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 () and Yuhua Zhang ()
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Yahui Lu: Zhejiang University
Xing Chen: Zhejiang Cancer Hospital
Fang Han: Zhejiang Cancer Hospital
Qian Zhao: Zhejiang University
Tao Xie: Zhejiang University
Jingjun Wu: Zhejiang University
Yuhua Zhang: Zhejiang Cancer Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-44324-6

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