Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma.

Objective: This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use. Results: The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models. Conclusion: The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.

Analysis of early response to chemotherapy for non-Hodgkin's lymphoma by quantitative contrast-enhanced ultrasound: A prospective case-control crossectional study.

OBJECTIVE: To investigate the value of quantitative contrast-enhanced ultrasonography (CEUS) in assessing and predicting early therapy response of non-Hodgkin's lymphoma (NHL). METHODS: Fifty-six cases of NHL were studied using CEUS before and after three cycles of R-CHOP / CHOP. Quantitative parameters such as arrival time (ATM), time to peak (TTP), △T = TTP-ATM, area under the gamma curve (Area), curve gradient (Grad), wash-out time (WT), base intensity (BI), peak intensity (PI) and ΔI = PI-BI were compared between the lymphoma and normal lymph nodes before and at mid-treatment, respectively. Changes in quantitative CEUS parameters were also compared between complete response (CR) and incomplete response(non-CR) groups. Besides, the correlation analysis was performed between pretreatment PI and changes in quantitative parameters. RESULTS: After three cycles of R-CHOP/CHOP, S/L (P < 0.001), PI (P = 0.002), ΔI (P < 0.001), Grad (P < 0.001), and Area (P < 0.001) of NHL were significantly decreased. The CR group and non-CR group only differed in ATM before treatment. In contrast, there was no statistical difference in any of the parameters between the two groups at mid-treatment. Finally, a significant correlation was observed between pre-treatment PI and PI△% (r = 0.736, P < 0.001). CONCLUSIONS: CEUS is promising for the assessment of response of NHL to R-CHOP/CHOP. Intra-lesion perfusion changes take precedence over morphological changes suggesting treatment efficacy. Pre-treatment ATM values may help to suggest efficacy outcomes and pre-treatment PI values may be a valid predictor of lymphoma perfusion response.

Self-supplying Cu2+ and H2O2 synergistically enhancing disulfiram-mediated melanoma chemotherapy.

Disulfiram (DSF) can target and kill cancer cells by disrupting cellular degradation of extruded proteins and has therefore received particular attention for its tumor chemotherapeutic potential. However, the uncontrollable Cu2+/DSF ratio reduces the efficacy of DSF-mediated chemotherapy. Herein, self-supplying Cu2+ and oxidative stress synergistically enhanced DSF-mediated chemotherapy is proposed for melanoma-based on PVP-coated CuO2 nanodots (CPNDs). Once ingested, DSF is broken down to diethyldithiocarbamate (DTC), which is delivered into a tumor via the circulation. Under the acidic tumor microenvironment, CPNDs produce sufficient Cu2+ and H2O2. DTC readily chelates Cu2+ ions to generate CuET, which shows antitumor efficacy. CuET-mediated chemotherapy can be enhanced by H2O2. Sufficient Cu2+ generation can guarantee the maximum efficacy of DSF-mediated chemotherapy. Furthermore, released Cu2+ can be reduced to Cu+ by glutathione (GSH) and O2- in tumor cells, and Cu+ can react with H2O2 to generate toxic hydroxyl radicals (·OH) via a Fenton-like reaction, promoting the efficacy of CuET. Therefore, this study hypothesizes that employing CPNDs instead of Cu2+ ions could enhance DSF-mediated melanoma chemotherapy, providing a simple but efficient strategy for achieving chemotherapeutic efficacy.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

Comparison of the diagnostic effectiveness of ultrasound imaging coupled with three mathematical models for discriminating thyroid nodules.

BACKGROUND: The overlapping nature of thyroid lesions visualized on ultrasound (US) images could result in misdiagnosis and missed diagnoses in clinical practice. PURPOSE: To compare the diagnostic effectiveness of US coupled with three mathematical models, namely logistic regression (Logistics), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM), in discriminating between malignant and benign thyroid nodules. MATERIAL AND METHODS: A total of 588 thyroid nodules (287 benign and 301 malignant) were collected, among which 80% were utilized for constructing the mathematical models and the remaining 20% were used for internal validation. In addition, an external validation cohort comprising 160 nodules (80 benign and 80 malignant) was employed to validate the accuracy of these mathematical models. RESULTS: Our study demonstrated that all three models exhibited effective predictive capabilities for distinguishing between benign and malignant nodules, whose diagnostic effectiveness surpassed that of the TI-RADS classification, particularly in terms of true negative diagnoses. SVM achieved a higher diagnostic rate for malignant thyroid nodules (93.8%) compared to Logistics (91.5%) and PLS-DA (91.6%). PLS-DA exhibited higher diagnostic rates for benign thyroid nodules (91.9%) compared to Logistics (86.7%) and SVM (88.7%). Both the area under the receiver operating characteristic curve (AUC) values of PLS-DA (0.917) and SVM (0.913) were higher than that of Logistics (0.891). CONCLUSION: Our findings indicate that SVM had significantly higher rates of true positive diagnoses and PLS-DA exhibited significantly higher rates of true negative diagnoses. All three models outperformed the TI-RADS classification in discriminating between malignant and benign thyroid nodules.

