Preoperative Planning

Several centers in Belgium use both transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) for the preoperative diagnosis of deep infiltrating endometriosis (DIE), while other centers rely almost exclusively on TVS. From the perspective of both the patient and the endometriosis care team, it is not primarily important that every individual lesion is mapped perfectly preoperatively, but rather that all lesions impacting clinical management and surgical planning are accurately detected. This is particularly crucial when a multidisciplinary approach is required, involving a urologist for bladder lesions and/or an abdominal surgeon for invasive rectosigmoid lesions. Moreover, providing the patient with thorough preoperative counseling is essential, and this is, of course, determined by the preoperative findings and the type of planned surgical procedure. In this study, we first assess the diagnostic performance of TVS in the preoperative diagnosis of DIE. As a secondary objective, we evaluate the added value of MRI compared to TVS for preoperative surgical planning in patients who also underwent an MRI examination.

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.