Quantitative analysis of contrast-enhanced ultrasonography in rat models of hepatic acute graft-versus-host disease.

Background: Hepatic acute graft-versus-host disease (aGVHD) is a major life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). We hypothesized that contrast-enhanced ultrasound (CEUS) could serve as a new imaging biomarker in early diagnosis of hepatic aGVHD by detecting liver microcirculation. Methods: Thirty Wistar rats received allo-HSCT were finally included after excluding 9 rats, and they were randomly divided into 5 groups (1- to 5-week groups, 6 per group). Six rats were used for the control group without any intervention. We observed the clinical scores, serum liver enzyme levels and liver CEUS parameters of rats in each group. Hepatic aGVHD was finally confirmed by histopathologic analysis. The diagnostic performance of CEUS parameters in detecting GVHD was evaluated by comparing the area under the receiver operating curve (AUC) values. Results: After HSCT, the rats developed ruffling of fur, maculopapular rash, weight loss, accompanied by increased clinical scores. Serum liver enzymes were significantly higher than those in the control group from the third week, especially alkaline phosphatase, while CEUS parameters, peak intensity (PI) and mean transit time (MTT), changed in the second week (P<0.001). Compared with non-aGVHD group, the PI was significantly decreased while time to peak and MTT were prolonged in aGVHD group. CEUS parameters were more strongly correlated with pathological grade than serology. PI was an independent predictor for hepatic aGVHD. The AUC of CEUS parameters for diagnosing hepatic aGVHD was 0.933 (95% CI: 0.779-0.992), which was higher than that of clinical scores (AUC =0.748, 95% CI: 0.557-0.888, P=0.032) and serological markers (AUC =0.902, 95% CI: 0.737-0.980, P=0.694). Conclusions: CEUS exhibits promising applications as a quantitative method to detect hepatic aGVHD and early liver damage.

Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.

Background: Knowledge graphs are a powerful tool for organizing knowledge, processing information and integrating scattered information, effectively visualizing the relationships among entities and supporting further intelligent applications. One of the critical tasks in building knowledge graphs is knowledge extraction. The existing knowledge extraction models in the Chinese medical domain usually require high-quality and large-scale manually labeled corpora for model training. In this study, we investigate rheumatoid arthritis (RA)-related Chinese electronic medical records (CEMRs) and address the automatic knowledge extraction task with a small number of annotated samples from CEMRs, from which an authoritative RA knowledge graph is constructed. Methods: After constructing the domain ontology of RA and completing manual labeling, we propose the MC-bidirectional encoder representation from transformers-bidirectional long short-term memory-conditional random field (BERT-BiLSTM-CRF) model for the named entity recognition (NER) task and the MC-BERT + feedforward neural network (FFNN) model for the entity extraction task. The pretrained language model (MC-BERT) is trained with many unlabeled medical data and fine-tuned using other medical domain datasets. We apply the established model to automatically label the remaining CEMRs, and then an RA knowledge graph is constructed based on the entities and entity relations, a preliminary assessment is conducted, and an intelligent application is presented. Results: The proposed model achieved better performance than that of other widely used models in knowledge extraction tasks, with mean F1 scores of 92.96% in entity recognition and 95.29% in relation extraction. This study preliminarily confirmed that using a pretrained medical language model could solve the problem that knowledge extraction from CEMRs requires a large number of manual annotations. An RA knowledge graph based on the above identified entities and extracted relations from 1,986 CEMRs was constructed. Experts verified the effectiveness of the constructed RA knowledge graph. Conclusions: In this paper, an RA knowledge graph based on CEMRs was established, the processes of data annotation, automatic knowledge extraction, and knowledge graph construction were described, and a preliminary assessment and an application were presented. The study demonstrated the viability of a pretrained language model combined with a deep neural network for knowledge extraction tasks from CEMRs based on a small number of manually annotated samples.

Role of Multiparametric Ultrasound in Evaluating Hepatic Acute Graft-versus-Host Disease: An Animal Study.