Preoperative Contrast-Enhanced Ultrasound Predicts Microvascular Invasion in Hepatocellular Carcinoma as Accurately as Contrast-Enhanced MR.

OBJECTIVES: Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR model. METHODS: A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were compared. RESULTS: By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three models. CONCLUSION: The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules.

This study compared the diagnostic efficiency of benign and malignant breast nodules using ultrasonographic characteristics coupled with several machine-learning models, including logistic regression (Logistics), partial least squares discriminant analysis (PLS-DA), linear support vector machine (Linear SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN) and random forest (RF). The clinical information and ultrasonographic characteristics of 926 female patients undergoing breast nodule surgery were collected and their relationships were analyzed using Pearson's correlation. The stepwise regression method was used for variable selection and the Monte Carlo cross-validation method was used to randomly divide these nodule cases into training and prediction sets. Our results showed that six independent variables could be used for building models, including age, background echotexture, shape, calcification, resistance index, and axillary lymph node. In the prediction set, Linear SVM had the highest diagnosis rate of benign nodules (0.881), and Logistics, ANN and LDA had the highest diagnosis rate of malignant nodules (0.910~0.912). The area under the ROC curve (AUC) of Linear SVM was the highest (0.890), followed by ANN (0.883), LDA (0.880), Logistics (0.878), RF (0.874), PLS-DA (0.866), and KNN (0.855), all of which were better than that of individual variances. On the whole, the diagnostic efficacy of Linear SVM was better than other methods.

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.

Objective: To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). Methods: One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Results: Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. Conclusion: The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.

OBJECTIVE: We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND METHODS: This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital. RESULTS: The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model. CONCLUSION: The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

The role of ultrasound quantitative parameters in the assessment of acute radiodermatitis after breast-conserving surgery.

This study aimed to assess the severity of acute radiodermatitis (ARD) by ultrasound quantitative parameters and to try to identify the influencing factors of skin toxicity. A total of 55 patients who underwent radiotherapy after unilateral breast-conserving surgery (BCS) were included in the study. The irradiated side of the breast was used as the research object and the quantitative ultrasound parameters (skin thickness, shear wave elasticity) were evaluated before radiotherapy, every week during radiotherapy. Two weeks after radiotherapy, the patients were divided into two groups, according to the World Health Organization scoring standard: mild (0-2 grade) and severe (3-4 grade). The differences in the parameters between the groups and the changes during radiotherapy were compared, and the relationship between these parameters and the severity of ARD was analyzed. In addition, some clinical factors that may affect ARD were also included in our study. Ninety-eight percent of patients developed different degrees of ARD, and Group 2 accounted for ~31%. At the end of 5 weeks of radiotherapy, the difference in thickness between the two groups was statistically significant (P < 0.05). There was no significant change in the elastic modulus of breast skin between the two groups (P > 0.05). Body mass index >25 kg/m2, breast thickness ≥18 mm, skin basic elastic modulus <23 kPa and skin thickness increment >0.3 mm were considered to be associated with severe skin reactions (P < 0.05). Ultrasound can be a useful tool for the non-invasive and objective assessment of skin changes during radiotherapy, documenting quantitative changes in the skin of breast cancer patients following BCS undergoing radiotherapy.

Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions.

PURPOSE: To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4. METHODS: A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves. RESULTS: On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test. CONCLUSION: CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS: A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. RESULTS: As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly. CONCLUSION: The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders. KEY POINTS: • Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.

A model to predict the prognosis of diffuse large B-cell lymphoma based on ultrasound images.

