Acute and chronic changes in porcine rete mirabile after embolization with the Menox system: angiographic and histopathological analyses.

Objective: To evaluate acute and chronic changes seen on angiographic and histopathological studies of porcine rete mirabile, comparing those treated with the Menox liquid embolic system (LES) and those treated with the Onyx LES. Materials and Methods: Five pigs, each weighing approximately 35 kg, were submitted to rete mirabile embolization under general anesthesia and fluoroscopic guidance, with the Menox LES or Onyx LES. Four animals were treated with the Menox LES and underwent cerebral angiography, followed by euthanasia, at 1, 30, 60, and 90 days after embolization. One animal was treated with the Onyx LES underwent the same procedures at 30 days after embolization. In a subsequent histopathological analysis, we compared the Menox LES and Onyx LES in terms of the acute and chronic changes observed. Results: We observed no significant changes in blood pressure, heart rate, or electrocardiographic parameters that could be attributed to the super-selective infusion of dimethyl sulfoxide or the Menox embolic agent. Fluoroscopy showed adequate material opacity, appropriate progression to the center of the rete mirabile and complete unilateral embolization. Microcatheters were uneventfully detached from the embolized nidus. We observed mild to moderate intravascular and extravascular inflammatory responses, without histological evidence of necrotizing arteritis. There were no adverse neurovascular events. Conclusion: The Menox LES appears to be safe and effective, as well as being apparently equivalent to the Onyx LES in terms of the postprocedure angiographic and histopathological findings.

Objetivo: Avaliar as alterações angiográficas e histopatológicas agudas e crônicas em rete mirabile suína tratadas com o Menox liquid embolic system (LES) e comparar essas alterações com a embolização com Onyx LES. Materiais e Métodos: A embolização da rete mirabile com Menox LES e Onyx LES foi realizada em cinco suínos pesando cerca de 35 kg sob anestesia geral e orientação fluoroscópica. Quatro animais tratados com Menox LES foram submetidos a angiografia cerebral seguida de eutanásia após 1, 30, 60 e 90 dias e um animal tratado com Onix LES foi submetido ao mesmo procedimento após 30 dias. A análise histopatológica subsequente para alterações agudas e crônicas avaliou o desempenho do Menox LES comparado ao Onyx LES. Resultados: Não foram observadas alterações significativas atribuíveis à infusão superseletiva de dimetilsulfóxido ou Menox nos parâmetros de pressão arterial, frequência cardíaca ou eletrocardiograma. A fluoroscopia mostrou opacidade adequada do material, progressão adequada para o centro da rete mirabile e embolização unilateral completa. Os microcateteres foram retirados do nidus embolizado sem complicações. Observou-se resposta inflamatória intravascular e extravascular leve a moderada, sem indício histológico de arterite necrosante. Nenhum dos casos apresentou eventos neurovasculares adversos. Conclusão: A injeção de Menox LES mostrou-se segura e eficaz, além de ser equivalente ao Onyx LES em relação aos achados angiográficos e histopatológicos pós-procedimento.

Contrast phase recognition in liver computer tomography using deep learning.

Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of São Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively.