Modelling and manufacturing of 3D-printed, patient-specific, and anthropomorphic gastric phantoms: a pilot study

Use of individualized 3D-printed models of pancreatic cancer to improve surgeons' anatomic understanding and surgical planning

OBJECTIVES

Three-dimensional (3D) printing has been increasingly used to create accurate patient-specific 3D-printed models from medical imaging data. We aimed to evaluate the utility of 3D-printed models in the localization and understanding of pancreatic cancer for surgeons before pancreatic surgery.

METHODS

Between March and September 2021, we prospectively enrolled 10 patients with suspected pancreatic cancer who were scheduled for surgery. We created an individualized 3D-printed model from preoperative CT images. Six surgeons (three staff and three residents) evaluated the CT images before and after the presentation of the 3D-printed model using a 7-item questionnaire (understanding of anatomy and pancreatic cancer [Q1-4], preoperative planning [Q5], and education for trainees or patients [Q6-7]) on a 5-point scale. Survey scores on Q1-5 before and after the presentation of the 3D-printed model were compared. Q6-7 assessed the 3D-printed model's effects on education compared to CT. Subgroup analysis was performed between staff and residents.

RESULTS

After the 3D-printed model presentation, survey scores improved in all five questions (before 3.90 vs. after 4.56, p < 0.001), with a mean improvement of 0.57‒0.93. Staff and resident scores improved after a 3D-printed model presentation (p < 0.05), except for Q4 in the resident group. The mean difference was higher among the staff than among the residents (staff: 0.50‒0.97 vs. residents: 0.27‒0.90). The scores of the 3D-printed model for education were high (trainees: 4.47 vs. patients: 4.60) compared to CT.

CONCLUSION

The 3D-printed model of pancreatic cancer improved surgeons' understanding of individual patients' pancreatic cancer and surgical planning.

CLINICAL RELEVANCE STATEMENT

The 3D-printed model of pancreatic cancer can be created using a preoperative CT image, which not only assists surgeons in surgical planning but also serves as a valuable educational resource for patients and students.

KEY POINTS

• A personalized 3D-printed pancreatic cancer model provides more intuitive information than CT, allowing surgeons to better visualize the tumor's location and relationship to neighboring organs. • In particular, the survey score was higher among staff who performed the surgery than among residents. • Individual patient pancreatic cancer models have the potential to be used for personalized patient education as well as resident education.

Patient-specific stomach biomechanics before and after laparoscopic sleeve gastrectomy

BACKGROUND

Obesity has become a global epidemic. Bariatric surgery is considered the most effective therapeutic weapon in terms of weight loss and improvement of quality of life and comorbidities. Laparoscopic sleeve gastrectomy (LSG) is one of the most performed procedures worldwide, although patients carry a nonnegligible risk of developing post-operative GERD and BE.

OBJECTIVES

The aim of this work is the development of computational patient-specific models to analyze the changes induced by bariatric surgery, i.e., the volumetric gastric reduction, the mechanical response of the stomach during an inflation process, and the related elongation strain (ES) distribution at different intragastric pressures.

METHODS

Patient-specific pre- and post-surgical models were extracted from Magnetic Resonance Imaging (MRI) scans of patients with morbid obesity submitted to LSG. Twenty-three patients were analyzed, resulting in forty-six 3D-geometries and related computational analyses.

RESULTS

A significant difference between the mechanical behavior of pre- and post-surgical stomach subjected to the same internal gastric pressure was observed, that can be correlated to a change in the global stomach stiffness and a minor gastric wall tension, resulting in unusual activations of mechanoreceptors following food intake and satiety variation after LSG.

CONCLUSIONS

Computational patient-specific models may contribute to improve the current knowledge about anatomical and physiological changes induced by LSG, aiming at reducing post-operative complications and improving quality of life in the long run.

Design and Mechanical Characterization Using Digital Image Correlation of Soft Tissue-Mimicking Polymers

Present and future anatomical models for biomedical applications will need bio-mimicking three-dimensional (3D)-printed tissues. These would enable, for example, the evaluation of the quality-performance of novel devices at an intermediate step between ex-vivo and in-vivo trials. Nowadays, PolyJet technology produces anatomical models with varying levels of realism and fidelity to replicate organic tissues. These include anatomical presets set with combinations of multiple materials, transitions, and colors that vary in hardness, flexibility, and density. This study aims to mechanically characterize multi-material specimens designed and fabricated to mimic various bio-inspired hierarchical structures targeted to mimic tendons and ligaments. A Stratasys® J750™ 3D Printer was used, combining the Agilus30™ material at different hardness levels in the bio-mimicking configurations. Then, the mechanical properties of these different options were tested to evaluate their behavior under uni-axial tensile tests. Digital Image Correlation (DIC) was used to accurately quantify the specimens' large strains in a non-contact fashion. A difference in the mechanical properties according to pattern type, proposed hardness combinations, and matrix-to-fiber ratio were evidenced. The specimens V, J1, A1, and C were selected as the best for every type of pattern. Specimens V were chosen as the leading combination since they exhibited the best balance of mechanical properties with the higher values of Modulus of elasticity (2.21 ± 0.17 MPa), maximum strain (1.86 ± 0.05 mm/mm), and tensile strength at break (2.11 ± 0.13 MPa). The approach demonstrates the versatility of PolyJet technology that enables core materials to be tailored based on specific needs. These findings will allow the development of more accurate and realistic computational and 3D printed soft tissue anatomical solutions mimicking something much closer to real tissues.

Development of an automatic modeling method for patient-specific aortic graft reconstruction guide in thoracoabdominal aortic repair

BACKGROUND AND OBJECTIVES

Because repairing visceral and segmental arteries in open surgical repair for thoracoabdominal aortic aneurysms is essential, two types of patient-specific graft reconstruction guides for reconstruction in the operating room have been developed that are applied clinically. However, designing the patient-specific guides is a time-consuming, laborious task. The aim of this study was to develop an automatic modeling method and to evaluate its accuracy.

METHODS

In 10 patients with thoracoabdominal aortic aneurysms, computer-aided designing was performed with conventional and automatic modeling methods for aortic reconstruction guides as follows: 1) a visualizing guide that presented the accurate shape of the aortic graft, visualizing the main aortic body and major blood vessels; and 2) a marking guide wherein the vessels in the visualizing guide were replaced by the protruding marking regions detectable by tactile sense. The script-based automatic guide modeling program was developed using an application programming interface presented in the 3-matic software with Python. For accuracy, the absolute mean differences of both modeling methods were assessed using Hausdorff distance. The modeling between conventional and automatic modeling methods was compared and evaluated using the Wilcoxon signed-rank test.

RESULTS

The absolute mean difference between the conventional and automatic modeling methods were 6.05 ± 4.86 µm for the visualizing guide and 5.51 ± 4.85 µm for the marking guide. For the visualizing guide, the modeling time of the automatic modeling method was reduced by approximately more than thirtyfold than the conventional modeling method (p<0.001). The marking guide was reduced about fortyfold (p<0.001).

CONCLUSIONS

Compared to the conventional method, the automatic modeling method was demonstrated to reduce the modeling time with reasonable accuracy, which could lead to a more efficient modeling and clinical application.