Case Report Early Identification of Traumatic Durotomy Associated with Atlantooccipital Dislocation May Prevent Retropharyngeal Pseudomeningocele Development

Atlantooccipital dislocation can be complicated by a traumatic durotomy that may lead to the rare development of a retropharyngeal pseudomeningocele. To our knowledge this has been reported only five times previously. We present the case of a 60-year-old man involved in a motor vehicle accident who suffered an atlantooccipital dislocation and C5-C6 three-column injury. A unique MRI image of a defect in the ventral dura posterior to C2 was appreciated. He underwent occiput to T2 internal fixation and arthrodesis. During surgery, CSF egress was seen caudal to the right C2 nerve root. A DuraMatrix onlay patch reinforced with DuraSeal was placed to stop the CSF leak. A lumbar subarachnoid drain was also placed. The patient made a satisfactory recovery with residual mild weakness of his right upper extremity. In this report, we demonstrate that careful MRI review can reveal a ventral durotomy in a traumatic atlantooccipital dislocation and, if discovered, effective treatment including a lumbar subarachnoid drain for CSF diversion may prevent progression to a retropharyngeal pseudomeningocele. The literature on this rare presentation and associated durotomy is provided

Early Identification of Traumatic Durotomy Associated with Atlantooccipital Dislocation May Prevent Retropharyngeal Pseudomeningocele Development

Atlantooccipital dislocation can be complicated by a traumatic durotomy that may lead to the rare development of a retropharyngeal pseudomeningocele. To our knowledge this has been reported only five times previously. We present the case of a 60-year-old man involved in a motor vehicle accident who suffered an atlantooccipital dislocation and C5-C6 three-column injury. A unique MRI image of a defect in the ventral dura posterior to C2 was appreciated. He underwent occiput to T2 internal fixation and arthrodesis. During surgery, CSF egress was seen caudal to the right C2 nerve root. A DuraMatrix onlay patch reinforced with DuraSeal was placed to stop the CSF leak. A lumbar subarachnoid drain was also placed. The patient made a satisfactory recovery with residual mild weakness of his right upper extremity. In this report, we demonstrate that careful MRI review can reveal a ventral durotomy in a traumatic atlantooccipital dislocation and, if discovered, effective treatment including a lumbar subarachnoid drain for CSF diversion may prevent progression to a retropharyngeal pseudomeningocele. The literature on this rare presentation and associated durotomy is provided

Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum

Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF. Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then applied] to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). TWS machine learning closely correlates with the gold-standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications