CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase.

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

Construction of the amniotic fluid-derived exosomal ceRNA network associated with ventricular septal defect.

Ventricular septal defect (VSD) is the most frequent congenital cardiac malformations. Amniotic fluid (AF) contains a higher abundance of biological compounds that could reflect fetal health information. The aims of our study were to construct a competitive endogenous RNA (ceRNA) network based on AF-derived exosomal ncRNAs. We conducted whole transcriptome profiling in six pairs of AF-derived exosomes from VSD fetuses and matched healthy controls. A total of 1252 differentially expressed (DE) mRNAs, 256 DE-miRNAs and 1090 DE-lncRNAs were found to be significantly altered in the VSD group. We constructed a ceRNA regulatory network including 46 mRNAs, 11 miRNAs and 47 lncRNAs. The expression level of 6 hub RNAs were validated using qRT-PCR. In conclusion, AF-derived exosomal VSD-related ceRNAs provide a basis for a better understanding of the role of ncRNAs in the pathogenesis and mechanisms of VSD, which may lead to the discovery of potential diagnostic biomarkers for fetal VSD.

Combination of high-frequency ultrasound and virtual touch tissue imaging and quantification improve the diagnostic efficiency for mild carpal tunnel syndrome.

BACKGROUND: Carpal tunnel syndrome (CTS) is the most common entrapment symptom in the peripheral nerves. High-frequency ultrasound (HFUS) is widely used in the diagnosis of CTS. Virtual Touch Tissue Imaging and Quantification (VTIQ), which provides more information about the hardness of organization, is used to diagnose CTS. However, the data of diagnostic value of them in various degrees of CTS are limited. Whether the combination of HFUS and VTIQ can improve the diagnostic efficiency also remains unknown. The study aimed to explore the diagnostic value of HFUS and VTIQ in various degrees of CTS and whether combination of HFUS and VTIQ could improve the diagnostic efficiency of CTS. METHODS: A collection and analysis of 133 CTS patients and 35 volunteers from January 2016 to January 2019 were performed. We compared the clinical characteristics, cross-sectional area (CSA) value and shear wave velocity SWVmean value of CTS group with volunteer group. RESULTS: The CSA value and SWVmean value of CTS cohort were significantly higher than volunteer group (10.79 ± 2.88 vs. 8.06 ± 1.39, p < 0.001, 4.36 ± 0.95 vs. 3.38 ± 1.09, p < 0.001, respectively). The area under the curve (AUC) of receiver operating characteristic (ROC) curve of CSA value and SWVmean value were 0.794 and 0.757, respectively. Hierarchical analysis of CSA value and SWVmean value showed that the AUC in the moderate and severe CTS group were higher than in mild CTS group. Furthermore, the CSA value combined with SWVmean value used to diagnose mild CTS was 0.758, which was higher than that of single CSA value or single SWVmean value. CONCLUSIONS: Both HFUS and VTIQ technology were feasible to evaluate CTS. HFUS was suitable for use in diagnosis of moderate and severe CTS. For mild CTS, combination of HFUS and VTIQ was relevant to improve the diagnostic efficiency of CTS.

Noninvasive evaluation of gastric emptying and gastric wall thickness in SLE patients.

OBJECTIVE: The objective of this study is to evaluate the gastric emptying in patients with systemic lupus erythematosus (SLE) with gastrointestinal involvement using three-dimensional (3D) ultrasonography. METHODS: The gastric emptying times at 25% (T1), 50% (T2), and 75% (T3) of SLE patients with gastrointestinal involvement (n = 40) and healthy controls (n = 80) were evaluated and compared. In addition, the correlations among the gastric wall thickness, SLE disease activity index (SLEDAI), and upper gastrointestinal symptoms were calculated. RESULTS: The gastric wall thickness was correlated with the SLEDAI (r = 0.928, p < 0.001) and the upper gastrointestinal symptom index (r = 0.848, p < 0.001). The emptying times T1, T2, and T3 of the SLE patients were 17.08 ± 2.65 min (mean ± standard deviation), 39.85 ± 6.54 min, and 83.58 ± 7.12 min, respectively. For healthy controls, they were 19.65 ± 5.39 min, 41.08 ± 7.51 min, and 70.34 ± 8.03 min. The T1 of the SLE patients was shorter (p < 0.01), while the T3 was longer (p < 0.001). Moreover, T3 in the SLE group had the best correlation with the upper gastrointestinal symptom index (r = 0.553, p < 0.001). T1 in the SLE group was anti-correlated with early satiety (r = -0.366, p < 0.05). CONCLUSIONS: Combining the emptying times T1 and T3, as well as the gastric wall thickness, the SLEDAI and the upper gastrointestinal symptoms index can provide accurate clinical diagnosis of SLE with gastric involvement.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning.

