Evidence map›Paper›PMID 38114507›Full record

ArticleNature communications2023

3D printing of self-healing personalized liver models for surgical training and preoperative planning.

Yahui Lu, Xing Chen, Fang Han, Qian Zhao, Tao Xie, Jingjun Wu, Yuhua Zhang

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06006338 (The Clinical Value of 3D Self-healing Elastic Liver Model for Surgical Training and Preoperative Planning), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 7% of its field
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.

NCT06006338 phase1unknown statusnot on this map

The Clinical Value of 3D Self-healing Elastic Liver Model for Surgical Training and Preoperative Planning: a Preliminary Study

TypeinterventionalSponsorZhejiang Cancer HospitalRan2023 to 2024Enrolled5ConditionsLiver CancerArms3D Printed Model for preoperative planning
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Couinaud segment-aware deep learning on point clouds for major liver resection planning.International journal of computer assisted radiology and surgery · 2026
    Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Recent Advances in Electrospinning Techniques for Precise Medicine.Cyborg and bionic systems (Washington, D.C.) · 2024
    Review
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

7 authors at 3 institutions in 1 country.

Yahui Lu *State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.
Xing Chen *Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
Fang HanZhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
Qian ZhaoState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.ORCID 0000-0002-6931-9859
Tao XieState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.ORCID 0000-0003-0222-9717
Jingjun WuState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China. jingjunwu@zju.edu.cn.ORCID 0000-0002-9026-5541
Yuhua ZhangZhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China. zhangyuhua1013@126.com.ORCID 0000-0002-1308-1992
Chinese Academy of Sciences · CNState Key Laboratory of Chemical Engineering · CNNingbo University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52273112National Natural Science Foundation of China (National Science Foundation of China) U20A6001
6 · The paper itself

Abstract

3D printing can produce intuitive, precise, and personalized anatomical models, providing invaluable support for precision medicine, particularly in areas like surgical training and preoperative planning. However, conventional 3D printed models are often significantly more rigid than human organs and cannot undergo repetitive resection, which severely restricts their clinical value. Here we report the stereolithographic 3D printing of personalized liver models based on physically crosslinked self-healing elastomers with liver-like softness. Benefiting from the short printing time, the highly individualized models can be fabricated immediately following enhanced CT examination. Leveraging the high-efficiency self-healing performance, these models support repetitive resection for optimal trace through a trial-and-error approach. At the preliminary explorative clinical trial (NCT06006338), a total of 5 participants are included for preoperative planning. The primary outcomes indicate that the negative surgery margins are achieved and the unforeseen injuries of vital vascular structures are avoided. The 3D printing of liver models can enhance the safety of hepatic surgery, demonstrating promising application value in clinical practice.

Indexed as

LiverPrinting, Three-DimensionalClinical Trials as TopicElastomersHumansModels, AnatomicPrecision MedicineElastomers

Identifiers

PMID38114507
PMCPMC10730511
OpenAlexW4389981764

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.