ABSTRACT
Objective:To explore MRI T 2-mapping and blood oxygenation level dependent (BOLD) to evaluate the functional changes of paraspinal muscle in rats with discogenic low back pain (DLBP) after swimming. Methods:Totally 54 female 1-month-old SD rats were selected, which were divided into 3 groups by random number table method, sham operation (Sham) group, DLBP non-swimming group and DLBP swimming group, with 18 rats in each group. Under the guidance of X-ray fluoroscopy, the L4/5 and L5/6 intervertebral discs of the rats in the DLBP non-swimming group and DLBP swimming group were punctured by the posterior approach, and establishment of DLBP rat model by destroying nucleus pulposus, and only paraspinal muscles at the same level were punctured in the Sham group. After modeling, the DLBP swimming group received swimming exercise intervention for 5 consecutive days (30 min/d), while the DLBP non-swimming group and Sham group did not receive any rehabilitation exercise intervention. Each group was divided into 3 time point subgroups on average, the T 2-mapping and BOLD sequences were scanned at 30, 90 and 180 days after modeling to obtain the T 2 value, R 2* value of the paraspinal muscles, and the paraspinal muscles at the modeling level were taken for immunofluorescence staining, and the fluorescence intensity of myosin heavy chain (MYH)1 (type Ⅱ muscle fiber) and MYH7 (type I muscle fiber) was analyzed. One-way analysis of variance was used for comparison among the 3 groups, and the Bonferroni method was used for multiple comparisons, and Pearson correlation coefficient was used to evaluate the correlation between quantitative MRI parameters T 2 value, R 2* value and MYH1, MYH7 immunofluorescence intensity of rat paraspinal muscles at 180 days after modeling. Results:At 30 days after modeling, there was no significant difference in T 2 value and R 2* value among the 3 groups (all P>0.05). At 90 days after modeling, the T 2 value of the DLBP swimming group was higher than that of the DLBP non-swimming group, and the T 2 value of the DLBP non-swimming group was lower than that of the Sham group (all P<0.05), and there was no significant difference in the R 2* value among the 3 groups ( P>0.05). At 180 days after modeling, the T 2 value of the DLBP swimming group was higher than that of the DLBP non-swimming group, and the R 2* value was lower than that of the DLBP non-swimming group; the T 2 value of the DLBP non-swimming group was lower than that of the Sham group, and the R 2* value was higher than that of the Sham group (all P<0.05). At 30 and 90 days after modeling, there was no significant difference in the expressions of MYH1 and MYH7 among the 3 groups (all P>0.05). At 180 days after modeling, the expression of MYH1 decreased and the expression of MYH7 increased in the DLBP swimming group compared with the DLBP non-swimming group; the expression of MYH1 increased and the expression of MYH7 decreased in the DLBP non-swimming group compared with the Sham group (all P<0.05). At 180 days after modeling, the T 2 value had a moderate negative correlation with the fluorescence intensity of MYH1 ( r=-0.511, P=0.043), and a moderate positive correlation with the fluorescence intensity of MYH7 ( r=0.564, P=0.023); R 2* value was moderate positive correlated with the fluorescence intensity of MYH1 ( r=0.625, P=0.010), and moderate negative correlated with the fluorescence intensity of MYH7 ( r=-0.653, P=0.006). Conclusions:Swimming exercise can improve the reduction of water content and perfusion in the paraspinal muscles of DLBP rats, and reduce the transformation of muscle fibers from type Ⅰ to type Ⅱ, the changes of T 2 and R 2* value can reflect the transformation of paraspinal muscle fiber types to a certain extent.
ABSTRACT
Objective:To explore the classification performance of combined model constructed from CT signs combined with radiomics for discriminating COVID-19 pneumonia and other viral pneumonia.Methods:The clinical and CT imaging data of 181 patients with viral pneumonia confirmed by reverse transcription-polymerase chain reaction in 15 hospitals of Yunnan Province from March 2015 to March 2020 were analyzed retrospectively. The 181 patients were divided into COVID-19 group (89 cases) and non-COVID-19 group (92 cases), which were further divided into training cohort (126 cases) and test cohort (55 cases) at a ratio of 7∶3 using random stratified sampling. The CT signs of pneumonia were determined and the radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models for predicting COVID-19 pneumonia. The diagnostic performance of the models were evaluated using receiver operating characteristic (ROC) analysis, continuous net reclassification index (NRI) calibration curve and decision curve analysis.Results:The combined models consisted of 3 significant CT signs and 14 selected radiomics features. For the radiomics model alone, the area under the ROC curve (AUC) were 0.904 (sensitivity was 85.5%, specificity was 84.4%, accuracy was 84.9%) in the training cohort and 0.866 (sensitivity was 77.8%, specificity was 78.6%, accuracy 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity was 91.9%, specificity was 85.9%, accuracy was 88.9%), while that for the test cohort was 0.943 (sensitivity was 88.9%, specificity was 85.7%, accuracy was 87.3%). The AUC values of the combined model and the radiomics model in the differentiation of COVID-19 group and the non-COVID-19 group were significantly different in the training cohort ( Z=-2.43, P=0.015), but difference had no statistical significance in the test cohort ( Z=-1.73, P=0.083), and further analysis using the NRI showed that the combined model in both the training cohort and the test cohort had a positive improvement ability compared with radiomics model alone (training cohort: continuous NRI 1.077, 95 %CI 0.783-1.370; test cohort: continuous NRI 1.421, 95 %CI 1.051-1.790). The calibration curve showed that the prediction probability of COVID-19 predicted by the combined model was in good agreement with the observed value in the training and test cohorts; the decision curve showed that a net benefit greater than 0.6 could be obtained when the threshold probability of the combined model was 0-0.75. Conclusion:The combination of CT signs and radiomics might be a potential method for distinguishing COVID-19 and other viral pneumonia with good performance.