Evidence map›Paper›PMID 39583479›Full record

ArticleCureus2024

Cross-Professional Evaluation of 3D Visualization of Liver Malignancies in the Decade of AI and Automatic Segmentation: A Benefit for Multidisciplinary Teams and Tumor Board Decisions?

Christian Heiliger, Dorian Andrade, Lucas Etzel, Daniel Roessler, Vanessa F Schmidt, Florian Boesch, Jens Ricke, Jens Werner, Konrad Karcz, Olga Solyanik

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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.

Christian HeiligerDepartment of General, Visceral and Transplant Surgery, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Dorian AndradeDepartment of General, Visceral and Transplant Surgery, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Lucas EtzelDepartment of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, DEU.
Daniel RoesslerDepartment of Internal Medicine II - Gastroenterology, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Vanessa F SchmidtDepartment of Radiology, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Florian BoeschDepartment of General, Visceral, and Pediatric Surgery, University Medical Center Göttingen (UMG), Göttingen, DEU.
Jens RickeDepartment of Radiology, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Jens WernerDepartment of General, Visceral and Transplant Surgery, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Konrad KarczDepartment of General, Visceral and Transplant Surgery, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.
Olga SolyanikDepartment of Radiology, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, DEU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose To investigate whether automatic 3D visualization of computed tomography (CT) data sets with singular liver tumor compared to 2D images could foster a broader understanding of tumor localization and resectability in the liver within a multidisciplinary team and might therefore be a useful tool in multidisciplinary decision-making. Material and methods The study was configured as a web-based questionnaire. Physicians of all levels of medical training from surgery, radiology, and gastroenterology departments were recruited. A total of seven cases with singular liver tumor CT images with adequate quality were selected. Automatic 3D segmentation was performed using Universal Atlas (Release 5.0) as part of the Brainlab of Elements software suite (Brainlab AG, Munich, Germany). All cases were randomly presented in a 2D and 3D manner. After every case-presentation, multiple choice (single answer) questions concerning tumor extent and resectability were asked. The questions as well as the answers defined to be correct, were evaluated by two senior consultants from the radiology and surgery department. The primary outcome parameters were the correctness of answers stratified for medical specialty and for the level of medical training. The secondary outcome was the time needed for the evaluation of seven liver cases using 2D versus 3D images. Six additional questions were tailored to evaluate the subjective value of the 3D visualization.  Results A total of 92 participants participated in the study, 31.5% of them were abdominal surgeons, 34.8% gastroenterologists, and 33.7% radiologists. Based on the level of medical training, 66 were residents (71.7%) and 26 consultants (28.3%). Only radiologists answered more questions correctly using 2D imaging compared to the 3D method (p = 0.006). There was no statistically significant difference between correctly answered questions when using 2D vs. 3D visualization in the gastroenterologist and surgeon groups (p > 0.05). The resident subgroup showed no statistically significant difference when using the 2D vs. 3D images (p > 0.05), the consultant subgroup answered more questions correctly using 2D imaging (p = 0.009). Physicians with elementary experience of liver pathology also showed no difference in 2D vs. 3D (p = 0.332), physicians with proficient experience of liver pathology answered more questions correctly using 2D imaging (p = 0.010). The median time taken for the evaluation of the seven liver cases was only significantly faster for the gastroenterologist group (p = 0.006) using the 3D analysis (median: 9.1 minutes) than the 2D analysis (median: 10.7 minutes).  Over 80% of the participants found the 3D presentation to be a helpful additional tool for the clinical routine according to the subjective questionnaire. Conclusion In this study 3D visualization of liver tumors was evaluated as helpful within a multidisciplinary team of radiologists, surgeons, and gastroenterologists. However, significantly superior results in the understanding of liver anatomy could not be demonstrated by means of 3D visualization. It may be that more immersive technologies such as augmented reality or virtual reality will lead to a superior understanding compared to conventional presentation of information in 2D cross-sectional images.

Indexed as

3d reconstructionautomatic segmentationinterdisciplinary decision makingliver malignanciesliver surgery

Identifiers

PMID39583479
PMCPMC11585349

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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.