Deep-learning-based biomarker of spinal cartilage endplate health using ultra-short echo time magnetic resonance imaging.

Background: T2* relaxation times in the spinal cartilage endplate (CEP) measured using ultra-short echo time magnetic resonance imaging (UTE MRI) reflect aspects of biochemical composition that influence the CEP's permeability to nutrients. Deficits in CEP composition measured using T2* biomarkers from UTE MRI are associated with more severe intervertebral disc degeneration in patients with chronic low back pain (cLBP). The goal of this study was to develop an objective, accurate, and efficient deep-learning-based method for calculating biomarkers of CEP health using UTE images. Methods: Multi-echo UTE MRI of the lumbar spine was acquired from a prospectively enrolled cross-sectional and consecutive cohort of 83 subjects spanning a wide range of ages and cLBP-related conditions. CEPs from the L4-S1 levels were manually segmented on 6,972 UTE images and used to train neural networks utilizing the u-net architecture. CEP segmentations and mean CEP T2* values derived from manually- and model-generated segmentations were compared using Dice scores, sensitivity, specificity, Bland-Altman, and receiver-operator characteristic (ROC) analysis. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated and related to model performance. Results: Compared with manual CEP segmentations, model-generated segmentations achieved sensitives of 0.80-0.91, specificities of 0.99, Dice scores of 0.77-0.85, area under the receiver-operating characteristic curve values of 0.99, and precision-recall (PR) AUC values of 0.56-0.77, depending on spinal level and sagittal image position. Mean CEP T2* values and principal CEP angles derived from the model-predicted segmentations had low bias in an unseen test dataset (T2* bias =0.33±2.37 ms, angle bias =0.36±2.65°). To simulate a hypothetical clinical scenario, the predicted segmentations were used to stratify CEPs into high, medium, and low T2* groups. Group predictions had diagnostic sensitivities of 0.77-0.86 and specificities of 0.86-0.95. Model performance was positively associated with image SNR and CNR. Conclusions: The trained deep learning models enable accurate, automated CEP segmentations and T2* biomarker computations that are statistically similar to those from manual segmentations. These models address limitations with inefficiency and subjectivity associated with manual methods. Such techniques could be used to elucidate the role of CEP composition in disc degeneration etiology and guide emerging therapies for cLBP.

Bullying, harassment, and discrimination of musculoskeletal researchers and the impact of the COVID-19 pandemic: an international study.

PURPOSE: Bullying, harassment, and discrimination (BHD) are prevalent in academic, scientific, and clinical departments, particularly orthopedic surgery, and can have lasting effects on victims. As it is unclear how BHD affects musculoskeletal (MSK) researchers, the following study assessed BHD in the MSK research community and whether the COVID-19 pandemic, which caused hardships in other industries, had an impact. METHODS: A web-based anonymous survey was developed in English by ORS Spine Section members to assess the impact of COVID-19 on MSK researchers in North America, Europe, and Asia, which included questions to evaluate the personal experience of researchers regarding BHD. RESULTS: 116 MSK researchers completed the survey. Of respondents, 34.5% (n = 40) focused on spine, 30.2% (n = 35) had multiple areas of interest, and 35.3% (n = 41) represented other areas of MSK research. BHD was observed by 26.7% (n = 31) of respondents and personally experienced by 11.2% (n = 13), with mid-career faculty both observing and experiencing the most BHD. Most who experienced BHD (53.8%, n = 7) experienced multiple forms. 32.8% (n = 38) of respondents were not able to speak out about BHD without fear of repercussions, with 13.8% (n = 16) being unsure about this. Of those who observed BHD, 54.8% (n = 17) noted that the COVID-19 pandemic had no impact on their observations. CONCLUSIONS: To our knowledge, this is the first study to address the prevalence and determinants of BHD among MSK researchers. MSK researchers experienced and observed BHD, while many were not comfortable reporting and discussing violations to their institution. The COVID-19 pandemic had mixed-effects on BHD. Awareness and proactive policy changes may be warranted to reduce/eliminate the occurrence of BHD in this community.

The contributions of cartilage endplate composition and vertebral bone marrow fat to intervertebral disc degeneration in patients with chronic low back pain.

