Ultrasound-based Radiomics Predicts Short-term Outcomes in Hepatitis B Virus-related Acute-on-chronic Liver Failure.

BACKGROUND: The prognosis in hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is challenging due to heterogeneity. Radiomics may enable noninvasive outcome prediction. OBJECTIVE: This study aimed to evaluate ultrasound-based radiomics for predicting outcomes in HBV-ACLF. METHODS: We enrolled 264 HBV-ACLF patients, dividing them into a training cohort (n=184) and a validation cohort (n=80). From hepatic ultrasound images, 455 radiomic features were extracted. Radiomics-based phenotypes were identified through unsupervised hierarchical clustering. A radiomic signature was developed using a Cox-LASSO algorithm to predict 30-day mortality. Furthermore, we integrated the signature with independent clinical predictors via multivariate Cox regression to construct a combined clinical-radiomic nomogram (CCR-nomogram). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) assessed performance improvements achieved by adding radiomic features to clinical data. RESULTS: Both clustering and radiomic signature identified two distinct subgroups with significant differences in clinical characteristics and 30-day prognosis. In the training cohort, the signature achieved a C-index of 0.746, replicated in validation with a C-index of 0.747. The CCR-nomogram achieved C-indices of 0.834 and 0.819 for the training and validation cohorts. Incorporating radiomic features significantly improved the CCRnomogram over the signature and clinical-only models, evidenced by IDI of 0.108-0.264 and NRI of 0.292-0.540 in both cohorts (all p0.05). CONCLUSION: Ultrasound-based radiomics offered prognostic information complementary to clinical data and demonstrated potential to enhance outcome prediction in HBV-ACLF.

Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis.

PURPOSE: This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis. METHODS: We prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1-year follow-up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet-50 to analyze 2D SWE images, a SWE-based deep learning signature (DLswe) was developed for 1-year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts. RESULTS: The C-index for DLswe was 0.748 (95% CI 0.666-0.829) and 0.744 (95% CI 0.623-0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C-index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763-0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707-0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable. CONCLUSIONS: The 2D SWE-based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.

Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3-5 nodule classification among radiologists: a multiple center study.

Background: Significant differences exist in the classification outcomes for radiologists using ultrasonography-based Breast Imaging Reporting and Data Systems for diagnosing category 3-5 (BI-RADS 3-5) breast nodules, due to a lack of clear and distinguishing image features. Consequently, this retrospective study investigated the improvement of BI-RADS 3-5 classification consistency using a transformer-based computer-aided diagnosis (CAD) model. Methods: Independently, 5 radiologists performed BI-RADS annotations on 21,332 breast ultrasonographic images collected from 3,978 female patients from 20 clinical centers in China. All images were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity (SEN), specificity (SPE), accuracy (ACC), area under the curve (AUC), and calibration curve were evaluated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS classification results for the sampling test set provided by CAD to determine whether classification consistency (the k value), SEN, SPE, and ACC could be improved. Results: After the training set (11,238 images) and validation set (2,996 images) were learned by the CAD model, the classification ACC of the CAD model applied to the test set (7,098 images) was 94.89% in category 3, 96.90% in category 4A, 95.49% in category 4B, 92.28% in category 4C, and 95.45% in category 5 nodules. Based on pathological results, the AUC of the CAD model was 0.924 and the predicted probability of CAD was a little higher than the actual probability in the calibration curve. After referencing BI-RADS classification results, the adjustments were made to 1,583 nodules, of which 905 were classified to a lower category and 678 to a higher category in the sampling test set. As a result, the ACC (72.41-82.65%), SEN (32.73-56.98%), and SPE (82.46-89.26%) of the classification by each radiologist were significantly improved on average, with the consistency (k values) in almost all of them increasing to >0.6. Conclusions: The radiologist's classification consistency was markedly improved with almost all the k values increasing by a value greater than 0.6, and the diagnostic efficiency was also improved by approximately 24% (32.73% to 56.98%) and 7% (82.46% to 89.26%) for SEN and SPE, respectively, of the total classification on average. The transformer-based CAD model can help to improve the radiologist's diagnostic efficacy and consistency with others in the classification of BI-RADS 3-5 nodules.

Multiregional radiomic model for breast cancer diagnosis: value of ultrasound-based peritumoral and parenchymal radiomics.

