3D printing in the endovascular treatment of visceral artery aneurysms.

Visceral artery aneurysms (VAAs) are vascular pathologies that are difficult to treat. The variable geometry of the vessels and the location of aneurysms render difficult their evaluation in radiological imaging studies. Less invasive endovascular procedures are increasingly used in common practice. Our aim was to test the feasibility of using 3D printing technology in the preparation of preoperative spatial models of visceral artery aneurysms and their impact on interventional treatment. In our observational study, we examined a group of patients with true aneurysms of the visceral arteries who were followed and who underwent endovascular procedures with the use of 3D prints for better imaging of vascular lesions. We analyzed the fused filament fabrication method of 3D printing and printable materials in the preparation of spatial vascular models. We confirmed that more accurate visualization and analysis of vascular anatomy could assist operators in attempting minimally invasive treatment with good results. Extending imaging studies using 3D printing models that allow for the assessment of the position, morphology and geometry of the aneurysm sac, particularly of vessel branches, could encourage surgeons to perform endovascular procedures.

Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence.

PURPOSE: The key problem raised in the paper is the change in the position of the breast tumor due to magnetic resonance imaging examinations in the abdominal position relative to the supine position during the surgical procedure. Changing the position of the patient leads to significant deformation of the breast, which leads to the inability to indicate the location of the neoplastic lesion correctly. METHODS: This study outlines a methodological process for treating cancer patients. Pre-qualification assessments are conducted for magnetic resonance imaging (MRI), and 3D scans are taken in three positions: supine with arms raised, supine surgical position (SS), and standing. MRI and standard ultrasonography (USG) imaging are performed, and breast and cancer tissue are segmented from the MRI images. Finite element analysis is used to simulate tissue behavior in different positions, and an artificial neural network is trained to predict tumor dislocation. Based on the model, a 3D-printed breast with a highlighted tumor is manufactured. This computer-aided analysis is used to create a detailed surgical plan, and lumpectomy surgery is performed in the SS. In addition, the geometry of the tumor is presented to the medical staff as a 3D-printed element. RESULTS: By utilizing a comprehensive range of techniques, including pre-qualification assessment, 3D scanning, MRI and USG imaging, segmentation of breast and cancer tissue, model analysis, image fusion, finite element analysis, artificial neural network training, and additive manufacturing, a detailed surgical plan can be created for performing lumpectomy surgery in the supine surgical position. CONCLUSION: The new approach developed for the pre-operative assessment and surgical planning of breast cancer patients has demonstrated significant potential for improving the accuracy and efficacy of surgical procedures. This procedure may also help the pathomorphological justification. Moreover, transparent 3D-printed breast models can benefit breast cancer operation assistance. The physical and computational models can help surgeons visualize the breast and the tumor more accurately and detailedly, allowing them to plan the surgery with greater precision and accuracy.