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Comprehensive preoperative planning and real-time intraoperative guidance are essential prerequisites for achieving precise liver resection. In pursuit of this goal, the investigators have developed innovative 3D printed liver models utilizing a physically crosslinked self-healing elastomer created through the copolymerization of 4-acryloylmorpholine (ACMO) and methoxy poly (ethylene glycol) acrylate (mPEGA). These printed models exhibit exceptional healing capabilities, efficiently restoring their structure within minutes at room temperature, and rapidly recovering within moments after being incised.
Herein, the investigators aim to assess the viability of employing these 3D printed liver models as instrumental tools in designing the optimal surgical approach through an iterative trial-and-error methodology. Concurrently, the investigators aim to determine whether the integration of these 3D printed models into conventional methods (contrast-enhanced CT or MRI) can enhance the safety, ease, and efficiency of hepatic resection procedures.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experimental arm | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 3D Printed Model for preoperative planning | Other | Besides conventional preoperative planning based on contrast-enhanced CT/MRI, the investigator refers to a 3D printed model to determine the optimal surgical approach via a trial-and-error method. Specifically, a personalized 3D printed model is fabricated. By referring to the printed model and CT/MRI images, a preliminary surgical trace is determined. Then, a simulation surgery is performed on the model, and surgical margin and the potential injury of vital vascular structures are evaluated. This assessment leads to the adjustment of the surgical path as necessary. The refined surgical route is then validated through a second simulation surgery performed on the healed 3D model. This iterative process is repeated multiple times, allowing for a comprehensive exploration of various approaches until the optimal surgical strategy crystallizes. Next, the investigator proceeds to execute the real surgery on the participant, meticulously adhering to the determined optimal surgical approach. |
| Measure | Description | Time Frame |
|---|---|---|
| R0 resection rate | The proportion of patients who achieved pathological negative surgical margin | 10 days |
| Measure | Description | Time Frame |
|---|---|---|
| Injury of vital vascular structures | The Injury of vital vascular structures, such as portal veins or hepatic veins. | Immediately after the surgery. |
| Operation time | Time length (minute) between the beginning and the end of the surgery. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chaoqun Fei | Contact | +086-0571-88122564 | ec@zjcc.org.cn |
| Name | Affiliation | Role |
|---|---|---|
| Yuhua Zhang | Zhejiang Cancer Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| 1# Banshan East Rd. Zhejiang cancer hospital | Recruiting | Hangzhou | Zhejiang | 310022 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38114507 | Derived | Lu Y, Chen X, Han F, Zhao Q, Xie T, Wu J, Zhang Y. 3D printing of self-healing personalized liver models for surgical training and preoperative planning. Nat Commun. 2023 Dec 19;14(1):8447. doi: 10.1038/s41467-023-44324-6. |
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| ID | Term |
|---|---|
| D008113 | Liver Neoplasms |
| ID | Term |
|---|---|
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
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|
| Immediately after the surgery. |
| Blood loss | The amount of lost blood (ml) in the operation. | Immediately after the surgery. |
| D008107 |
| Liver Diseases |