Background: Breast cancer consists not only of neoplastic cells but also of significant changes in the surrounding and parenchymal stroma, which can be reflected in radiomics. This study aimed to perform breast lesion classification through an ultrasound-based multiregional (intratumoral, peritumoral, and parenchymal) radiomic model. Methods: We retrospectively reviewed ultrasound images of breast lesions from institution #1 (n=485) and institution #2 (n=106). Radiomic features were extracted from different regions (intratumoral, peritumoral, and ipsilateral breast parenchymal) and selected to train the random forest classifier with the training cohort (n=339, a subset of the institution #1 dataset). Then, the intratumoral, peritumoral, and parenchymal, intratumoral & peritumoral (In&Peri), intratumoral & parenchymal (In&P), and intratumoral & peritumoral & parenchymal (In&Peri&P) models were developed and validated on the internal (n=146, another subset of institution 1) and external (n=106, institution #2 dataset) test cohorts. Discrimination was evaluated using the area under the curve (AUC). Calibration curve and Hosmer-Lemeshow test assessed calibration. Integrated discrimination improvement (IDI) was used to assess performance improvement. Results: The performance of the In&Peri (AUC values 0.892 and 0.866), In&P (0.866 and 0.863), and In&Peri&P (0.929 and 0.911) models was significantly better than that of the intratumoral model (0.849 and 0.838) in the internal and external test cohorts (IDI test, all P<0.05). The intratumoral, In&Peri and In&Peri&P models showed good calibration (Hosmer-Lemeshow test, all P>0.05). The multiregional (In&Peri&P) model had the highest discrimination among the 6 radiomic models in the test cohorts, respectively. Conclusions: The multiregional model combining radiomic information of intratumoral, peritumoral, and ipsilateral parenchymal regions yielded better performance than the intratumoral model in distinguishing malignant breast lesions from benign lesions.

Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter study.

Background: The presence of gross extrathyroidal extension (ETE) in thyroid cancer will affect the prognosis of patients, but imaging examination cannot provide a reliable diagnosis for it. This study was conducted to develop a deep learning (DL) model for localization and evaluation of thyroid cancer nodules in ultrasound images before surgery for the presence of gross ETE. Methods: From January 2016 to December 2021 grayscale ultrasound images of 806 thyroid cancer nodules (4451 images) from 4 medical centers were retrospectively analyzed, including 517 no gross ETE nodules and 289 gross ETE nodules. 283 no gross ETE nodules and 158 gross ETE nodules were randomly selected from the internal dataset to form a training set and validation set (2914 images), and a multitask DL model was constructed for diagnosing gross ETE. In addition, the clinical model and the clinical and DL combined model were constructed. In the internal test set [974 images (139 no gross ETE nodules and 83 gross ETE nodules)] and the external test set [563 images (95 no gross ETE nodules and 48 gross ETE nodules)], the diagnostic performance of DL model was verified based on the pathological results. And then, compared the results with the diagnosis by 2 senior and 2 junior radiologists. Findings: In the internal test set, DL model demonstrated the highest AUC (0.91; 95% CI: 0.87, 0.96), which was significantly higher than that of two senior radiologists [(AUC, 0.78; 95% CI: 0.71, 0.85; P < 0.001) and (AUC, 0.76; 95% CI: 0.70, 0.83; P < 0.001)] and two juniors radiologists [(AUC, 0.65; 95% CI: 0.58, 0.73; P < 0.001) and (AUC, 0.69; 95% CI: 0.62, 0.77; P < 0.001)]. DL model was significantly higher than clinical model [(AUC, 0.84; 95% CI: 0.79, 0.89; P = 0.019)], but there was no significant difference between DL model and clinical and DL combined model [(AUC, 0.94; 95% CI: 0.91, 0.97; P = 0.143)]. In the external test set, DL model also demonstrated the highest AUC (0.88, 95% CI: 0.81, 0.94), which was significantly higher than that of one of senior radiologists [(AUC, 0.75; 95% CI: 0.66, 0.84; P = 0.008) and (AUC, 0.81; 95% CI: 0.72, 0.89; P = 0.152)] and two junior radiologists [(AUC, 0.72; 95% CI: 0.62, 0.81; P = 0.002) and (AUC, 0.67; 95 CI: 0.57, 0.77; P < 0.001]. There was no significant difference between DL model and clinical model [(AUC, 0.85; 95% CI: 0.79, 0.91; P = 0.516)] and clinical + DL model [(AUC, 0.92; 95% CI: 0.87, 0.96; P = 0.093)]. Using DL model, the diagnostic ability of two junior radiologists was significantly improved. Interpretation: The DL model based on ultrasound imaging is a simple and helpful tool for preoperative diagnosis of gross ETE thyroid cancer, and its diagnostic performance is equivalent to or even better than that of senior radiologists. Funding: Jiangxi Provincial Natural Science Foundation (20224BAB216079), the Key Research and Development Program of Jiangxi Province (20181BBG70031), and the Interdisciplinary Innovation Fund of Natural Science, Nanchang University (9167-28220007-YB2110).

Transcriptomic analysis reveals the inhibition mechanism of pulsed light on fungal growth and ochratoxin A biosynthesis in Aspergillus carbonarius.

Pulsed light (PL) technology has a good effect on the control of fungi in postharvest fruit. In this present work, PL inhibited the growth of Aspergillus carbonarius in a dose-dependent manner, the mycelial growth decreased by 4.83 %, 13.91 % and 30.01 % at a fluence of 4.5 J·cm-2 (PL5), 9 J·cm-2 (PL10) and 13.5 J·cm2 (PL15), respectively. When inoculated with PL15 treated A. carbonarius, the scab diameter of the pears, ergosterol and OTA content was reduced by 23.2 %, 27.9 % and 80.7 % after 7 days, respectively. The third-generation sequencing technique was applied to study the transcriptome response of A. carbonarius treated with PL. Compared with the blank control, a total number of 268 and 963 differentially expressed genes (DEGs) were discovered in the group of PL10 and PL15, respectively. To be specific, a large amount of DEGs involved in DNA metabolism were up-regulated, while most of DEGs related to cell integrity, energy and glucose metabolism, ochratoxin A (OTA) biosynthesis and transport were down-regulated. In addition, the stress response of A. carbonarius was imbalanced, including up-regulation of Catalase and PEX12 and down-regulation of taurine and subtaurine metabolism, alcohol dehydrogenase and glutathione metabolism. Meanwhile, the results of transmission electron microscopy, mycelium cellular leakage and DNA electrophoresis indicated that PL15 treatment caused mitochondrial swelling, the destroyed cell membrane permeability and imbalance of DNA metabolism. The expression of P450 and Hal involved in OTA biosynthesis pathway were down-regulated in PL treated samples detected by qRT-PCR. In conclusion, this study reveals the molecular mechanism of pulsed light on inhibiting the growth, development and toxin production of A. carbonarius.

The value of contrast-enhanced ultrasound in the diagnosis of BI-RADS-US 4a lesions less than 2 cm in diameter.

BACKGROUND: Breast cancer is the most common malignant tumor in women. Early diagnosis of benign and malignant breast tumors is of great significance. OBJECTIVE: To retrospectively analyze the value of contrast-enhanced ultrasonography (CEUS) in the diagnosis of Breast Imaging-Reporting and Data System (BI-RADS) 4a breast lesions less than 2 cm in diameter. METHODS: CEUS was performed for 143 breast masses less than 2 cm in diameter that were diagnosed as BI-RADS 4a by ultrasound and reclassified. Considering pathological diagnosis as the gold standard, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of reclassified lesions after CEUS for the diagnosis of benign and malignant masses were analyzed. RESULTS: BI-RADS 4a breast masses with a diameter less than 2 cm (n = 143) were confirmed by pathology; 103 and 40 were classified as benign and malignant, respectively. The sensitivity, specificity, PPV, and NPV of CEUS for the diagnosis were 90%, 86%, 72%, and 95%, respectively. The area under the receiver operating characteristic (ROC) curve of CEUS for the diagnosis of benign and malignant tumors after CEUS was 0.904. CONCLUSION: CEUS can help to improve the diagnostic accuracy of BI-RADS 4a masses with a diameter less than 2 cm.

Carotid contrast-enhanced ultrasonography combined with sirtuin-3 in the diagnosis of plaques in carotid atherosclerosis.

