Background Prostatic artery embolization (PAE) is associated with patients’ quality of life improvements and limited side effects compared to surgery. However, this procedure remains technically challenging due to complex vasculature, anatomical variations and small arteries, inducing long procedure times and high radiation exposure levels both to patients and medical staff. Moreover, the risk of non-target embolization can lead to relevant complications. In this context, advanced imaging can constitute a solid ally to address these challenges and deliver good clinical outcomes at acceptable radiation levels. Main text This technical note aims to share the consolidated experience of four institutions detailing their optimized workflow using advanced image guidance, discussing variants, and sharing their best practices to reach a consensus standardized imaging workflow for PAE procedure, as well as pre and post-operative imaging. Conclusions This technical note puts forth a consensus optimized imaging workflow and best practices, with the hope of helping drive adoption of the procedure, deliver good clinical outcomes, and minimize radiation dose levels and contrast media injections while making PAE procedures shorter and safer.

Purpose: We report a new approach to perform endovascular treatment of thoracoabdominal aneurysms under electromagnetic navigation guidance using a modified system (IOPS; Centerline Biomedical, Inc., Cleveland, OH, USA) and a modified branched endograft (E-nside TAAA Multibranch Stent Graft System; Artivion Inc., Kennesaw, GA, USA). Case Report: We performed this case in an aortic in vitro model made from transparent polyurethane in our research hybrid room (Discovery IGS 730; GE HealthCare, Chicago, IL, USA). While the implantation of this device typically involves several challenging steps, including precise endograft implantation, snaring of preloaded guide wires, and cannulation of target visceral arteries, all were successfully performed using electromagnetic navigation guidance. Conclusion: Our preliminary experience suggests that endograft implantation under electromagnetic navigation guidance in an integrated hybrid operating room is an innovative option to address technical challenges and reduce patient and operator radiation exposure associated with complex endovascular surgery. Clinical Impact Most steps of a branched endografting procedure can be performed without X-Ray exposure when using electromagnetic navigation guidance and a modified branched endograft.

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However, the procedure is invasive and carries the risk of infection and bleeding. In this study, we used deep learning to differentiate benign from malignant focal solid liver lesions based on their appearance on routine abdominal ultrasound. Materials: A ResNet50-based neural network was fine-tuned using pre-trained weights on ImageNet. Patients who had abdominal ultrasound from 2014-2018 with liver code C2-C5 on Code Abdomen, an institutional lesion categorization system for abdominal organs on cross-sectional imaging modeled after BI-RADS, were included (Table 1). Non-cystic liver lesions with definite diagnosis by histopathology or MRI were included. Our dataset was split into training and validation sets by patient and augmented in real-time with zoom, skew, and flip transformations during training. Subgroup analysis of category 3 and 4 lesions (uncertain diagnosis set) was performed. Accuracy of the final model was compared with expert interpretation. Results: Among the 410 patients who met the inclusion criteria, there were 521 images of individual lesions, of which 170 were malignant and 351 were benign. Our training set had 419 lesions and our validation set had 102 lesions. Our model achieved a validation accuracy of 83.3% compared to expert accuracy of 77.5% and 60.8%, respectively. The model had an ROC AUC of 0.83, sensitivity of 0.82, and specificity of 0.84. For the uncertain diagnosis set, our model achieved a validation accuracy of 85.1% compared to expert accuracy of 74.5% and 61.7%, respectively. The model had an ROC AUC of 0.78, sensitivity of 0.91, and specificity of 0.67. Conclusions: Deep learning distinguished benign from malignant ultrasound-captured solid liver lesions with a high accuracy compared to expert radiologists and can better triage patients for biopsy, particularly those at high risk or with multiple lesions.

Prostatic artery embolization (PAE) is associated with patients’ quality of life improvements and limited side effects compared to surgery. However, this procedure remains technically challenging due to complex vasculature, anatomical variations and small arteries, inducing long procedure times and high radiation exposure levels both to patients and medical staff. Moreover, the risk of non-target embolization can lead to relevant complications. In this context, advanced imaging can constitute a solid ally to address these challenges and deliver good clinical outcomes at acceptable radiation levels. This technical note aims to share the consolidated experience of four institutions detailing their optimized workflow using advanced image guidance, discussing variants, and sharing their best practices.