Objectives:To evaluate the values of sagittal CT image histogram features in the differential diagnosis of brucella spondylitis(BS)and pyogenic spondylitis(PS).Methods:The data of 40 BS patients[25 males,15 females;age:51.6±13.0 years old;body mass index(BMI):23(20,28)kg/m2,the BS group]and 33 PS patients[13 males,20 females;age:50.8±16.7 years old;BMI:23(20,26)kg/m2,the PS group]who underwent CT examination of the spine in our hospital and were confirmed through pathology and/or etiology were collected.The region of interest(ROI)was delineated on each level of the sagittal CT images of the two groups of patients by using the 3D Slicer platform and grayscale global histogram analysis was performed.The clinical data were compared using chi square test,independent sample t-test,and Mann Whitney U test between the two groups of patients;Univariate analysis,correlation analysis,and multivariate analysis were used in sequence to identify the histogram features with significant differences between the two groups(including 10％percentile,1％percentile,25％percentile,5％percentile,median,minimum,skewness,and variance);Logistic regression and the screened features were combined for modeling,and receiver operating characteristic(ROC)curves were drawn and areas under the curve(AUC)were calculated to compare the discriminative ability of each histogram feature.Results:There was no statistically significant difference in age,gender,and BMI between the two groups of patients(P＞0.05).Among the histogram parameters,10％percentile value,1％percentile value,25％percentile value,5％percentile value,median,minimum value,skewness,and variance were statistically different between the two groups(P＜0.05).The 10％percentile value displayed the best diagnostic performance,with an AUC value of 0.824 and a specificity of 0.893.The combined model had an AUC value of 0.860 and a specificity of 0.946.Conclusions:Based on 10％percentile value of CT grayscale histogram and joint model,PS and BS can be distinguished effectively,providing a basis for accurately distinguishing the two diseases in clinical practice.

Objective:To elucidate the diagnostic utility of clinical features and radiomics characteristics derived from magnetic resonance imaging T2-weighted fat-suppressed images (T2WI-FS) in differentiating brucellosis spondylitis from pyogenic spondylitis.Methods:Clinical records of 26 patients diagnosed with Brucellosis Spondylitis and 23 with Pyogenic Spondylitis were retrospectively reviewed from Xinjiang Medical University First Affiliated Hospital between January 2019 and December 2021. Confirmatory diagnosis was ascertained through histopathological examination and/or microbial culture. Demographic characteristics, symptoms, clinical manifestations, and hematological tests were collected, followed by a univariate analysis to discern clinically significant risk factors. For the radiomics evaluation, preoperative sagittal T2WI-FS images were utilized. Regions of interest (ROIs) were manually outlined by two adept radiologists. Employing the PyRadiomics toolkit, an extensive array of radiomics features encompassing shape, texture, and gray-level attributes were extracted, yielding a total of 1,500 radiomics parameters. Feature normalization and redundancy elimination were implemented to optimize the predictive efficacy of the model. Discriminatory radiomics features were identified through statistical methods like t-tests or rank-sum tests, followed by refinement via least absolute shrinkage and selection operator (LASSO) regression. An integrative logistic regression model incorporated selected clinical risk factors, radiomics attributes, and a composite radiomics score (Rad-Score). The diagnostic performance of three models clinical risk factors alone, Rad-Score alone, and a synergistic combination were appraised using a confusion matrix and receiver operating characteristic (ROC) analysis.Results:The cohort comprised 49 patients, including 36 males and 13 females, with a mean age of 53.79±13.79 years. C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) emerged as significant clinical risk factors ( P<0.005). A total of seven discriminative radiomics features (logarithm glrlm SRLGLE, exponential glcm Imc1, exponential glcm MCC, exponential gldm SDLGLE, square glcm ClusterShade, squareroot glszm SALGLE and wavelet.HHH glrlm Run Variance) were isolated through LASSO regression. Among these selected features, the square glcmClusterShade feature exhibited the best performance, with an area under the curve (AUC) value of 0.780. It demonstrated a sensitivity of 68.8%, specificity of 94.4%, accuracy of 82.4%, precision of 91.7%, and negative predictive value of 0.773. Furthermore, the logarithm glrlm SRLGLE feature had an AUC of 0.736, sensitivity of 68.8%, specificity of 72.2%, accuracy of 76.5%, precision of 72.2%, and negative predictive value of 0.812. The exponential glcm Imc1 feature had an AUC of 0.736, sensitivity of 50.0%, specificity of 94.4%, accuracy of 73.5%, precision of 88.9%, and negative predictive value of 0.680. Three diagnostic models were constructed: the clinical risk factors model, the radiomics score model, and the integrated model (clinical risk factors+radiomics score), which showed AUC values of 0.801, 0.818, and 0.875, respectively. Notably, the integrated model exhibited superior diagnostic efficacy. Conclusion:The amalgamation of clinical and radiomics variables within a sophisticated, integrated model demonstrates promising efficacy in accurately discriminating between Brucellosis Spondylitis and Pyogenic Spondylitis. This cutting-edge methodology underscores its potential in facilitating nuanced clinical decision-making, precise diagnostic differentiation, and the tailoring of therapeutic regimens.