BACKGROUND: Carotid atherosclerosis (CAS) is one of the main causes of ischemic stroke. Currently, the clinical evidence for contrast-enhanced ultrasonography (CEUS) as a method for diagnosing CAS is still inadequate. Sirtuin-3 (SIRT3) is associated with the inflammation response; however, few studies have evaluated SIRT3 in CAS. OBJECTIVES: To investigate the role of SIRT3 in CAS patients and its diagnostic value for unstable plaques when combined with CEUS. MATERIAL AND METHODS: This is a prospective observational study including 517 CAS patients who were admitted to our hospital from January 2015 to December 2020. All patients received a normal Doppler ultrasound, CEUS and magnetic resonance imaging (MRI). The latter was used as the gold standard in evaluating plaque conditions. Serum SIRT3 levels were measured using an enzyme-linked immunosorbent assay (ELISA). Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-ch), low-density lipoprotein cholesterol (LDL-ch), C-reactive protein (CRP), and interleukin (IL)-6 levels were measured and recorded. RESULTS: Patients with severe CAS showed significantly higher levels of CRP, IL-6, TC, and LDL-ch, a higher frequency of unstable plaques, as well as a lower level of HDL-ch. In patients with severe CAS and CAS patients with stable plaques, the levels of SIRT3 were markedly lower. Patients with a high expression of SIRT3 showed significantly lower levels of CRP, IL-6, TC and LDL-ch, and higher levels of HDL-ch, as well as a lower frequency of unstable plaques. Receiver operating characteristic (ROC) curves showed that the combination of CEUS and SIRT3 could achieve high sensitivity and specificity in the diagnosis of unstable plaques. High levels of C-reactive protein, IL-6, TC, TG and LDL-ch, as well as low levels of SIRT3 and HDL-ch, and current smoking were risk factors of unstable plaques in CAS patients. CONCLUSIONS: A low expression of SIRT3 predicted a higher risk for unstable plaques in CAS patients. The combination of CEUS and SIRT3 is a potential strategy for diagnosing unstable plaques.

Artificial Neural Network-Based Ultrasound Radiomics Can Predict Large-Volume Lymph Node Metastasis in Clinical N0 Papillary Thyroid Carcinoma Patients.

Objective: To evaluate the ability of artificial neural network- (ANN-) based ultrasound radiomics to predict large-volume lymph node metastasis (LNM) preoperatively in clinical N0 disease (cN0) papillary thyroid carcinoma (PTC) patients. Methods: From January 2020 to April 2021, 306 cN0 PTC patients admitted to our hospital were retrospectively reviewed and divided into a training (n = 183) cohort and a validation cohort (n = 123) in a 6 : 4 ratio. Radiomic features quantitatively extracted from ultrasound images were pruned to train one ANN-based radiomic model and three conventional machine learning-based classifiers in the training cohort. Furthermore, an integrated model using ANN was constructed for better prediction. Meanwhile, the prediction of the two models was evaluated in the papillary thyroid microcarcinoma (PTMC) and conventional papillary thyroid cancer (CPTC) subgroups. Results: The radiomic model showed better discrimination than other classifiers for large-volume LNM in the validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.856 and an area under the precision-recall curve (AUPR) of 0.381. The performance of the integrated model was better, with an AUROC of 0.910 and an AUPR of 0.463. According to the calibration curve and decision curve analysis, the radiomic and integrated models had good calibration and clinical usefulness. Moreover, the models had good predictive performance in the PTMC and CPTC subgroups. Conclusion: ANN-based ultrasound radiomics could be a potential tool to predict large-volume LNM preoperatively in cN0 PTC patients.

A model based on two-dimensional shear wave elastography for acute-on-chronic liver failure development in patients with acutely decompensated hepatitis B cirrhosis.

Background: To evaluate the accuracy of two-dimensional (2D) shear wave elastography (SWE), develop and validate a novel prognostic model in predicting acute-on-chronic liver failure (ACLF) development in patients with acutely decompensated hepatitis B cirrhosis. Methods: This prospective cohort study enrolled 221 patients in the First Affiliated Hospital of Nanchang University from September 2019 to January 2021, and randomly assigned them to the derivation and validation cohorts (7:3 ratio). Ultrasound, 2D SWE, clinical and laboratory data were collected, and outcome (ACLF developed) was recorded during a 90-day follow-up period. We evaluated the ability of 2D SWE to predict the outcome, developed a model for predicting ACLF development in the derivation cohort, and assessed the model in the validation cohort. Results: 2D SWE values were significantly higher in patients with ACLF development (P<0.05). The accuracy of 2D SWE in predicting the outcome was better than that of serum parameters of liver fibrosis (all P<0.05). The SWE model for ACLF development had good calibration and discrimination [concordance index (C-index): 0.855 and 0.840 respectively] in derivation and validation cohorts, outperforming serum prognostic scores (all P<0.05). Conclusions: The SWE model, superior to serum prognostic scores in predicting ACLF development, could be a noninvasive tool to guide the individual management of patients with acutely decompensated hepatitis B cirrhosis.

Nomogram Combining Radiomics With the American College of Radiology Thyroid Imaging Reporting and Data System Can Improve Predictive Performance for Malignant Thyroid Nodules.