OBJECTIVE: Hepatic acute graft-versus-host disease (aGVHD) is a serious complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT) and is one of the leading causes of early non-recurrent death. The current diagnosis is based mainly based on clinical diagnosis, and there is a lack of non-invasive quantitative diagnosis methods. We propose a multiparametric ultrasound (MPUS) imaging method and explore its effectiveness in evaluating hepatic aGVHD. METHODS: In this study, 48 female Wistar rats were used as receptors and 12 male Fischer 344 rats were used as donors for allo-HSCT to establish aGVHD models. After transplantation, 8 rats were randomly selected for ultrasonic examination weekly, including color Doppler ultrasound, contrast-enhanced ultrasound (CEUS) and shear wave dispersion (SWD) imaging. The values of nine ultrasonic parameters were obtained. Hepatic aGVHD was subsequently diagnosed by histopathological analysis. A classification model for predicting hepatic aGVHD was established using principal component analysis and support vector machines. RESULTS: According to the pathological results, the transplanted rats were categorized into the hepatic aGVHD and non-GVHD (nGVHD) groups. All parameters obtained by MPUS differed statistically between the two groups. The first three contributing percentages of principal component analysis results were resistivity index, peak intensity and shear wave dispersion slope, respectively. The accuracy of classifying aGVHD and nGVHD using support vector machines reached 100%. The accuracy of the multiparameter classifier was significantly higher than that of the single parameter. CONCLUSION: The MPUS imaging method has proven to be useful in detecting hepatic aGVHD.

Efficacy of shear wave dispersion imaging for viscoelastic assessment of the liver in acute graft-versus-host disease rats.

Background: To investigate the feasibility of using shear wave dispersion (SWD) imaging to evaluate hepatic acute graft-versus-host disease (aGVHD) in a rat model. Methods: To establish an aGVHD model, 30 Wistar rats were subjected to bone marrow transplantation, 10 Fischer 344 rats were used as donors, and 6 Wistar rats were used as the control group. Each week, 6 rats were randomly chosen and divided into groups of 1 week (1 w) to 5 weeks (5 w). For each subgroup, the rats received a clinical index assessment and shear wave dispersion (SWD) examination with 2 quantitative values, shear wave (SW) speed and SWD slope. The histological characteristics were then used as the reference standard to divide the rats into the aGVHD group and the no aGVHD (nGVHD) group. Results: In the 2 weeks (2 w) group, only SWD slope [median: 7.26, interquartile range (IQR): 7.04 to 7.31] showed a significant increase in the measured value (P<0.05). The value of the 3 weeks (3 w) group (median: 7.88, IQR: 7.84 to 8.49) significantly increased compared with the 2 w value (P<0.05). Although the value increased gradually from week 3 to week 5, it had no statistical significance (P>0.05). The SW speed [mean ± standard deviation (SD): 1.54±0.11, 95% confidence interval (CI): 1.48 to 1.59] and SWD slope (mean ± SD: 8.29±0.56, 95% CI: 7.99 to 8.59) of the aGVHD group were higher than those of the control group and the nGVHD group (P<0.001). The correlation of SWD slope with pathological grade was the highest (r=0.798, P<0.01), followed by SW speed (r=0.785, P<0.01), and the correlation of clinical index with pathological grade was the lowest (r=0.751, P<0.01). In addition, the area under the receiver operating characteristic (ROC) curve (AUC) value of aGVHD using the SWD slope was 0.844 (95% CI: 0.67 to 0.95, sensitivity: 93.75%, specificity: 78.57%), which was higher than the AUC of both SW speed and clinical index, and the difference was statistically significant compared to the AUC of the clinical index. Conclusions: The SWD slope could show significant abnormalities earlier than SW speed and clinical index and is also more consistent with the change in aGVHD severity level. The SWD slope may help in detecting hepatic aGVHD during ultrasound SWD examination.

Detecting and monitoring tumors in orthotopic colorectal liver metastatic animal models with high-resolution ultrasound.