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Development, Validation, and Comparison of 2 Ultrasound Feature-Guided Machine Learning Models to Distinguish Cervical Lymphadenopathy.

ABSTRACT: The objective of this study is to develop and validate the performance of 2 ultrasound (US) feature-guided machine learning models in distinguishing cervical lymphadenopathy. We enrolled 705 patients whose US characteristics of lymph nodes were collected at our hospital. B-mode US and color Doppler US features of cervical lymph nodes in both cohorts were analyzed by 2 radiologists. The decision tree and back propagation (BP) neural network were developed by combining clinical data (age, sex, and history of tumor) and US features. The performance of the 2 models was evaluated by calculating the area under the receiver operating characteristics curve (AUC), accuracy value, precision value, recall value, and balanced F score (F1 score). The AUC of the decision tree and BP model in the modeling cohort were 0.796 (0.757, 0.835) and 0.854 (0.756, 0.952), respectively. The AUC, accuracy value, precision value, recall value, and F1 score of the decision tree in the validation cohort were all higher than those of the BP model: 0.817 (0.786, 0.848) vs 0.674 (0.601, 0.747), 0.774 (0.737, 0.811) vs 0.702 (0.629, 0.775), 0.786 (0.739, 0.833) vs 0.644 (0.568, 0.720), 0.733 (0.694, 0.772) vs 0.630 (0.542, 0.718), and 0.750 (0.705, 0.795) vs 0.627 (0.541, 0.713), respectively. The US feature-guided decision tree model was more efficient in the diagnosis of cervical lymphadenopathy than the BP model.

Ultrasound combined with Ki-67 to construct the prognostic model for radioactive iodine therapy outcomes in Graves' disease patients.

The aim of this study was to develop a prognostic model for radioactive iodine (RAI) therapy outcome in patients with Graves' disease. We enrolled 127 patients. Information on RAI therapy, ultrasound indexes of thyroid, and other lifestyle factors was collected. The competing risk model was used to estimate the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for nonhealing or recurrence of hyperthyroidism (NHRH). The performance of the model was assessed by receiver operator characteristic analysis and the Brier score and internally validated by bootstrap resampling. Then, a nomogram was developed. Forty-one cases (32.2%) of NHRH were documented. Positive Ki-67 expression, a higher dose of per-unit thyroid volume, and females showed lower risks of NHRH (all P < 0.05). The HR values (95% CI) were 0.42 (0.23, 0.79), 0.01 (0.00, 0.02), and 0.47 (0.25, 0.89), respectively. The bootstrap validation showed that the model had the highest accuracy and good calibration for predicting cumulative risk of NHRH at 180 days after RAI therapy (AUC = 0.772; 95% CI: 0.640-0.889, Brier score = 0.153). By decision curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.20 and 0.40. Ki-67, ultrasound volumetry, and scintigraphy techniques can play important roles in evaluating RAI therapy outcome in Graves' disease patients. The prediction nomogram shows reasonable accuracy in predicting NHRH.

Prediction of papillary thyroid metastases to the central compartment: proposal of a model taking into consideration other thyroid conditions.

Objective: To construct risk prediction models for cervical lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) under different thyroid disease backgrounds and to analyze and compare risk factors among different groups. Methods: This retrospective study included 518 patients with PTC that was pathologically confirmed post-operatively from January 2021 to November 2021. Demographic, ultrasound and pathological data were recorded. Univariate and multivariate logistic regression analyses were performed to identify factors associated with CLNM in the whole patient cohort and in patients grouped according to diagnoses of Hashimoto's thyroiditis (HT), nodular goiter (NG), and no background disease. Prediction models were constructed for each group, and their performances were compared. Results: Analysis of the whole PTC patient cohort identified NG as independently associated with CLNM. The independent risk factors for patients with no background disease were the maximum thyroid nodule diameter and American College of Radiology Thyroid Imaging Reporting & Data System score; those for patients with HT were the maximum thyroid nodule diameter, ACR TI-RADS score, and multifocality; and those for patients with NG were the maximum thyroid nodule diameter, ACR TI-RADS score, multifocality and gender. Conclusion: Background thyroid disease impacts CLNM in PTC patients, and risk factors for CLNM vary among PTC patients with different background diseases. Ultrasound is useful for diagnosing background thyroid disease, which can inform treatment planning. Different prediction models are recommended for PTC cases with different thyroid diseases.

