Evidence map›Paper›PMID 39220654›Full record

ArticleFrontiers in oncology2024

Application of 3D reconstruction and 3D printing technology in advanced ovarian cancer surgery: a retrospective study.

Zhihui Cai, Ke Zhang, Linqian Li, Yuping Suo

Abstract read
In one paragraph

Article in Frontiers in oncology, 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
  2. Article
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

4 authors.

Zhihui CaiGynecology Department, The Fifth Clinical Medical School of Shanxi Medical University, Taiyuan, Shanxi, China.
Ke Zhang3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
Linqian Li3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
Yuping SuoGynecology Department, The Fifth Clinical Medical School of Shanxi Medical University, Taiyuan, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Backgrounds: Advanced ovarian cancer is frequently accompanied by extensive peritoneal metastasis, complicating surgical interventions. This study aims to explore the application of 3D reconstruction and 3D printing technology in the treatment of advanced ovarian cancer. Methods: We conducted a retrospective analysis of 60 patients with stage III ovarian cancer who underwent cytoreductive surgery at Hebei University Affiliated Hospital between 2020 and 2023. Patients were randomly assigned to three groups: a 3D visualization group, a 3D visualization plus 3D printing group, and a traditional 2D CT imaging evaluation group. High-precision medical imaging techniques (e.g., CT, MRI) were employed to create digital 3D models, which were then converted into physical entities using 3D printing for surgical planning and simulation. Results: Both the 3D visualization group and the 3D visualization plus 3D printing group demonstrated superior outcomes in terms of surgery duration and blood loss compared to the traditional 2D CT group, indicating the efficacy of 3D reconstruction and 3D printing in preoperative planning. Postoperative recovery indicators, such as hospital stay and time to first flatus, were also more favorable in the groups utilizing 3D technology. Although there were no significant differences in postoperative complications and recurrence rates among the three groups, the groups using 3D technology showed advantages in reducing certain complications. Conclusions: The results indicate that medical 3D technology has significant value in the surgical planning of advanced ovarian cancer, enhancing surgical precision and reducing intraoperative risks, which may aid in improving postoperative recovery.

Indexed as

3D printing3D visualizationintraoperative risksovarian cancerpreoperative planning

Identifiers

PMID39220654
PMCPMC11361939

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Registered trials

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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.