The purpose of this paper was to assess the value of ultrasonography in the prognosis of diffuse large b-cell lymphoma (DLBCL) by developing a new prognostic model. One hundred and eleven DLBCL patients with complete clinical information and ultrasound findings were enrolled in our study. Univariate and multivariate regression analyses were used to identify independent risk factors for progression-free survival (PFS) and overall survival (OS). Receiver operator characteristic (ROC) curves were plotted and the corresponding area under the curve (AUC) was calculated to assess the accuracy of the international prognostic index (IPI) and new model in DLBCL risk stratification. The results suggested that hilum loss and ineffective treatment were independent risk variables for both PFS and OS in DLBCL patients. Additionally, the new model that added hilum loss and ineffective treatment to IPI had a better AUC for PFS and OS than IPI alone (AUC: 0.90, 0.88, and 0.82 vs. 0.71, 0.74, and 0.68 for 1-, 3-, and 5-year PFS, respectively; AUC: 0.92, 0.85 and 0.86 vs. 0.71, 0.75 and 0.76, for 1-, 3-, and 5-year OS, respectively). The model based on ultrasound images could better suggest PFS and OS of DLBCL, allowing for better risk stratification.

Added value of contrast-enhanced ultrasound to conventional ultrasound for characterization of indeterminate soft-tissue tumors.

OBJECTIVE: To assess the added value of contrast-enhanced ultrasound (CEUS) to conventional ultrasound in differentiating benign soft-tissue tumors from malignant ones. METHODS: 197 soft-tissue tumors underwent ultrasound examination with confirmed histopathology were retrospectively evaluated. The radiologists classified all the tumors as benign, malignant, or indeterminate according to ultrasound features. The indeterminate tumors underwent CEUS were reviewed afterwards for malignancy identification by using individual and combined CEUS features. RESULTS: Ultrasound analysis classified 62 soft-tissue tumors as benign, 111 tumors as indeterminate and 24 tumors as malignant. There 104 indeterminate tumors were subject to CEUS. Three CEUS features including enlargement of enhancement area, infiltrative enhancement boundary, and intratumoral arrival time difference were significantly associated with the tumor nature in both univariable and multivariable analysis for the indeterminate tumors (all p < 0.05). When at least one out of the three discriminant CEUS features were present, the best sensitivity of 100% for malignancy identification was obtained with the specificity of 66.7% and the AUC of 0.833. When at least two of the three discriminant CEUS features were present, the best area under the receiver operating characteristic curve (AUC) of 0.924 for malignancy identification was obtained. The combination of at least two discriminant CEUS features showed much better diagnostic performance than the optimal combination of ultrasound features in terms of AUC (0.924 vs 0.608, p < 0.0001), sensitivity (94.0% vs 42.0%, p < 0.0001), and specificity (90.7% vs 79.6%, p = 0.210) for the indeterminate tumors. CONCLUSION: The combination CEUS features of enlargement of enhancement area, infiltrative enhancement boundary and intratumoral arrival time difference are valuable to improve the discriminating performance for indeterminate soft-tissue tumors on conventional ultrasound. ADVANCES IN KNOWLEDGE: The combination of peritumoral and arrival-time CEUS features can improve the discriminating performance for indeterminate soft-tissue tumors on conventional ultrasound.

Development and validation of nomograms by radiomic features on ultrasound imaging for predicting overall survival in patients with primary nodal diffuse large B-cell lymphoma.

Objectives: To develop and validate a nomogram to predict the overall survival (OS) of patients with primary nodal diffuse large B-cell lymphoma(N-DLBCL) based on radiomic features and clinical features. Materials and methods: A retrospective analysis was performed on 145 patients confirmed with N-DLBCL and they were randomly assigned to training set(n=78), internal validation set(n=33), external validation set(n=34). First, a clinical model (model 1) was established according to clinical features and ultrasound (US) results. Then, based on the radiomics features extracted from conventional ultrasound images, a radiomic signature was constructed (model 2), and the radiomics score (Rad-Score) was calculated. Finally, a comprehensive model was established (model 3) combined with Rad-score and clinical features. Receiver operating characteristic (ROC) curves were employed to evaluate the performance of model 1, model 2 and model 3. Based on model 3, we plotted a nomogram. Calibration curves were used to test the effectiveness of the nomogram, and decision curve analysis (DCA) was used to asset the nomogram in clinical use. Results: According to multivariate analysis, 3 clinical features and Rad-score were finally selected to construct the model 3, which showed better predictive value for OS in patients with N-DLBCL than mode 1 and model 2 in training (AUC,0. 891 vs. 0.779 vs.0.756), internal validation (AUC, 0.868 vs. 0.713, vs.0.756) and external validation (AUC, 914 vs. 0.866, vs.0.789) sets. Decision curve analysis demonstrated that the nomogram based on model 3 was more clinically useful than the other two models. Conclusion: The developed nomogram is a useful tool for precisely analyzing the prognosis of N-DLBCL patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.