Objective:To analyze risk factors for prolonged postoperative hospitalization in patients with Brucella spondylitis (BS).Methods:A total of 130 patients with BS who underwent surgical treatment in the Department of Spine Surgery, the First Affiliated Hospital of Xinjiang Medical University from June 2011 to December 2021 were retrospectively analyzed. There were 95 males and 35 females, aged 51.53±12.26 years (range, 20-76 years). The 75th percentile of patients' hospitalization time was used as the critical value, and hospitalization time≥75% quartile was defined as prolonged hospitalization time. Baseline data, clinical outcomes, laboratory test indices, and imaging findings were compared between patients with prolonged and normal length of stay. Indicators with statistically significant differences between the two groups were included in a binary logistic regression analysis to determine independent risk factors for prolonged postoperative hospitalization for BS. The receiver operating characteristic (ROC) curve was plotted for subjects with prolonged postoperative hospitalization, and the area under the curve (AUC) for each independent risk factor was calculated. Additionally, 95% confidence intervals (CI), sensitivity, and specificity were determined.Results:All patients were operated successfully. The length of hospitalization was 6.98±2.73 days (range, 6-20 days). The 75% quartile of the length of hospitalization was 9 days, so hospitalization time≤9 days was considered as normal length of hospitalization (normal group) and more than 9 days was considered as prolonged hospitalization (prolonged group), of which there were 99 cases in the normal group and 31 cases in the prolonged group. All patients were followed up for 12.3±3.2 months (range, 7-31 months). The results of univariate analysis showed elevated body mass index ( Z=901.00, P<0.001), recent wasting (χ 2=15.84, P<0.001), elevated erythrocyte sedimentation rate ( t=-4.82, P<0.001), elevated C-reactive protein ( Z=895.50, P<0.001), decreased albumin ( Z=2199.50, P<0.001), presence of epidural abscess on MRI (χ 2=10.45, P=0.001), and increased intraoperative blood loss (χ 2=8.81, P=0.003) may be risk factors for prolonged hospitalization after BS. Binary logistic regression analysis showed that increased body mass index ( OR=1.25, P=0.033), recent wasting ( OR=0.04, P=3.395), increased erythrocyte sedimentation rate ( OR=7.50, P<0.001), elevated C-reactive protein ( OR=4.71, P=0.008), and epidural abscess on MRI ( OR=3.69, P=0.033) were independent risk factors for prolonged postoperative hospital stay of BS, and the AUC of ROC was 0.70, 0.71, 0.71, 0.75, 0.66, respectively. The AUC of the combined prediction model was 0.89, and the prediction value was good. Conclusion:Elevated body mass index, recent wasting, elevated C-reactive protein, elevated erythrocyte sedimentation rate, and the presence of an epidural abscess on MRI are independent risk factors for prolonged postoperative hospitalization in patients with BS, and the combined prediction model has better predictive efficacy.