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SH-LPS System in Preoperative Planning for Liver Resection

NCT06911086 · Status: NOT_YET_RECRUITING · Phase: PHASE2/PHASE3 · Type: INTERVENTIONAL · Enrollment: 100

Last updated 2025-04-04

No results posted yet for this study

Summary

Effective preoperative planning and real-time intraoperative guidance are crucial for performing accurate liver resections. To address this need, the researchers have designed advanced 3D-printed liver models using a self-healing elastomer, created through the copolymerization of 4-acryloylmorpholine (ACMO) and methoxy poly(ethylene glycol) acrylate (mPEGA). These models demonstrate outstanding healing properties, swiftly restoring their structure within minutes at room temperature, and quickly recovering after incisions.

In previous studies, Professor Yuhua Zhang, the project applicant, collaborated with a team from Zhejiang University to develop a 3D-printed liver model that is self-healing and reusable for repeated cutting. They preliminarily explored the feasibility of applying this model for preoperative planning and surgical training for liver surgeries. The results were published in Nature Communications (Lu et al., Nat Commun. Dec 19;14(1):8447). Building on this, the applicant intends to establish a personalized liver surgery planning system (Personalized Liver Surgery Planning System Based on High-Fidelity 3D Printed Self-Healing Liver Models, SH-LPS), which will assess, through a randomized controlled trial, the value of SH-LPS in improving liver surgery efficiency and safety.

Conditions

  • Liver Tumor; Surgery
  • 3D Printing
  • Preoperative Planning

Interventions

DEVICE

3D printed models

A three-dimensional digital model is constructed based on the patient's preoperative CT/MRI images, and a personalized physical model is created using 3D printing. This model has the ability to self-heal after being cut. Surgeons can perform multiple simulated surgeries on the model to plan the optimal surgical path before the authetic surgery

OTHER

CT or MRI image

Obtain the patient's CT/MRI images and determine the definitive surgical path based on the two-dimensional images.

Sponsors & Collaborators

  • Zhejiang Cancer Hospital

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-04-15
Primary Completion
2026-01-01
Completion
2026-06-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06911086 on ClinicalTrials.gov