Volumetric evaluation of lumbar epidural fat distribution in epidural lipomatosis and back pain in patients who are obese: introducing a novel technique (Fat Finder algorithm).

Objective: Spinal epidural lipomatosis (EL) represents an excessive deposition of unencapsulated adipose tissue in the spinal canal that can result in chronic back pain in patients who are obese with and without diabetes. We aim to calculate the total volumetric epidural fat on lumbar spine MRI in a predominately obese population and correlate total epidural fat to lower back pain (LBP) and body mass index (BMI). Research design and methods: We developed a program (Fat Finder) to quantify volumetric distribution of epidural fat throughout the lumbar spine. Eleven patients with LBP were imaged using two MRI protocols: parallel axial slices and conventional clinical protocol. The distribution of epidural fat per level was analyzed and normalized to the spinal canal size. Results: Our sample had an average age of 59.9 years and BMI of 31.57 kg/m2. EL subgroup consisted of seven patients. The L2-L5 total fat volume was 3477.6 mm3 (1431.1-5595.9) in the EL group versus 1783.8 mm3 (815.0-2717.5) in the age-similar non-EL group. A higher percentage of fat volume in the canal was associated with higher LBP scores. The fat percentage was 32.2% among patients with EL versus 15.4% for age-similar non-EL with LBP score of 6.1 and 4.0, respectively. Conclusions: The Fat Finder is a novel volumetric method to quantify epidural lumbar spinal fat. The epidural fat favors the lower spinal segment with direct proportionality between the fat volume and LBP score, independent of BMI.

Role of α2ß1 integrins in mediating cell shape on microtextured titanium surfaces.

Surface microroughness plays an important role in determining osteoblast behavior on titanium. Previous studies have shown that osteoblast differentiation on microtextured titanium substrates is dependent on alpha-2 beta-1 (α2ß1) integrin signaling. This study used focused ion beam milling and scanning electron microscopy, combined with three-dimensional image reconstruction, to investigate early interactions of individual cells with their substrate and the role of integrin α2ß1 in determining cell shape. MG63 osteoblast-like cells on sand blasted/acid etched (SLA) Ti surfaces after 3 days of culturing indicated decreased cell number, increased cell differentiation, and increased expression of mRNA levels for α1, α2, αV, and ß1 integrin subunits compared to cells on smooth Ti (PT) surfaces. α2 or ß1 silenced cells exhibited increased cell number and decreased differentiation on SLA compared to wild-type cells. Wild-type cells on SLA possessed an elongated morphology with reduced cell area, increased cell thickness, and more apparent contact points. Cells on PT exhibited greater spreading and were relatively flat. Silenced cells possessed a morphology and phenotype similar to wild-type cells grown on PT. These observations indicate that surface microroughness affects cell response via α2ß1 integrin signaling, resulting in a cell shape that promotes osteoblastic differentiation.

Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma.

Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological images. To address this challenge, we develop a clinical decision support system for automated Fuhrman grading of renal carcinoma biopsy images. We extract 1316 color, shape, texture and topology features and develop one vs. all models for four Fuhrman grades. Our models are highly accurate with 90.4% accuracy in a four-class prediction. Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation. We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists' grading criteria. Our study identifies novel imaging features that are semantically linked to Fuhrman grading criteria.