Value of CT grayscale histogram features in the differential diagnosis of brucella spondylitis and pyogenic spondylitis

English Keywords:Brucella spondylitis  Pyogenic spondylitis  Computed tomography  Radiomics  Histogram analysis

English Abstract:

  【Abstract】 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.