Vertebral bone quality score was associated with paraspinal muscles fat infiltration, but not modic classification in patients with chronic low back pain: a prospective cross-sectional study.

BACKGROUND: The lumbar vertebra and paraspinal muscles play an important role in maintaining the stability of the lumbar spine. Therefore, the aim of this study was to investigate the relationship between paraspinal muscles fat infiltration and vertebral body related changes [vertebral bone quality (VBQ) score and Modic changes (MCs)] in patients with chronic low back pain (CLBP). METHODS: Patients with CLBP were prospectively collected in four hospitals and all patients underwent 3.0T magnetic resonance scanning. Basic clinical information was collected, including age, sex, course of disease (COD), and body mass index (BMI). MCs were divided into 3 types based on their signal intensity on T1 and T2-weighted imaging. VBQ was obtained by midsagittal T1-weighted imaging (T1WI) and calculated using the formula: SIL1-4/SICSF. The Proton density fat fraction (PDFF) values and cross-sectional area (CSA) of paraspinal muscles were measured on the fat fraction map from the iterative decomposition of water and fat with the echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) sequences and in/out phase images at the central level of the L4/5 and L5/S1 discs. RESULTS: This study included 476 patients with CLBP, including 189 males and 287 females. 69% had no Modic changes and 31% had Modic changes. There was no difference in CSA and PDFF for multifidus(MF) and erector spinae (ES) at both levels between Modic type I and type II, all P values>0.05. Spearman correlation analysis showed that VBQ was weakly negatively correlated with paraspinal muscles CSA (all r values < 0.3 and all p values < 0.05), moderately positive correlation with PDFF of MF at L4/5 level (r values = 0.304, p values<0.001) and weakly positively correlated with PDFF of other muscles (all r values<0.3 and all p values<0.001). Multivariate linear regression analysis showed that age (ß = 0.141, p < 0.001), gender (ß = 4.285, p < 0.001) and VBQ (ß = 1.310, p = 0.001) were related to the total PDFF of muscles. For MCs, binary logistic regression showed that the odds ratio values of age, BMI and COD were 1.092, 1.082 and 1.004, respectively (all p values ＜ 0.05). CONCLUSIONS: PDFF of paraspinal muscles was not associated with Modic classification. In addition to age and gender, PDFF of paraspinal muscles is also affected by VBQ. Age and BMI are considered risk factors for the MCs in CLBP patients.

Multidimensional risk factor analysis of acute low back pain progressing to chronicity: a longitudinal cohort study protocol.

Introduction: Approximately 40% of patients with acute low back pain (LBP) develop chronic low back pain, which significantly increases the risk of poor prognosis. To reduce the risk of acute LBP becoming chronic, effective preventive strategies are needed. Early identification of risk factors for the development of chronic LBP can help clinicians choose appropriate treatment options and improve patient outcomes. However, previous screening tools have not considered medical imaging findings. The aim of this study is to identify factors that can predict the risk of acute LBP becoming chronic based on clinical information, pain and disability assessment, and MRI imaging findings. This protocol describes the methodology and plan for investigating multidimensional risk factors for acute LBP becoming chronic, in order to better understand the development of acute LBP and prevent chronic LBP. Methods: This is a prospective multicenter study. We plan to recruit 1,000 adult patients with acute low back pain from four centers. In order to select four representative centers, we find the larger hospitals from different regions in Yunnan Province. The study will use a longitudinal cohort design. Patients will undergo baseline assessments upon admission and will be followed up for 5 years to collect the time of chronicity and associated risk factors. Upon admission, patients will be collected detailed demographic information, subjective and objective pain scores, disability scale, and lumbar spine MRI scanning. In addition, patient's medical history, lifestyle, psychological factors will be collected. Patients will be followed up at 3 months, 6 months, 1 year, 2 years and up for 5 years after admission to collect the time of chronicity and associated factors. Multivariate analysis will be used to explore the multidimensional risk factors affecting the chronicity of acute LBP patients (such as age, gender, BMI, degree of intervertebral disc degeneration, etc.), and survival analysis will be performed to explore the impact of each factor on the time of chronicity. Ethics and dissemination: The study has been approved by the institutional research ethics committee of each study center (main center number: 2022-L-305). Results will be disseminated through scientific conferences and peer-reviewed publications, as well as meetings with stakeholders.

Dynamic analysis of pulmonary computed tomography (CT) characteristics in cured coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: To retrospectively analyze the pulmonary computed tomography (CT) characteristics and dynamic changes in the lungs of cured coronavirus disease 2019 (COVID-19) patients at discharge and reexamination. METHODS: A total of 155 cured COVID-19 patients admitted to designated hospitals in Yunnan Province, China, from February 1, 2020, to March 20, 2020, were included. All patients underwent pulmonary CT at discharge and at 2 weeks after discharge (during reexamination at hospital). A retrospective analysis was performed using these two pulmonary CT scans of the cured patients to observe changes in the number, distribution, morphology, and density of lesions. RESULTS: At discharge, the lung CT images of 15 cured patients showed no obvious lesions, while those of the remaining 140 patients showed different degrees of residual lesions. Patients with moderate disease mostly had multiple pulmonary lesions, mainly in the lower lobes of both lungs. At reexamination, the lung lesions in the patients with moderate disease had significantly improved (P<0.05), and the lung lesions in the patients with severe disease had partially improved, especially in patients with multi-lobe involvement (χ 2 =3.956, P<0.05). At reexamination, the lung lesions of patients with severe disease did not show significant changes (P>0.05). CONCLUSIONS: The pulmonary CT manifestations of cured COVID-19 patients had certain characteristics and variation patterns, providing a reference for the clinical evaluation of treatment efficacy and prognosis of patients.

Hip-Joint CT Image Segmentation Based on Hidden Markov Model with Gauss Regression Constraints.

Hip-joint CT images have low organizational contrast, irregular shape of boundaries and image noises. Traditional segmentation algorithms often require manual intervention or introduction of some prior information, which results in low efficiency and is unable to meet clinical needs. In order to overcome the sensitivity of classical fuzzy clustering image segmentation algorithm to image noise, this paper proposes a fuzzy clustering image segmentation algorithm combining Gaussian regression model (GRM) and hidden Markov random field (HMRF). The algorithm uses the prior information to regularize the objective function of the fuzzy C-means, and then improves it with KL information. The HMRF model establishes the neighborhood relationship of the label field by prior probability, while CRM model establishes the neighborhood relationship of feature field on the basis of the consistency between the central pixel label and its neighborhood pixel label. The experimental results show that the proposed algorithm has high segmentation accuracy.