Features of lumbar spine texture extracted from routine MRI correlate with bone mineral density and can potentially differentiate patients with and without fragility fractures in the spine

The use of routine magnetic resonance imaging (MRI) to potentially assess skeletal fragility has been widely studied in osteoporosis. The aim of this study was to evaluate bone texture attributes (TA) from routine lumbar spine (LS) MRI and their correlation with vertebral fragility fractures (VFF) and bone mineral density (BMD). Sixty-four post-menopausal women were submitted to LS densitometry, total spine radiographs, and routine T2-weighted LS MRI. Twenty-two TA were extracted with the platform IBEX from L3 vertebra. The statistical difference was evaluated using ANOVA and Duncan's post-test. Correlation analyses were performed using Spearman's coefficient. Statistical significance was considered when P<0.05. The results did not show a significant difference in BMD between the women with and without fractures. Two bone TA (cluster tendency and variance) were significantly lower in the fracture group. Cluster tendency with VFF in osteopenia was 1.54±1.37 and in osteoporosis was 1.11±58. Cluster tendency without VFF in osteopenia was 2.23±1.38 and in osteoporosis was 1.88±1.14). Variance with VFF in osteopenia was 1.44±1.37 and in osteoporosis was 1.13±59. Variance without VFF in osteopenia was 2.34±1.38 and in osteoporosis was 1.89±1.14. There was a significant correlation between BMD and cluster prominence (r=0.409), cluster tendency (r=0.345), correlation (r=0.570), entropy (r=0.364), information measure corr1 (r=0.378), inverse variance (r=0.449), sum entropy (r=0.320), variance (r=0.338), sum average (r=-0.274), and sum variance (r=-0.266). Our results demonstrated the potential use of TA extracted from routine MRI as a biomarker to assess osteoporosis and identify the tendency of skeletal fragility vertebral fractures.

Manual and semiautomatic segmentation of bone sarcomas on MRI have high similarity

The aims of this study were to evaluate the intra- and interobserver reproducibility of manual segmentation of bone sarcomas in magnetic resonance imaging (MRI) studies and to compare manual and semiautomatic segmentation methods. This retrospective study included twelve osteosarcoma and eight Ewing sarcoma MRI studies performed prior to any therapeutic intervention. All cases were histopathologically confirmed. Three radiologists used 3D-Slicer software to perform manual segmentation of bone sarcomas in a blinded and independent manner. One radiologist segmented manually and also performed semiautomatic segmentation with the GrowCut tool. Segmentation exercises were timed for comparison. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to evaluate similarity between the segmentation results and further statistical analyses were performed to compare DSC, HD, and volumetric results. Manual segmentation was reproducible with intraobserver DSC varying from 0.83 to 0.97 and HD from 3.37 to 28.73 mm. Interobserver DSC of manual segmentation showed variation from 0.73 to 0.97 and HD from 3.93 to 33.40 mm. Semiautomatic segmentation compared to manual segmentation resulted in DSCs of 0.71−0.96 and HDs of 5.38−31.54 mm. Semiautomatic segmentation required significantly less time compared to manual segmentation (P value ≤0.05). Among all situations compared, tumor volumetry did not show significant statistical differences (P value >0.05). We found excellent intra- and interobserver agreement for manual segmentation of osteosarcoma and Ewing sarcoma. There was high similarity between manual and semiautomatic segmentation, with a significant reduction of segmentation time using the semiautomatic method.

Classificação semi-automática da degeneração de discos intervertebrais em imagens imagens de ressonância magnética / Semi-automatic classification of disc degeneration intervertebraises in magnetic resonance images

Este trabalho descreve o desenvolvimento de um método que classifica de forma semi-automática a degeneração de discos intervertebrais lombares em imagens de ressonância magnética ponderadas em T2. O conjunto de imagens consiste de 210 discos extraídos de exames de 94 indivíduos (20 a 80 anos). A classificação é feita por uma rede neural do tipo perceptron multicamada com 6 entradas, 15 neurônios na camada intermediária e 1 saída. Os resultados obtidos mostraram uma taxa média de acerto de 81,42%, com erro padrão de 9,11%.

This article describes the development of a method that classifies semi-automatic degeneration of lumbar intervertebral discs in magnetic resonance T2-weighted images. The dataset consists of images of 210 discs obtained from94 individuals (20 to 80 year old). An artificial neural network of the multilayer perceptron with 6 inputs, 15 neuronsin the hidden layer and 1 output, was used to check the efficiency of this study. Obtained an average rate of sucess of81.42%, with a standard error of 9.11%.