The ability to noninvasively detect and monitor the growth of orthotopic liver transplantation tumors is critical for replicating advanced colorectal cancer liver metastases (CRLMs) in animal models. We assessed the use of high-resolution ultrasound (HRU) to monitor CRLMs transplanted using various cell concentrations. Sixty BALB/c female mice were randomly divided into 3 groups, and murine colonic CT26 cells were injected into the left liver lobe at concentrations of 1 × 102 (group 1), 1 × 103 (group 2), or 1 × 104 (group 3). Tumor presentation, location, number, size, shape, and echogenicity were assessed daily with 24-MHz center frequency HRU starting 6 days after injection. Animals were sacrificed when the largest tumor was ≥ 1 cm in diameter. Sensitivity, specificity, and area under curve (AUC) of CRLMs diagnosed with HRU were calculated using receiver operating characteristic curve analysis. In group 1, 94% of mice formed < 5 tumors, and 41% formed a single tumor. Tumors were first detected with HRU on day 12 in group 1, day 10 in group 2, and day 7 in group 3; tumor volume doubling times were 14-15 days, 11-12 days, and 7-8 days, respectively. With a long diameter threshold of 2.4 mm, diagnostic sensitivity and specificity of HRU were 94.1% and 88.7%, respectively, and the AUC was 0.962. These findings suggest that HRU can be used to accurately detect and monitor the growth of CRLMs in an orthotopic transplantation mouse model, especially when a lower concentration of cells is used.

Viscoelasticity assessment for in vivo quantification of muscle contusion injury in rats using shear wave elastography.

The aim of the study described here was to investigate the role of viscoelasticity in assessing muscle fibrosis and inflammation in a rat model of contusion using quantitative shear wave elastography (SWE). Unilateral gastrocnemius muscle contusion was induced in 32 male rats using an impactor apparatus. The contralateral muscles served as the control group. SWE was applied to the control group and rats 1, 3, 14 and 21 d after successful modeling (each time point group, n = 8). Histologic features were used as reference standards. The degree of fibrosis was moderately correlated with shear wave speed (r = 0.53), whereas the degree of inflammation was well correlated with shear wave dispersion (SWD) slope (r = 0.74). The area under the receiver operating characteristic curve (AUC) for the dispersion slope for muscle inflammation and fibrosis assessment was 0.87 (95% confidence interval: 0.705-0.963), which exceeded that of the shear wave speed (0.68, 95% confidence interval: 0.494-0.834). The larger decline in dispersion slope in the fibrotic stage than in the inflammation stage (1-d group vs. 14-d group or 21-d group, p < 0.05) indicated better predictive performance than the shear wave speed.

Quantification of Nerve Viscosity Using Shear Wave Dispersion Imaging in Diabetic Rats: A Novel Technique for Evaluating Diabetic Neuropathy.

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.

A Scoring System for Assessing the Risk of Malignant Partially Cystic Thyroid Nodules Based on Ultrasound Features.

OBJECTIVE: To assess the ultrasound (US) features of partially cystic thyroid nodules (PCTNs) and to establish a scoring system to further improve the diagnostic accuracy. METHODS: A total of 262 consecutive nodules from September 2017 to March 2020 were included in a primary cohort to construct a scoring system. Moreover, 83 consecutive nodules were enrolled as an validation cohort from May 2018 to August 2020. All nodules were determined to be benign or malignant according to the pathological results after surgery or ultrasound-guided fine-needle aspiration (US-FNA). The US images and demographic characteristics of the patients were analyzed. The ultrasound features of PCTNs were extracted from primary cohort by two experienced radiologists. The features extracted were used to develop a scoring system using logistic regression analysis. Receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic efficacy of the scoring system in both the primary cohort and validation cohort. In addition, the radiologists evaluated the benign and malignant PCTNs of the validation cohort according to the ACR TI-RADS guidelines and clinical experience, and the accuracy of their diagnosis were compared with that of the scoring system. RESULTS: Based on the eight features of PCTNs, the scoring system showed good differentiation and reproducibility in both cohorts. The scoring system was based on eight features of PCTNs and showed good performance. The area under the curve (AUC) was 0.876 (95% CI, 0.830 - 0.913) in the primary cohort and 0.829(95% CI, 0.730 - 0.903) in the validation cohort. The optimal cutoff value of the scoring system for the diagnosis of malignant PCTNs was 4 points, with a good sensitivity of 71.05% and specificity of 87.63%. The scoring system (AUC=0.829) was superior to radiologists (AUC= 0.736) in diagnosing PCTNs and is a promising method for clinical application. CONCLUSIONS: The scoring system described herein is a convenient and clinically valuable method that can diagnose PCTNs with relatively high accuracy. The use of this method to diagnose PCTNs, which have been previously underestimated, will allow PCTNs to receive reasonable attention, and assist radiologist to confidently diagnose the benignity or malignancy.