Construction and Validation of a Predictive Nomogram Based on Ultrasound for Lymph Node Metastasis of Papillary Thyroid Carcinoma in the Cervical Central Region.

ABSTRACT: To establish and validate a nomogram for predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) in the cervical central region. This retrospective study included 287 PTC patients with 309 nodules treated from December 2018 to May 2020 at our hospital. The cohort was divided randomly into a training set and a testing set according to a 7:3 ratio. The training set contained 216 nodules, and the testing set contained 93 nodules. The nomogram was developed using the training set, and the data of the testing set were used to validate the performance of nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The study showed multifocality, thyroid lesion size, and American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) score were significantly independently associated with LNM in the cervical central region. In the testing set, the calibration curve showed that the nomogram had good discrimination with a C-index of 0.775 (95% confidence interval, 0.680-0.869) and adequate calibration ( P = 0.808). By decision curve analysis and clinical impact curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.40 and 0.75. The nomogram can be used for predicting LNM of PTC in the cervical central region and may provide valuable guidance for planning the surgical treatment of PTC patients.

Establishment and comparison of piecewise linear regression models to measure thyroid volume by 2D and 3D ultrasound.

OBJECTIVE: Compared thyroid volumes measured by 2-D and 3-D US with those of resected specimens and proposed new models to improve measurement accuracy. METHODS: This study included 80 patients who underwent total thyroidectomy. One 2D_model and one 3D_model were developed using piecewise linear regression analysis. The accuracy of these models was compared using an ellipsoid model (2-D_US value × 0.5), 3-D_US value, and Ying's model [1.76 + (2-D_US value × 0.38)]. RESULTS: The new 2D_model was: V=2.66 + (0.71 * X1) - (1.51 * X2). In this model, if 2-D_US value <= 228.39, X1 = 2-D_US value and X2 = 0; otherwise, X1 = 2-D_US value and X2 = 2-D_US value - 228.39. The 3D_model was: V= 2.90 + (1.08 * X1) + (2.43 * X2). In this model, if 3-D_US value <= 102.06, X1 = 3-D_US value and X2 = 0; otherwise, X1 = 3-D_US value and X2 = 3-D_US value - 102.06. The accuracy of the new models was higher than that of the 3-D_US value, the ellipsoid model, and Ying's model (P<0.05). CONCLUSION: The models established are more accurate than the traditional ones and can accurately measure thyroid volume.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer.

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer.

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

Calcification, Posterior Acoustic, and Blood Flow: Ultrasonic Characteristics of Triple-Negative Breast Cancer.

Previous studies suggest that triple-negative breast cancer (TNBC) may have unique imaging characteristics, however, studies focused on the imaging characteristics of TNBC are still limited. The aim of the present study is to analyze the ultrasonic characteristics of TNBC and to provide more reliable information on imaging diagnosis of TNBC. This retrospective study was performed including 162 TNBC patients with 184 TNBC lesions. 174 non-TNBC cases with 196 lesions were used as the control group. The median size of TNBC lesions and non-TNBC lesions were 23 mm × 16 mm and 21 mm × 15 mm, respectively. The shape of most breast cancer lesions was irregular. However, 15.30% (28/183) TNBC lesions and 16.84% (33/196) non-TNBC lesions were oval-shaped. Most breast cancer lesions (79.78% TNBC & 85.71% non-TNBC) were ill-defined. In comparison to non-TNBC, the distinctive ultrasonic characteristics of TNBC were summarized as three features: calcifications, posterior acoustic, and blood flow. Microcalcifications was less common in non-TNBC. The remarkable posterior acoustic characteristics on TNBC were no posterior acoustic features (136, 73.91%). Avascular pattern (21.74%) was also more common in TNBC. The other feature of TNBC was markedly hypoechoic lesions (23.91%). The above-mentioned differences between TNBC and non-TNBC were significant. 93.48% TBNC and 94.39% non-TNBC lesions were in BI-RADS-US category of 4A-5. The results indicate that TNBC has some distinctive ultrasound characteristics. Ultrasound is a useful adjunct in early detection of breast cancer. A combination of ultrasound with mammography is excellent for detecting breast cancer.