PURPOSE: To develop and validate a nomogram combining radiomics of B-mode ultrasound (BMUS) images and the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) for predicting malignant thyroid nodules and improving the performance of the guideline. METHOD: A total of 451 thyroid nodules referred for surgery and proven pathologically at an academic referral center from January 2019 to September 2020 were retrospectively collected and randomly assigned to training and validation cohorts (7:3 ratio). A nomogram was developed through combining the BMUS radiomics score (Rad-Score) with ACR TI-RADS score (ACR-Score) in the training cohort; the performance of the nomogram was assessed with respect to discrimination, calibration, and clinical application in the validation and entire cohorts. RESULTS: The ACR-Rad nomogram showed good calibration and yielded an AUC of 0.877 (95% CI 0.836-0.919) in the training cohort and 0.864 (95% CI 0.799-0.931) in the validation cohort, which were significantly better than the ACR-Score model (p < 0.001 and 0.031, respectively). The significantly improved AUC, net reclassification index (NRI), and integrated discriminatory improvement (IDI) of the nomogram were found for both senior and junior radiologists (all p < 0.001). Decision curve analysis indicated that the nomogram was clinically useful. When cutoff values for 50% predicted malignancy risk (ACR-Rad_50%) were applied, the nomogram showed increased specificity, accuracy and positive predictive value (PPV), and decreased unnecessary fine-needle aspiration (FNA) rates in comparison to ACR TI-RADS. CONCLUSION: The ACR-Rad nomogram has favorable value in predicting malignant thyroid nodules and improving performance of the ACR TI-RADS for senior and junior radiologists.

Explore the Diagnostic Efficiency of Chinese Thyroid Imaging Reporting and Data Systems by Comparing With the Other Four Systems (ACR TI-RADS, Kwak-TIRADS, KSThR-TIRADS, and EU-TIRADS): A Single-Center Study.

Purpose: To explore the characteristics of C-TIRADS by comparing it with ACR-TIRADS, Kwak-TIRADS, KSThR-TIRADS and EU-TIRADS. Methods: A total of 1096 nodules were collected from 884 patients undergoing thyroidectomy in our center between May 2018 and December 2020. Divided the nodules into two groups: ">10mm" and "≤10mm". Ultrasound characteristics of each nodule were observed and recorded by 2 doctors, then classified based on ACR-TIRADS, Kwak-TIRADS, KSThR-TIRADS, EU-TIRADS, and C-TIRADS. Results: A total of 682 benign nodules cases (62.23%) and 414 malignant nodules cases (37.77%) were identified. The ICC value of each guideline was:0.937(ACR-TIRADS), 0.858(EU-IRADS), 0.811(Kwak-TIRADS), 0.835(KTA/KSThR-TIRADS) and 0.854(C-TIRADS). The nodule malignancy rates in the groups(Kwak-TIRADS 4B, C-TIRADS 4B、4C) of two sizes were significantly different (all p<0.05). There was no statistical difference in the other grades of two sizes (all p>0.05). Unnecessary biopsy rates were the lowest in C-TIRADS (49.02% p<0.001). Furthermore, Kwak-TIRADS had the highest sensitivity and NPV (89.9%, 91.0%, all p<0.05), while C-TIRADS had the highest specificity and PPV (82.3%, 69.2%, all p<0.05). C-TIRADS and Kwak-TIRADS had the highest accuracy (76.0%, 72.5%, P=0.071). The AUCs of the 5 guidelines were C-TIRADS(0.816, P<0.05), Kwak-TIRADS(0.789, P<0.05) KTA/KSThR-TIRADS and ACR-TIRADS(0.773, 0.763, P=0.305), EU-TIRADS(0.734, P<0.05). The AUCs of the five guidelines were not statistically different between "nodules>10mm" and "nodules ≤ 10mm" (all P>0.05). Conclusions: All five guides showed excellent interobserver agreement. C-TIRADS was slightly efficient than Kwak-IRADS, KTA/KSThR-TIRADS and ACR-TIRADS, and had greater advantages than EU-TIRADS. The diagnostic abilities of the five guidelines for "nodules ≤ 10mm" were not inferior to that of "nodules> 10mm". C-TIRADS is simple and easy to implement and can provide effective thyroid tumor risk stratification for thyroid nodule diagnosis, especially in China.

Nomograms Combining Ultrasonic Features With Clinical and Pathological Features for Estimation of Delphian Lymph Node Metastasis Risk in Papillary Thyroid Carcinoma.

