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Objective:To investigate the feasibility of using shear wave dispersion (SWD) imaging in evaluating acute graft-versus-host disease (aGVHD) of the liver.Methods:A total of 42 Wistar rats were used as receptors and 10 Fischer 344 rats were used as donors for bone marrow transplantation to establish aGVHD models. Six rats were randomly selected every week for clinical observation and scoring. Then, ultrasonic SWD was performed to obtain shear wave speed (SWS) and shear wave dispersion slope (SWDS). Then, the histological characteristics were used as a reference standard, and the rats were divided into two groups: the group without aGVHD (nGVHD group) and the group with aGVHD. The differences in the clinical score and SWD values between the two groups were compared, the meaningful indexes were screened by binary Logistic regression analysis, and the joint prediction parameters were obtained. The ROC curve was plotted and the diagnostic efficiency was compared. The correlations between SWS, SWDS, clinical score and pathological grade were analyzed.Results:Clinical score, SWS, and SWDS in aGVHD group were higher than those in the nGVHD group (all P<0.05). The correlation between SWDS and pathological grade ( r=0.774, P<0.001) was higher than those between SWS, clinical score and pathological grade ( r=0.579, P=0.005; r=0.452, P=0.034). Logistic regression showed that SWDS was a significant indicator. In addition, the AUC values determined by SWDS and predictive parameters were (0.859, 0.886), which were significantly higher than the AUC of the clinical score (0.760, P<0.05). Conclusions:SWD imaging technology may become a promising method to evaluate hepatic aGVHD.
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Objective@#Viscoelasticity is an essential feature of nerves, although little is known about their viscous properties. The discovery of shear wave dispersion (SWD) imaging has presented a new approach for the non-invasive evaluation of tissue viscosity.The present study investigated the feasibility of using SWD imaging to evaluate diabetic neuropathy using the sciatic nerve in a diabetic rat model. @*Materials and Methods@#This study included 11 diabetic rats in the diabetic group and 12 healthy rats in the control group.Bilateral sciatic nerves were evaluated 3 months after treatment with streptozotocin. We measured the nerve cross-sectional area (CSA), nerve stiffness using shear wave elastography (SWE), and nerve viscosity using SWD imaging. The motor nerve conduction velocity (MNCV) was also measured. These four indicators and the histology of the sciatic nerves were then compared between the two groups. The performance of CSA, SWE, and SWD imaging in distinguishing the two groups was assessed using receiver operating characteristic (ROC) analysis. @*Results@#Nerve CSA, stiffness, and viscosity in the diabetic group was significantly higher than those in the control group (all p < 0.05). The results also revealed a significantly lower MNCV in the diabetic group (p = 0.005). Additionally, the density of myelinated fibers was significantly lower in the diabetic group (p = 0.004). The average thickness of the myelin sheath was also lower in the diabetic group (p = 0.012). The area under the ROC curve for distinguishing the diabetic neuropathy group from the control group was 0.876 for SWD imaging, which was significantly greater than 0.677 for CSA (p = 0.030) and 0.705 for SWE (p = 0.035). @*Conclusion@#Sciatic nerve viscosity measured using SWD imaging was significantly higher in diabetic rats. The viscosity measured using SWD imaging performed well in distinguishing the diabetic neuropathy group from the control group.Therefore, SWD imaging may be a promising method for the evaluation of diabetic neuropathy.
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Objective To explore the diagnostic efficiency of K‐T IRADS ,ACR‐T IRADS and AT A risk stratification in computer‐aided detection and diagnosis ( CAD ) software and the application value of CAD‐assisted ultrasound physicians in diagnosing thyroid nodules . Methods One hundred and ninety‐two thyroid nodules with postoperative pathological results were retrospectively analyzed . All of them were graded by K‐T IRADS ,ACR‐T IRADS and A T A with CAD software ,and the best guide was recognized by calculating the area under the ROC curve ,sensitivity and specificity . T hen ,based on the best guidelines for the classification criteria , the double‐blind method was used to compare the ability of the same ultrasonologist to diagnose thyroid nodules before and after CAD . Results T he AUC value of K‐T IRADS , ACR‐T IRADS ,A T A was 0 .88 ,0 .77 ,0 .62 respectively in the CAD software . T he difference between the two groups was statistically significant ( P ＜0 .05 ) . T here was no significant difference in the specificity between K‐T IRADS and A T A ( P ＝0 .176 ) ,w hich were both higher than ACR‐T IRADS with statistically significant differences ( P ＜ 0 .05 ) . T he AUC value of the diagnosis among CAD itself , ultrasound physicians and physicians combined CAD was 0 .88 ,0 .80 ,0 .93 ,respectively . T he difference between the two groups was statistically significant ( P ＜0 .05) . T here was no significant difference in the sensitivity between CAD itself and physicians combined CAD ( P ＝0 .163 ) ,w hich were both higher than ultrasound physicians with statistical significant differences( P ＜0 .05) . Among ultrasound physicians ,CAD itself and physicians combined CAD ,the difference in specificity between the two groups was statistically significant ( P ＜0 .05) . Conclusions All the three risk stratification systems of thyroid ultrasound in CAD software have good diagnostic values ,among w hich K‐T IRADS has the largest AUC . T he CAD software can assist ultrasound physicians to improve the thyroid nodule diagnostic performance , and has a good clinical application prospect .
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Objective@#To explore the diagnostic efficiency of K-TIRADS, ACR-TIRADS and ATA risk stratification in computer-aided detection and diagnosis(CAD) software and the application value of CAD-assisted ultrasound physicians in diagnosing thyroid nodules.@*Methods@#One hundred and ninety-two thyroid nodules with postoperative pathological results were retrospectively analyzed. All of them were graded by K-TIRADS, ACR-TIRADS and ATA with CAD software, and the best guide was recognized by calculating the area under the ROC curve, sensitivity and specificity. Then, based on the best guidelines for the classification criteria, the double-blind method was used to compare the ability of the same ultrasonologist to diagnose thyroid nodules before and after CAD.@*Results@#The AUC value of K-TIRADS, ACR-TIRADS, ATA was 0.88, 0.77, 0.62 respectively in the CAD software. The difference between the two groups was statistically significant (P<0.05). There was no significant difference in the specificity between K-TIRADS and ATA(P=0.176), which were both higher than ACR-TIRADS with statistically significant differences (P<0.05). The AUC value of the diagnosis among CAD itself, ultrasound physicians and physicians combined CAD was 0.88, 0.80, 0.93, respectively. The difference between the two groups was statistically significant (P<0.05). There was no significant difference in the sensitivity between CAD itself and physicians combined CAD(P=0.163), which were both higher than ultrasound physicians with statistical significant differences(P<0.05). Among ultrasound physicians, CAD itself and physicians combined CAD, the difference in specificity between the two groups was statistically significant(P<0.05).@*Conclusions@#All the three risk stratification systems of thyroid ultrasound in CAD software have good diagnostic values, among which K-TIRADS has the largest AUC. The CAD software can assist ultrasound physicians to improve the thyroid nodule diagnostic performance, and has a good clinical application prospect.