germany.
Hruy MengheshaDepartment of Thoracic Surgery, Helios Hospital Bonn Rhein-Sieg, Bonn, Germany.
Jens BuermannDepartement of Thoracic Surgery, University Hospital Bonn, Bonn, Germany.
Donatas ZalepugasDepartement of Thoracic Surgery, University Hospital Bonn, Bonn, Germany.
Jan ArensmeyerDepartement of Thoracic Surgery, University Hospital Bonn, Bonn, Germany.ORCID 0000-0003-0705-2973
Joachim SchmidtDepartement of Thoracic Surgery, University Hospital Bonn, Bonn, Germany.
Philipp FeodoroviciDepartement of Thoracic Surgery, University Hospital Bonn, Bonn, Germany.ORCID 0009-0000-3940-0501

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oncologic chest wall resection and reconstruction present significant surgical challenges due to the complex interplay of anatomical and physiological factors. Ensuring adequate oncologic margins while preserving structural integrity and function is essential for optimal oncological and physiological patient outcomes. Advanced visualization technologies such as virtual reality (VR) are being increasingly investigated for surgical use cases because of their ability to provide a comprehensive and immersive representation of anatomical structures, thereby enhancing preoperative planning and, potentially, intraoperative guidance. The goal of this study is to establish a streamlined workflow using state-of-the-art technology to optimize surgical planning and potentially improve patient outcomes in the complex field of chest wall reconstruction.Eight cases of complex chest wall resection were retrospectively analyzed using the "Medical Imaging XR" VR platform with AI-driven anatomical auto segmentation. An interdisciplinary team of surgeons collaboratively planned the surgical procedures in VR, and predicted parameters such as resection extent, defect dimensions, and reconstruction strategies. These were then quantitatively compared with actual intraoperative findings. User experience was assessed with the User Experience Questionnaire (UEQ), workspace perception ratings, and Simulator Sickness Questionnaire (SSQ).In 3 cases (37.5%), the actual resection exceeded the VR-predicted extent due to underestimated tumor infiltration. Planning exceeded resection in 50% of cases by up to 24% and one case (12.5%) showed a large overestimation in VR. UEQ scores showed high hedonic quality (Stimulation = 2.19, Novelty = 2.69) and positive pragmatic usability (Efficiency = 1.13, Dependability = 1.63). Workspace perception was favorable (mean 4.9/6), and cybersickness remained low.AI-enhanced VR planning enables interdisciplinary collaboration and can improve spatial understanding in complex chest wall surgery. Although it facilitates structured preoperative planning and communication, it should be viewed as a complementary tool to select the surgical strategy rather than as a definitive predictor of the extent of resection. Limitations in imaging resolution and segmentation accuracy can lead to under- or overestimation of tumor boundaries. Further development and clinical validation are necessary to determine its full impact on surgical planning quality and outcomes.

Indexed as

Artificial IntelligenceInterdisciplinary CommunicationIntersectoral CollaborationPlastic Surgery ProceduresSarcomaSurgery, Computer-AssistedThoracic NeoplasmsThoracic WallVirtual RealityAdultAgedFemaleHumansMaleMiddle AgedPatient Care Team

Identifiers

PMID41285152
PMCPMC12643786

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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.