BACKGROUND: This work explores the clinical significance of Delphian lymph nodes (DLN) in thyroid papillary carcinoma (PTC). At the same time, a nomogram is constructed based on clinical, pathological, and ultrasonic (US) features to evaluate the possibility of DLN metastasis (DLNM) in PTC patients. This is the first study to predict DLNM using US characteristics. METHODS: A total of 485 patients, surgically diagnosed with PTC between February 2017 and June 2021, all of whom underwent thyroidectomy, were included in the study. Using the clinical, pathological, and US information of patients, the related factors of DLNM were retrospectively analyzed. The risk factors associated with DLNM were identified through univariate and multivariate analyses. According to clinical + pathology, clinical + US, and clinical + US + pathology, the predictive nomogram for DLNM was established and validated. RESULTS: Of the 485 patients with DLN, 98 (20.2%) exhibited DLNM. The DLNM positive group had higher positive rates of central lymph node metastasis (CLNM), lateral lymph node metastasis (LLNM), and T3b-T4b thyroid tumors than the negative rates. The number of CLNM and LLNM lymph nodes in the DLNM+ group was higher as compared to that in the DLNM- group. Multivariate analysis demonstrated that the common independent risk factors of the three prediction models were male, bilaterality, and located in the isthmus. Age ≥45 years, located in the lower pole, and nodural goiter were protective factors. In addition, the independent risk factors were classified as follows: (I) P-extrathyroidal extension (ETE) and CLNM based on clinical + pathological characteristics; (II) US-ETE and US-CLNM based on clinical + US characteristics; and (III) US-ETE and CLNM based on clinical +US + pathological features. Better diagnostic efficacy was reported with clinical + pathology + US diagnostic model than that of clinical + pathology diagnostic model (AUC 0.872 vs. 0.821, p = 0.039). However, there was no significant difference between clinical + pathology + US diagnostic model and clinical + US diagnostic model (AUC 0.872 vs. 0.821, p = 0.724). CONCLUSIONS: This study found that DLNM may be a sign that PTC is more invasive and has extensive lymph node metastasis. By exploring the clinical, pathology, and US characteristics of PTC progression to DLNM, three prediction nomograms, established according to different combinations of features, can be used in different situations to evaluate the transfer risk of DLN.

Echocardiographic diagnosis of anomalous origin of the left coronary artery from the pulmonary artery.

The aim of this research is to investigate the application value of TTE in the diagnosis of the anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA).The echocardiographic findings of 11 patients with ALCAPA confirmed by surgery in our hospital from October 2007 to December 2018 were retrospectively analyzed and compared with the preoperative computed tomography angiography (CTA) diagnosis and intraoperative diagnosis.Surgery was performed in all of the patients to establish the dual coronary artery system. Four underwent the Takeuchi procedure and 7 had re-implantation of the anomalous left coronary artery. The CTA diagnoses of the 11 patients were consistent with the surgical diagnoses, and the diagnostic accuracy was 100% (11/11). Echocardiographic diagnosis showed consistent results in 10 cases, while one case was misdiagnosed as endocardial fibroelastosis; the diagnostic accuracy was 90.9% (10/11). The echocardiographic features of these patients with ALCAPA included: abnormal left coronary ostium arising from the pulmonary trunk with retrograde coronary artery flow in 10 patients; enlargement of the right coronary artery in 8 patients; abundant intercoronary septal collaterals in 6 patients; and moderate and significant mitral regurgitation in 7 patients. Echocardiography showed that the left ventricular end-diastolic diameter and left ventricular end-systolic diameter before surgery were significantly different from those after surgery (P < .05) and that the left ventricular ejection fraction and fractional shortening before surgery were not significantly different from those after surgery (P > .05).Transthoracic echocardiography can diagnose ALCAPA in a timely, accurate, and noninvasive manner, and it could be of great significance in guiding clinical operations and in predicting prognosis.

Diagnosis of infective endocarditis using echocardiography.

The aim of this study was to evaluate the application of transthoracic echocardiography for the diagnosis of infective endocarditis (IE) to provide a basis for the better treatment of IE. From October 2016 to October 2018, 87 consecutive patients with IE at our hospital were selected for this study. All the patients were subjected to transthoracic echocardiography. The morphology, structure, activity, and closure of the patients' heart valves were observed for vegetation identification, and the size, number, location, morphology, and echo intensity of vegetation, as well as degree of valve involvement, were determined.The 87 patients investigated in this study included 38 cases of congenital heart disease, 27 cases of nonrheumatic valvular heart disease, 12 patients who underwent valve surgery, 5 cases of rheumatic valvular heart disease, and 5 patients with no obvious signs of heart disease. The most common clinical manifestations were heart murmur in 80 cases and fever in 60 cases. The most common complications were heart failure in 35 cases, followed by organ embolism in 12 cases. There were 36 cases of positive blood cultures, including 26 cases of Gram-positive cocci and 10 cases of Gram-negative bacilli. Echocardiography showed aortic valve involvement in 37 cases, mitral valve involvement in 34 cases, tricuspid valve involvement in 10 cases, pulmonary valve involvement in 2 cases, and the involvement of an artificial valve in 5 cases. Twenty-six of these cases showed multiple valve involvement, and 20 patients exhibited serious complications. No significant differences were found between echocardiography and actual surgical observations with respect to their accuracy in detecting the size, number, and location of vegetation in the 69 patients who underwent surgery (P > .05). Echocardiography could detect the occurrence of severe complications, namely, the rupture of chordae tendineae, valve prolapse, valve perforation, and paravalvular abscess, and no significant difference in diagnostic accuracy was found between echocardiography and surgical observations (P > .05).Transthoracic echocardiography can rapidly and accurately detect IE vegetation and its complications and has important clinical value for guiding clinical treatment and determining prognosis.