PURPOSE: The composition of the subchondral bone marrow and cartilage endplate (CEP) could affect intervertebral disc health by influencing vertebral perfusion and nutrient diffusion. However, the relative contributions of these factors to disc degeneration in patients with chronic low back pain (cLBP) have not been quantified. The goal of this study was to use compositional biomarkers derived from quantitative MRI to establish how CEP composition (surrogate for permeability) and vertebral bone marrow fat fraction (BMFF, surrogate for perfusion) relate to disc degeneration. METHODS: MRI data from 60 patients with cLBP were included in this prospective observational study (28 female, 32 male; age = 40.0 ± 11.9 years, 19-65 [mean ± SD, min-max]). Ultra-short echo-time MRI was used to calculate CEP T2* relaxation times (reflecting biochemical composition), water-fat MRI was used to calculate vertebral BMFF, and T1ρ MRI was used to calculate T1ρ relaxation times in the nucleus pulposus (NP T1ρ, reflecting proteoglycan content and degenerative grade). Univariate linear regression was used to assess the independent effects of CEP T2* and vertebral BMFF on NP T1ρ. Mixed effects multivariable linear regression accounting for age, sex, and BMI was used to assess the combined relationship between variables. RESULTS: CEP T2* and vertebral BMFF were independently associated with NP T1ρ (p = 0.003 and 0.0001, respectively). After adjusting for age, sex, and BMI, NP T1ρ remained significantly associated with CEP T2* (p = 0.0001) but not vertebral BMFF (p = 0.43). CONCLUSION: Poor CEP composition plays a significant role in disc degeneration severity and can affect disc health both with and without deficits in vertebral perfusion.

Measurement of vertebral endplate bone marrow lesion (Modic change) composition with water-fat MRI and relationship to patient-reported outcome measures.

PURPOSE: Vertebral endplate bone marrow lesions ("Modic changes", MC) are associated with chronic low back pain (CLBP). Bone marrow composition in MC is poorly understood. The goals of this study were to: (1) measure bone marrow fat fraction (BMF) in CLBP patients with MC using water-fat MRI and (2) assess the relationship between BMF measurements and patient-reported clinical characteristics. METHODS: In this cross-sectional study, 42 CLBP patients (men, n = 21; age, 48 ± 12.4 years) and 18 asymptomatic controls (men, n = 10; 42.7 ± 12.8 years) underwent 3 T MRI between January 2016 and July 2018. Imaging consisted of T1- and T2-weighted sequences to evaluate MC and spoiled gradient-recalled echo sequence with asymmetric echoes and least-squares fitting to measure BMF. BMF was compared between vertebrae with and without MC using mixed effects models. The relationship between the BMF measurements and patient-reported disability scores was examined using regression. RESULTS: Twenty-seven subjects (26 CLBP, 1 control) had MC, and MC presence coincided with significantly altered BMF. In MC 1, BMF was lower than endplates without MC (absolute difference -22.3%; p < 0.001); in MC 2, BMF was higher (absolute difference 21.0%; p < 0.001). Absolute BMF differences between affected and unaffected marrow were larger in patients with greater disability (p = 0.029-0.032) and were not associated with pain (p = 0.49-0.83). CONCLUSION: BMF is significantly altered in MC. Water-fat MRI enables BMF measurements that may eventually form the basis for quantitative assessments of MC severity and progression.

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

Serum Biomarkers for Connective Tissue and Basement Membrane Remodeling are Associated with Vertebral Endplate Bone Marrow Lesions as Seen on MRI (Modic Changes).

Vertebral endplate bone marrow lesions, visualized on magnetic resonance imaging (MRI) as Modic changes (MC), are associated with chronic low back pain (cLBP). Since guidelines recommend against routine spinal MRI for cLBP in primary care, MC may be underdiagnosed. Serum biomarkers for MC would allow early diagnosis, inform clinical care decisions, and supplement treatment monitoring. We aimed to discover biomarkers in the blood serum that correlate with MC pathophysiological processes. For this single-site cross-sectional study, we recruited 54 subjects with 38 cLBP patients and 16 volunteers without a history of LBP. All subjects completed an Oswestry Disability Index (ODI) questionnaire and 10-cm Visual Analog Score (VAS) for LBP (VASback) and leg pain. Lumbar T1-weighted and fat-saturated T2-weighted MRI were acquired at 3T and used for MC classification in each endplate. Blood serum was collected on the day of MRI. Biomarkers related to disc resorption and bone marrow fibrosis were analyzed with enzyme-linked immune-absorbent assays. The concentration of biomarkers between no MC and any type of MC (AnyMC), MC1, and MC2 were compared. The Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated for each biomarker and for bivariable biomarker models. We found that biomarkers related to type III and type IV collagen degradation and formation tended to correlate with the presence of MC (p = 0.060-0.088). The bivariable model with the highest AUC was PRO-C3 + C4M and had a moderate diagnostic value for AnyMC in cLBP patients (AUC = 0.73, specificity = 78.9%, sensitivity = 73.7%). In conclusion, serum biomarkers related to the formation and degradation of type III and type IV collagen, which are key molecules in bone marrow fibrosis, correlated with MC presence. Bone marrow fibrosis may be an important pathophysiological process in MC that should be targeted in larger biomarker and treatment studies.

Measuring and reporting of vertebral endplate bone marrow lesions as seen on MRI (Modic changes): recommendations from the ISSLS Degenerative Spinal Phenotypes Group.

