Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans.

OBJECTIVES To investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models. METHODS This single-center retrospective study included patients who underwent two thoraco-abdominal CT scans during clinical routine with an average inter-scan interval of 21.7 ± 13.1 months (range 5-52 months). Automatic spine segmentation and vBMD extraction was performed by a convolutional neural network framework (anduin.bonescreen.de). Mean vBMD was calculated for levels T5-8, T9-12, and L1-5. VFs were identified by an expert in spine imaging. Odds ratios (ORs) for prevalent and incident VFs were calculated for vBMD (per standard deviation decrease) at each level, for baseline VF prevalence (yes/no), and for baseline VF count (n) using logistic regression models, adjusted for age and sex. Models were compared using Akaike's and Bayesian information criteria (AIC & BIC). RESULTS 420 patients (mean age, 63 years ± 9, 276 males) were included in this study. 40 (25 female) had prevalent and 24 (13 female) had incident VFs. Individuals with lower vBMD at any spine level had higher odds for VFs (L1-5, prevalent VF: OR,95%-CI,p: 2.2, 1.4-3.5,p=0.001; incident VF: 3.5, 1.8-6.9,p<0.001). In contrast, VF status (2.15, 0.72-6.43,p=0.170) and count (1.38, 0.89-2.12,p=0.147) performed worse in incident VF prediction. Information criteria revealed best fit for vBMD-based models (AIC vBMD=165.2; VF status=181.0; count=180.7). CONCLUSIONS VF prediction based on automatically extracted vBMD from routine clinical MDCT outperforms prediction models based on VF status and count. These findings underline the importance of opportunistic quantitative osteoporosis screening in clinical routine MDCT data

Sex differences and age-related changes in vertebral body volume and volumetric bone mineral density at the thoracolumbar spine using opportunistic QCT

ObjectivesTo quantitatively investigate the age- and sex-related longitudinal changes in trabecular volumetric bone mineral density (vBMD) and vertebral body volume at the thoracolumbar spine in adults.MethodsWe retrospectively included 168 adults (mean age 58.7 ± 9.8 years, 51 women) who received ≥7 MDCT scans over a period of ≥6.5 years (mean follow-up 9.0 ± 2.1 years) for clinical reasons. Level-wise vBMD and vertebral body volume were extracted from 22720 thoracolumbar vertebrae using a convolutional neural network (CNN)-based framework with asynchronous calibration and correction of the contrast media phase. Human readers conducted semiquantitative assessment of fracture status and bony degenerations.ResultsIn the 40-60 years age group, women had a significantly higher trabecular vBMD than men at all thoracolumbar levels (p&lt;0.05 to p&lt;0.001). Conversely, men, on average, had larger vertebrae with lower vBMD. This sex difference in vBMD did not persist in the 60-80 years age group. While the lumbar (T12-L5) vBMD slopes in women only showed a non-significant trend of accelerated decline with age, vertebrae T1-11 displayed a distinct pattern, with women demonstrating a significantly accelerated decline compared to men (p&lt;0.01 to p&lt;0.0001). Between baseline and last follow-up examinations, the vertebral body volume slightly increased in women (T1-12: 1.1 ± 1.0 cm3; L1-5: 1.0 ± 1.4 cm3) and men (T1-12: 1.2 ± 1.3 cm3; L1-5: 1.5 ± 1.6 cm3). After excluding vertebrae with bony degenerations, the residual increase was only small in women (T1-12: 0.6 ± 0.6 cm3; L1-5: 0.7 ± 0.7 cm3) and men (T1-12: 0.7 ± 0.6 cm3; L1-5: 1.2 ± 0.8 cm3). In non-degenerated vertebrae, the mean change in volume was &lt;5% of the respective vertebral body volumes.ConclusionSex differences in thoracolumbar vBMD were apparent before menopause, and disappeared after menopause, likely attributable to an accelerated and more profound vBMD decline in women at the thoracic spine. In patients without advanced spine degeneration, the overall volumetric changes in the vertebral body appeared subtle

Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans

ObjectivesTo investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models.MethodsThis single-center retrospective study included patients who underwent two thoraco-abdominal CT scans during clinical routine with an average inter-scan interval of 21.7 ± 13.1 months (range 5–52 months). Automatic spine segmentation and vBMD extraction was performed by a convolutional neural network framework (anduin.bonescreen.de). Mean vBMD was calculated for levels T5-8, T9-12, and L1-5. VFs were identified by an expert in spine imaging. Odds ratios (ORs) for prevalent and incident VFs were calculated for vBMD (per standard deviation decrease) at each level, for baseline VF prevalence (yes/no), and for baseline VF count (n) using logistic regression models, adjusted for age and sex. Models were compared using Akaike’s and Bayesian information criteria (AIC &amp; BIC).Results420 patients (mean age, 63 years ± 9, 276 males) were included in this study. 40 (25 female) had prevalent and 24 (13 female) had incident VFs. Individuals with lower vBMD at any spine level had higher odds for VFs (L1-5, prevalent VF: OR,95%-CI,p: 2.2, 1.4–3.5,p=0.001; incident VF: 3.5, 1.8–6.9,p&lt;0.001). In contrast, VF status (2.15, 0.72–6.43,p=0.170) and count (1.38, 0.89–2.12,p=0.147) performed worse in incident VF prediction. Information criteria revealed best fit for vBMD-based models (AIC vBMD=165.2; VF status=181.0; count=180.7).ConclusionsVF prediction based on automatically extracted vBMD from routine clinical MDCT outperforms prediction models based on VF status and count. These findings underline the importance of opportunistic quantitative osteoporosis screening in clinical routine MDCT data

Multibody Models of the Thoracolumbar Spine: A Review on Applications, Limitations, and Challenges

Numerical models of the musculoskeletal system as investigative tools are an integral part of biomechanical and clinical research. While finite element modeling is primarily suitable for the examination of deformation states and internal stresses in flexible bodies, multibody modeling is based on the assumption of rigid bodies, that are connected via joints and flexible elements. This simplification allows the consideration of biomechanical systems from a holistic perspective and thus takes into account multiple influencing factors of mechanical loads. Being the source of major health issues worldwide, the human spine is subject to a variety of studies using these models to investigate and understand healthy and pathological biomechanics of the upper body. In this review, we summarize the current state-of-the-art literature on multibody models of the thoracolumbar spine and identify limitations and challenges related to current modeling approaches

Obese and overweight individuals have greater knee synovial inflammation and associated structural and cartilage compositional degeneration: data from the osteoarthritis initiative.

ObjectiveThis work aims to study (i) the relationship between body mass index (BMI) and knee synovial inflammation using non-contrast-enhanced MRI and (ii) the association of synovial inflammation versus degenerative abnormalities and pain.Materials and methodsSubjects with risk for and mild to moderate radiographic osteoarthritis were selected from the Osteoarthritis Initiative. Subjects were grouped into three BMI categories with 87 subjects per group: normal weight (BMI, 20-24.9&nbsp;kg/m2), overweight (BMI, 25-29.9&nbsp;kg/m2), and obese (BMI, ≥ 30&nbsp;kg/m2), frequency matched for age, sex, race, Kellgren-Lawrence grade, and history of knee surgery and injury. Semi-quantitative synovial inflammation imaging biomarkers were obtained including effusion-synovitis, size and intensity of infrapatellar fat pad signal abnormality, and synovial proliferation score. Cartilage composition was measured using T2 relaxation time and structural abnormalities using the whole-organ magnetic resonance imaging score (WORMS). The Western Ontario and McMasters (WOMAC) Osteoarthritis Index was used for pain assessment. Intra- and inter-reader reproducibility was assessed by kappa values.ResultsOverweight and obese groups had higher prevalence and severity of all synovial inflammatory markers (p ≤ 0.03). Positive associations were found between synovial inflammation imaging biomarkers and average T2 values, WORMS maximum scores and total WOMAC pain scores (p &lt; 0.05). Intra- and inter-reader kappa values for imaging biomarkers were high (0.76-1.00 and 0.60-0.94, respectively).ConclusionBeing overweight or obese was significantly associated with a greater prevalence and severity of synovial inflammation imaging biomarkers. Substantial reproducibility and high correlation with knee structural, cartilage compositional degeneration, and WOMAC pain scores validate the synovial inflammation biomarkers used in this study

Obese and overweight individuals have greater knee synovial inflammation and associated structural and cartilage compositional degeneration: data from the osteoarthritis initiative.