Circulating microRNA: Myocardium-derived prenatal biomarker of ventricular septal defects.

Background: Recently, circulating microRNAs (miRNAs) from maternal blood and amniotic fluid have been used as biomarkers for ventricular septal defect (VSD) diagnosis. However, whether circulating miRNAs are associated with fetal myocardium remains unknown. Methods: Dimethadione (DMO) induced a VSD rat model. The miRNA expression profiles of the myocardium, amniotic fluid and maternal serum were analyzed. Differentially expressed microRNAs (DE-microRNAs) were verified by qRT-PCR. The target gene of miR-1-3p was confirmed by dual luciferase reporter assays. Expression of amniotic fluid-derived DE-microRNAs was verified in clinical samples. Results: MiRNAs were differentially expressed in VSD fetal rats and might be involved in cardiomyocyte differentiation and apoptosis. MiR-1-3p, miR-1b and miR-293-5p were downregulated in the myocardium and upregulated in amniotic fluid/maternal serum. The expression of amniotic fluid-derived DE-microRNAs (miR-1-3p, miR-206 and miR-184) was verified in clinical samples. Dual luciferase reporter assays confirmed that miR-1-3p directly targeted SLC8A1/NCX1. Conclusion: MiR-1-3p, miR-1b and miR-293-5p are downregulated in VSD myocardium and upregulated in circulation and may be released into circulation by cardiomyocytes. MiR-1-3p targets SLC8A1/NCX1 and participates in myocardial apoptosis. MiR-1-3p upregulation in circulation is a direct and powerful indicator of fetal VSD and is expected to serve as a prenatal VSD diagnostic marker.

[Establishment of a predictive model for central cervical lymph node metastasis of papillary thyroid carcinoma based on ACR TI-RADS score and evaluation of its diagnostic efficacy].

Objective:To establish a predictive model for central lymph node metastasis（CLNM） of papillary thyroid carcinoma（PTC） based on ACR TI-RADS grades（ATR model） and evaluate its diagnostic efficacy. Methods:A total of 319 patients with PTC diagnosed from January 2019 to May 2020 were included, including 366 nodules were used as the modeling cohort to construct the risk prediction model. A total of 105 PTC patients with 121 nodules from June to August 2020 were included as the external validation cohort. The C-index of the model was calculated and the Hosmer-Lemeshow goodness-of-fit test was performed to compare the diagnostic efficiency of ACR model and those conventional imaging models. Results:The ATR model, Y=-3.719+0.765×gender+1.094×multifocality+0.08×maximum diameter+0.266×ACR TI-RADS score. In the training set, validation set and external validation cohort, the model C-index was 0.758（95%CI: 0.699-0.817）, 0.717（95%CI: 0.619-0.815） and 0.756（95%CI: 0.671-0.840）, respectively. The Hosmer-Lemeshow goodness of fit test showed that the prediction rate of the model was consistent with the actual incidence rate（P=0.918; P=0.581; P=0.366）. With ≥0.434 as the diagnostic threshold, the model had the highest diagnostic efficacy （sensitivity: 86.0%, specificity: 56.3%, Youden index: 0.423）. In the external validation cohort, there was no significant difference between C-US and CT（P>0.05）. Compared with C-US and CT, the sensitivity（66.1% vs 16.1%, P<0.001; 66.1% vs 9.7%, P<0.001） and accuracy（68.6% vs 55.4%, P=0.041; 68.6% vs 52.9%, P=0.012） of ATR model were higher, and the negative predictive value was higher than that of CT（66.7% vs 50.9%, P=0.042）, but there was no difference between ATR model and C-US（66.7% vs 52.3%, P=0.066）; There was no significant difference among the three positive predictive values（70.7% vs 83.3%, P=0.211; 70.7% vs 85.7%, P=0.319）, but the specificity of the model was low （71.2% vs 96.6%, P=0.001; 71.2% vs 98.3%, P<0.001）. Conclusion:The predictive model based on ACR TI-RADS grades can predict CLNM of PTC more accurately and sensitively than traditional imaging examination.