PURPOSE: The positive association between low back pain and MRI evidence of vertebral endplate bone marrow lesions, often called Modic changes (MC), offers the exciting prospect of diagnosing a specific phenotype of chronic low back pain (LBP). However, imprecision in the reporting of MC has introduced substantial challenges, as variations in both imaging equipment and scanning parameters can impact conspicuity of MC. This review discusses key methodological factors that impact MC classification and recommends guidelines for more consistent MC reporting that will allow for better integration of research into this LBP phenotype. METHODS: Non-systematic literature review. RESULTS: The high diagnostic specificity of MC classification for a painful level contributes to the significant association observed between MC and LBP, whereas low and variable sensitivity underlies the between- and within-study variability in observed associations. Poor sensitivity may be owing to the presence of other pain generators, to the limited MRI resolution, and to the imperfect reliability of MC classification, which lowers diagnostic sensitivity and thus influences the association between MC and LBP. Importantly, magnetic field strength and pulse sequence parameters also impact detection of MC. Advances in pulse sequences may improve reliability and prove valuable for quantifying lesion severity. CONCLUSIONS: Comparison of MC data between studies can be problematic. Various methodological factors impact detection and classification of MC, and the lack of reporting guidelines hinders interpretation and comparison of findings. Thus, it is critical to adopt imaging and reporting standards that codify acceptable methodological criteria. These slides can be retrieved under Electronic Supplementary Material.

Glucocorticoid suppression of osteocyte perilacunar remodeling is associated with subchondral bone degeneration in osteonecrosis.

Through a process called perilacunar remodeling, bone-embedded osteocytes dynamically resorb and replace the surrounding perilacunar bone matrix to maintain mineral homeostasis. The vital canalicular networks required for osteocyte nourishment and communication, as well as the exquisitely organized bone extracellular matrix, also depend upon perilacunar remodeling. Nonetheless, many questions remain about the regulation of perilacunar remodeling and its role in skeletal disease. Here, we find that suppression of osteocyte-driven perilacunar remodeling, a fundamental cellular mechanism, plays a critical role in the glucocorticoid-induced osteonecrosis. In glucocorticoid-treated mice, we find that glucocorticoids coordinately suppress expression of several proteases required for perilacunar remodeling while causing degeneration of the osteocyte lacunocanalicular network, collagen disorganization, and matrix hypermineralization; all of which are apparent in human osteonecrotic lesions. Thus, osteocyte-mediated perilacunar remodeling maintains bone homeostasis, is dysregulated in skeletal disease, and may represent an attractive therapeutic target for the treatment of osteonecrosis.

Pathobiology of Modic changes.

PURPOSE: Low back pain (LBP) is the most disabling condition worldwide. Although LBP relates to different spinal pathologies, vertebral bone marrow lesions visualized as Modic changes on MRI have a high specificity for discogenic LBP. This review summarizes the pathobiology of Modic changes and suggests a disease model. METHODS: Non-systematic literature review. RESULTS: Chemical and mechanical stimulation of nociceptors adjacent to damaged endplates are likely a source of pain. Modic changes are adjacent to a degenerated intervertebral disc and have three generally interconvertible types suggesting that the different Modic change types represent different stages of the same pathological process, which is characterized by inflammation, high bone turnover, and fibrosis. A disease model is suggested where disc/endplate damage and the persistence of an inflammatory stimulus (i.e., occult discitis or autoimmune response against disc material) create predisposing conditions. The risk to develop Modic changes likely depends on the inflammatory potential of the disc and the capacity of the bone marrow to respond to it. Bone marrow lesions in osteoarthritic knee joints share many characteristics with Modic changes adjacent to degenerated discs and suggest that damage-associated molecular patterns and marrow fat metabolism are important pathogenetic factors. There is no consensus on the ideal therapy. Non-surgical treatment approaches including intradiscal steroid injections, anti-TNF-α antibody, antibiotics, and bisphosphonates have some demonstrated efficacy in mostly non-replicated clinical studies in reducing Modic changes in the short term, but with unknown long-term benefits. New diagnostic tools and animal models are required to improve painful Modic change identification and classification, and to clarify the pathogenesis. CONCLUSION: Modic changes are likely to be more than just a coincidental imaging finding in LBP patients and rather represent an underlying pathology that should be a target for therapy.

Structural parameters determining the strength of the porcine vertebral body affected by tumours.

Spinal metastatic disease could lead to catastrophic consequences for the patient. However, the structural parameters that explain the weakening of vertebrae affected by tumours are not fully understood. In this study, we developed a specimen-specific finite element model to predict the strength of the porcine vertebra with simulated tumours and used it to find the structural parameters determining the strength. We validated our model with mechanical testing and then we analysed the compressive strength of intact vertebrae and seven defects with different size and shape. The results showed that the minimum bone mineral mass of the cross section and areal defect fraction were the best predictors of the normalized strength. We also found that areal parameters appeared to be better predictors than the volumetric ones. In conclusion, reduction in bone strength for vertebrae weakened by metastatic tumours is mostly associated with decrease in the mechanical properties of the cross section.