Liver Stiffness Detected by Shear Wave Elastography Predicts Esophageal Varices in Cirrhotic Patients.

ABSTRACT: Ultrasound elastography has become a promising noninvasive approach for assessing liver fibrosis. The purpose of this study was to evaluate the diagnosis ability of liver stiffness detected by shear wave elastography (SWE) for predicting the presence of esophageal varices (EVs) in cirrhotic patients. Four hundred sixty-eight cirrhotic patients were enrolled consecutively. Liver stiffness and EVs were detected by SWE and endoscopy, respectively. The baseline characteristics were recorded, and areas under the receiver operating characteristic curves (AUROCs) were used to compare the diagnosis accuracy. Multivariate analysis was used to identify the risk factors for EVs in cirrhosis. The mean liver stiffness was 18.4 kPa with a range of 6.8 to 52.5 kPa. Two hundred seventy-one patients had no EVs (57.9%), 139 patients had F1 EVs (29.7%), and 58 patients had high-risk EVs (12.4%). The optimal cutoff values of SWE for predicting EVs and high-risk varices were 18.5 and 20.4 kPa, respectively. The AUROCs for predicting the incidence of EVs were 0.792 (95% confidence interval [CI], 0.884-0.842), 0.814 (95% CI, 0.658-0.875), and 0.895 (95% CI, 0.813-0.918) for platelet, platelet count-to-spleen diameter ratio, and liver stiffness, respectively. For predicting the presence of high-risk varices, liver stiffness again had the highest AUROC. Multivariate analysis identified liver stiffness and platelet count-to-spleen diameter ratio as independent predictive factors for EVs in cirrhosis. Liver stiffness measured by SWE is an effective diagnostic tool for predicting EVs with greater accuracy, and SWE value is an independent factor for predicting high-risk EVs.

Ultrasonography assessment of spermatic vein thrombosis.

There are few clinical problem of spermatic vein thrombosis were reported. Spontaneous spermatic vein thrombosis is a particularly rare diseases which can lead to acute or chronic testicular pain and testicular swelling. We report a case in which ultrasonography (US) discovered hypoechoic mass of left spermatic vein and contrast-enhanced ultrasonography (CEUS) revealed thrombus formation of left spermatic vein in a patient who had underwent a continuous driving for eight hours twenty days ago.

Contrast-Enhanced Ultrasound Differentiation Between Low- and High- Grade Bladder Urothelial Carcinoma and Correlation With Tumor Microvessel Density.

OBJECTIVES: Time-intensity curves (TICs) of contrast-enhanced ultrasound (CEUS) were analyzed retrospectively to differentiate between low-grade and high-grade bladder urothelial carcinoma, and to investigate correlation with tumor microvessel density (MVD). METHODS: The data of 105 patients with pathologically confirmed bladder urothelial carcinoma (55 low-grade and 50 high-grade) were reviewed. Lesions were examined before surgery using conventional ultrasound and CEUS with TIC analysis. The TIC parameters time from peak to one-half the signal intensity (TPH) and the corresponding descending slope (DS) of the low-grade and high-grade groups were compared, and receiver operating characteristic curves constructed. The MVDs of the resectioned tissue specimens were quantified via immunohistochemistry for CD34. RESULTS: Based on conventional ultrasound, the low-grade and high-grade groups were similar in tumor shape, number, topography, internal echo, height, width, and vascularity. The TPH of the high-grade group was significantly longer than that of the low-grade group, and the DS was lower. The cutoff points of TPH and DS for differentiating low-grade and high-grade bladder urothelial carcinoma were 48.06 seconds and 0.15 dB/seconds, respectively (area under the receiver operating characteristic curve = 0.79 for both). The mean MVDs per high-power field of the low-grade and high-grade groups were 41.39 16.65 and 51.03 20.16, respectively (P = .009). The TPH correlated linearly with MVD (P < .01), as did the DS (P < .01). CONCLUSIONS: Contrast-enhanced ultrasound can be used to differentiate low from high-grade bladder urothelial carcinoma. The TIC parameters of CEUS reflect the MVD of bladder urothelial tumors and may be helpful for evaluating tumor angiogenesis, with implications for clinical diagnosis, treatment, and prognosis.

Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images.

The objective of this study is to evaluate the diagnostic value of combination of artificial neural networks (ANN) and support vector machine (SVM)-based CAD systems in differentiating malignant from benign thyroid nodes with gray-scale ultrasound images. Two morphological and 65 texture features extracted from regions of interest in 610 2D-ultrasound thyroid node images from 543 patients (207 malignant, 403 benign) were used to develop the ANN and SVM models. Tenfold cross validation evaluated their performance; the best models showed accuracy of 99% for ANN and 100% for SVM. From 50 thyroid node ultrasound images from 45 prospectively enrolled patients, the ANN model showed sensitivity, specificity, positive and negative predictive values, Youden index, and accuracy of 88.24, 90.91, 83.33, 93.75, 79.14, and 90.00%, respectively, the SVM model 76.47, 90.91, 81.25, 88.24, 67.38, and 86.00%, respectively, and in combination 100.00, 87.88, 80.95, 100.00, 87.88, and 92.00%, respectively. Both ANN and SVM had high value in classifying thyroid nodes. In combination, the sensitivity increased but specificity decreased. This combination might provide a second opinion for radiologists dealing with difficult to diagnose thyroid node ultrasound images.

Diagnosis of mitral valve cleft using real-time 3-dimensional echocardiography.

BACKGROUND: Mitral valve cleft (MVC) is the most common cause of congenital mitral insufficiency, and MVC may occur alone or in association with other congenital heart lesions. Direct suture and valvuloplasty are the major and effective treatments for mitral regurgitation (MR) caused by MVC. Therefore, it is important to determine the location and magnitude of the pathological damage due to MVC when selecting a surgical procedure for treatment. This study explored the application value of transthoracic real-time 3-dimensional (3D) echocardiography (RT-3DE) in the diagnosis of MVC. METHODS: From October 2012 to June 2016, 19 consecutive patients with MVC diagnosed by 2-dimensional (2D) echocardiography in our hospital were selected for this study. Full-volume RT-3DE was performed on all patients. The 3D-imaging data were cropped and rotated in 3 views (horizontal, sagittal, and coronal) with 6 directions to observe the position and shape of the MVC and the spatial position between the cleft and its surrounding structures. The maximum longitudinal diameter and the maximum width of the cleft were measured. The origin of the mitral regurgitant jet and the severity of MR were evaluated, and these RT-3DE data were compared with the intraoperative findings. RESULTS: Of the 19 patients studied, 4 patients had isolated cleft mitral valve, and cleft mitral valves combined with other congenital heart lesions were detected in 15 patients. The clefts of 6 patients were located in the A2 segment, the clefts of 4 patients were located in the A1 segment, the clefts of 4 patients were located in the A3 segment, the clefts of 4 patients were located in the A2-A3 segment, and the cleft of 1 patient was located in the P2 segment. Regarding the shape of the cleft, 13 patients had V-shaped clefts, and the others had C- or S-shaped clefts. The severity of the MR at presentation was mild in 2 patients, moderate in 9 and severe in 8. Two of the patients with mild MR did not undergo surgery, while the remaining 17 patients did undergo surgery. Surgical treatment involved direct suture in 11 cases, reconstruction with ring annuloplasty in 3 cases and replacement in 3 cases. The diagnoses of MVC were confirmed by intraoperative findings. RT-3DE successfully captured full-volume 3D images of the 19 patients, which directly displayed the 3D structure of MVC with multiple views such as the position, shape, longitudinal diameter and width of the MVC, and the spatial position between the chordae tendineae surrounding the MVC and the aortic valve. The maximum longitudinal diameter of the valve leaflet cleft measured by RT-3DE and direct measurements during surgery were 12.02±2.12 and 13.01±2.45 mm, respectively, and the difference between these measurements was not statistically significant (P>0.05). Our results indicate that RT-3DE can provide more direct, accurate and abundant information. CONCLUSIONS: RT-3DE is a simple and fast imaging technique, and the detailed 3D images obtained can be used to confirm the diagnosis of MVC. RT-3DE is considered to be an important preoperative test that provides more comprehensive information for selecting a subsequent procedure for treatment.