Applications of Mechanical Arm in the Virtual Touch Tissue Imaging Quantification and the Differential Diagnosis of Breast Tumors.

ABSTRACT: To assess the diagnostic performance of quantitative parameters for virtual touch tissue imaging quantification by the mechanical arm probe, 128 women with 152 breast lesions (90 benign and 62 malignant) were included and maximum elasticity (Vmax), median elasticity (Vmean), maximum elasticity minus minimum (Vmax-min), and minimum elasticity (Vmin) were measured. Intercorrelation coefficients and Bland-Altman plots were used to compare the repeatability and consistency of handheld probe and mechanical arm measurements. Receiver operating characteristic curve was used to evaluate the diagnostic efficiency. The results showed that intercorrelation coefficients were 0.694 to 0.951 by the mechanical arm and 95% limits of agreement were wider than handheld probe. Using 4.36 m/s as cutoff value for Vmax by the mechanical arm, we achieved sensitivity (88.7%) and specificity (90.0%). We concluded that the mechanical arm can reduce the influence of different scanning pressures on the elastic modulus of breast tissue.

Development of an Ultrasound Prediction Model to Discriminate between Malignant and Benign Liver Lesions.

To discriminate between malignant and benign liver lesions, we evaluated the ultrasound features of the target lesions in 266 patients and established a prediction model using a logistic regression algorithm. The prediction model based on independent factors was expressed as predictive score = 1.129 × interaction of irregular shape and unclear boundary + 1.398 × occupying effect + 2.363 × hypo-echoic halo + 1.987 × marginal vascular sign + 3.627 × cirrhosis background + 2.976 × nodule in nodule sign + 3.690 × metastasis sign. Receiver operating characteristic curve analysis revealed that the optimal cutoff predictive score was 2.8 (area under the curve = 0.942). The specificity of the prediction model was not significantly different from that of computed tomography/magnetic resonance imaging (91.7% vs. 98.8%, p = 0.077), whereas the prediction model had a lower sensitivity (90.1% vs. 97.8%, p < 0.001) and accuracy (90.6% vs. 98.1%, p < 0.001) than computed tomography/magnetic resonance imaging. We conclude that the ultrasound prediction model exhibited good diagnostic performance in discriminating malignant from benign liver lesions.

Use of Lung Ultrasound to Differentiate Coronavirus Disease 2019 (COVID-19) Pneumonia From Community-Acquired Pneumonia.

To investigate the feasibility of lung ultrasound in evaluating coronavirus disease 2019 (COVID-19) and distinguish the sonographic features between COVID-19 and community-acquired pneumonia (CAP), a total of 12 COVID-19 patients and 20 CAP patients were selected and underwent lung ultrasound. The modified Buda scoring system for interstitial lung disease was used to evaluate the severity and treatment effect of COVID-19 on ultrasonography. The differences between modified lung ultrasound (MLUS) score and high-resolution computed tomography (HRCT) Warrick score were analyzed to evaluate their correlation. COVID-19 showed the following sonographic features: thickening (12/12), blurred (9/12), discontinuous (6/12) pleural line; rocket sign (4/12), partially diffused B-line (12/12), completely diffused B-line (10/12), waterfall sign (4/12); C-line sign (5/12); pleural effusion (1/12) and pulmonary balloon (Am line, 1/12). The last two features were rarely seen. Differences of ultrasonic features, including lesion range, lung signs and pneumonia-related complications, between COVID-19 and CAP were statistically significant (p˂ 0.05 or 0.001). MLUS scores (p = 0.006) and HRCT Warrick scores (p = 0.015) increased as the severity of COVID-19 increased. The differences between moderate (29.00 [25.75-37.50]) and severe (43.00 [38.75-47.25]) (p = 0.022) or between moderate and critical (47.50 [44.25-50.00]) (p = 0.002) type COVID-19 were statistically significant, compared with those between severe and critical types. Correlation between MLUS scores and HRCT Warrick scores was positive (r = 0.54, p = 0.048). MLUS scores (Z = 2.61, p = 0.009) and HRCT Warrick scores (Z = 2.63, p = 0.009) of five severe or critical COVID-19 patients significantly decreased as their conditions improved after treatment. The differences of sonographic features between COVID-19 and CAP patients were notable. The MLUS scoring system could be used to evaluate the severity and treatment effect of COVID-19.