The Relationship Between Wild-Type Transthyretin Amyloid Load and Ligamentum Flavum Thickness in Lumbar Stenosis Patients.

BACKGROUND: One key contributor to lumbar stenosis is thickening of the ligamentum flavum (LF), a process still poorly understood. Wild-type transthyretin amyloid (ATTRwt) has been found in the LF of patients undergoing decompression surgery, suggesting that amyloid may play a role. However, it is unclear whether within patients harboring ATTRwt, the amount of amyloid is associated with LF thickness. METHODS: From an initial cohort of 324 consecutive lumbar stenosis patients whose LF specimens from decompression surgery were sent for analysis (2018-2019), 33 patients met the following criteria: 1) Congo red-positive amyloid in the LF, 2) ATTRwt by mass spectrometry-based proteomics, and 3) an available preoperative magnetic resonance imaging. Histological specimens were digitized, and amyloid load was quantified through Trainable Weka Segmentation machine learning. LF thicknesses were manually measured on axial T2-weighted preoperative magnetic resonance imaging scans at each lumbar level, L1-S1. The sum of thicknesses at every lumbar LF level (L1-S1) equals "lumbar LF burden". RESULTS: Patients had a mean age of 72.7 years (range = 59-87), were mostly male (61%) and white (82%), and predominantly had surgery at L4-L5 levels (73%). Amyloid load was positively correlated with LF thickness (R = 0.345, P = 0.0492) at the levels of surgical decompression. Furthermore, amyloid load was positively correlated with lumbar LF burden (R = 0.383, P = 0.0279). CONCLUSIONS: Amyloid load is positively correlated with LF thickness and lumbar LF burden across all lumbar levels, in a dose-dependent manner. Further studies are needed to validate these findings, uncover the underlying pathophysiology, and pave the way toward using therapies that slow LF thickening.

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.

Tumoral Calcinosis of the Cervical Spine Associated with a Pathologic Odontoid Fracture.

Tumoral calcinosis involves focal calcium deposits in the soft tissues surrounding a joint and most commonly occurs in the hips and elbows, rarely in the cervical spine. Furthermore, it has not been known to be associated with pathologic fractures. To the best of our knowledge, our case report highlights the first case of a pathologic type II odontoid fracture associated with adjacent tumoral calcinosis, resulting in pain, dysphagia, and severe spinal stenosis. The patient underwent a posterior occipitocervical fusion and C1 laminectomy, along with planned tracheostomy and gastrostomy to avoid expected difficulty with postoperative extubation and dysphagia. Additionally, we present a review of existing literature on tumoral calcinosis in the upper cervical spine.

The life and legacy of William Beecher Scoville.

Dr. William Beecher Scoville (1906-1984) is a giant figure in the history of neurosurgery, well known by the public for his operation on Patient H.M. He developed dozens of neurosurgical instruments and techniques, with many tools named after him that are still widely used today. He founded numerous neurosurgical societies around the world. He led the movement in psychosurgery, developing the technique of selective orbital undercutting and performing hundreds of lobotomies throughout his career. However, his many contributions to the advancement of neurosurgery have not been well described in the medical literature. To bridge the knowledge gap, this article seeks to detail the life and career of William Beecher Scoville and bring to attention the enduring impact of his work.