ObjectiveThis work aims to study (i) the relationship between body mass index (BMI) and knee synovial inflammation using non-contrast-enhanced MRI and (ii) the association of synovial inflammation versus degenerative abnormalities and pain.Materials and methodsSubjects with risk for and mild to moderate radiographic osteoarthritis were selected from the Osteoarthritis Initiative. Subjects were grouped into three BMI categories with 87 subjects per group: normal weight (BMI, 20-24.9&nbsp;kg/m2), overweight (BMI, 25-29.9&nbsp;kg/m2), and obese (BMI, ≥ 30&nbsp;kg/m2), frequency matched for age, sex, race, Kellgren-Lawrence grade, and history of knee surgery and injury. Semi-quantitative synovial inflammation imaging biomarkers were obtained including effusion-synovitis, size and intensity of infrapatellar fat pad signal abnormality, and synovial proliferation score. Cartilage composition was measured using T2 relaxation time and structural abnormalities using the whole-organ magnetic resonance imaging score (WORMS). The Western Ontario and McMasters (WOMAC) Osteoarthritis Index was used for pain assessment. Intra- and inter-reader reproducibility was assessed by kappa values.ResultsOverweight and obese groups had higher prevalence and severity of all synovial inflammatory markers (p ≤ 0.03). Positive associations were found between synovial inflammation imaging biomarkers and average T2 values, WORMS maximum scores and total WOMAC pain scores (p &lt; 0.05). Intra- and inter-reader kappa values for imaging biomarkers were high (0.76-1.00 and 0.60-0.94, respectively).ConclusionBeing overweight or obese was significantly associated with a greater prevalence and severity of synovial inflammation imaging biomarkers. Substantial reproducibility and high correlation with knee structural, cartilage compositional degeneration, and WOMAC pain scores validate the synovial inflammation biomarkers used in this study

Joint-adjacent Adipose Tissue by MRI is Associated With Prevalence and Progression of Knee Degenerative Changes: Data from the Osteoarthritis Initiative.

BackgroundAdipose tissue has recently gained interest as an independent imaging biomarker for osteoarthritis.PurposeTo explore 1) cross-sectional associations between local subcutaneous fat (SCF) thickness at the knee and the extent of degenerative changes in overweight and obese individuals and 2) associations between local fat distribution and progression of osteoarthritis over 4 years.Study typeRetrospective cohort study.Population338 obese and overweight participants from the Osteoarthritis Initiative cohort without radiographic evidence of osteoarthritis.Field strength3T: 3D-FLASH-WE; 3D-DESS-WE; T1w-SE; MSME.AssessmentBaseline SCF thickness was measured in standardized locations medial, lateral and anterior to the knee and the average joint-adjacent SCF (ajSCF) was calculated. Right thigh SCF cross-sectional area was assessed. Quantitative cartilage T2 relaxation times and semi-quantitative whole organ MRI scores (WORMS) were obtained at baseline and 4-year follow-up. WORMSsum was calculated as sum of cartilage, bone marrow edema, subchondral cyst, and meniscal scores.Statistical testsAssociations of SCF measures with baseline, and 4-year change in T2 and WORMS were analyzed using regression models. SCF measurements were standardized using the equation ValueParticipant-MeanCohortStandard deviation . Analyses were adjusted for age, sex, physical activity, and BMI.ResultsCross-sectionally, significant associations between lateral SCF, lateral compartment WORMS and T2 were found ( ΔWORMSsum1SDchange in lateralSCF , [95% CI]: 0.53, [0.12-0.95], P &lt; 0.05; ΔT2 : 0.50, [0.02-0.98], P &lt; 0.05). Moreover, greater lateral SCF was associated with faster progression of lateral WORMSsum gradings (OR&nbsp;=&nbsp;1.50, [1.05-2.15], P &lt; 0.05). No significant positive associations were found for thigh SCF and WORMSsum (P&nbsp;=&nbsp;0.44) or T2 measurements (medial: P&nbsp;=&nbsp;0.15, lateral: 0.39, patellar: P&nbsp;=&nbsp;0.75).Data conclusionJoint-adjacent SCF thickness was associated with imaging parameters of knee osteoarthritis, both cross-sectionally and longitudinally, while thigh SCF was not, suggesting a spatial association of SCF and knee osteoarthritis. Based on these findings, joint-adjacent SCF may play a role in the development and progression of knee osteoarthritis.Level of evidence4 TECHNICAL EFFICACY: Stage 5