Thrombin injection under B-flow and ultrasound guidance: A safe and effective treatment of pseudoaneurysms.

PURPOSE: To evaluate the method of thrombin injection under B-flow and ultrasound guidance (BUGTI) for the treatment of pseudoaneurysms. MATERIALS AND METHODS: Twenty-one patients suffering from pseudoaneurysm (PSA) were retrospectively reviewed at the First Affiliated Hospital of Nanjing Medical University in Nanjing, China, from January 2018 to August 2019. The patients were treated using an ultrasound-guided injection of thrombin (500 IU/mL) combined with B-mode blood flow imaging (B-flow). The information on the PSA, including the size of the arterial rupture and sac, flow rate, thrombin dose, and treatment outcome, was recorded during the procedure. Follow-up evaluation was performed at 1, 3, and 6 months after the treatment. Pearson's correlation analysis was performed among the characteristics of PSA and the dose of thrombin. RESULT: The age of patients ranged from 34 to 80 years and averaged 62.8 years. The maximum cross-sectional area of PSA ranged from 208 to 1148 mm2. All patients were treated with thrombin injections. The dose of thrombin ranged from 300 to 1667 IU. No reperfusions were detected at follow-up 6 months, and the BUGTI treatment was successful in all 21 cases. Pearson's correlation analysis demonstrated that the dose of thrombin was positively correlated with the width (r = 0.449, p < .05) and maximum cross-sectional area (r = 0.504, p < .05) of PSA. CONCLUSION: Thrombin injection under B-flow and ultrasound guidance is a rapid and effective treatment for PSA. Additionally, the sac size could be used to estimate the dose of thrombin.

Novel Human Artificial Intelligence Hybrid Framework Pinpoints Thyroid Nodule Malignancy and Identifies Overlooked Second-Order Ultrasonographic Features.

We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule malignancy stratification and diagnosis. We defined extra ultrasonographical features from color Doppler images to explore malignancy-relevant features. We proposed Gated Attentional Factorization Machine (GAFM) to identify second-order interacting features trained via a 10 fold distribution-balanced stratified cross-validation scheme on ultrasound images of 3002 nodules all finally characterized by postoperative pathology (1270 malignant ones), retrospectively collected from 131 hospitals. Our GAFM-HAIbrid model demonstrated significant improvements in Area Under the Curve (AUC) value (p-value < 10−5), reaching about 0.92 over the standalone CNN (~0.87) and senior radiologists (~0.86), and identified a second-order vascularity localization and morphological pattern which was overlooked if only first-order features were considered. We validated the advantages of the integration framework on an already-trained commercial CNN system and our findings using an extra set of ultrasound images of 500 nodules. Our HAIbrid framework allows natural integration to clinical workflow for thyroid nodule malignancy risk stratification and diagnosis, and the proposed GAFM-HAIbrid model may help identify novel diagnosis-relevant second-order features beyond ultrasonography.

Diagnostic Performance of US and MRI in Predicting Malignancy of Soft Tissue Masses: Using a Scoring System.

Objective: To assess the diagnostic performance of US and MRI in predicting malignancy of soft tissue masses by using a scoring system. Methods: A total of 120 cases of pathologically confirmed soft tissue masses (71 cases of malignant lesions and 49 cases of benign lesions) were enrolled. All patients underwent ultrasound and MRI examination prior to biopsy or surgical excision. A scoring system based on the parameters of conventional US and MRI to distinguish malignant and benign masses was established by the regression model. The receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of US and MRI. Results: Multivariate analysis showed that margin, maximum diameter, and vascular density were independent predictors for malignancy found by US, while maximum diameter, margin, and affected peripheral soft tissue were independent predictors for malignancy found by MRI. The mean scores of the benign and malignant groups were 2.8 ± 1.6, 5.1 ± 1.1 on US and 1.3 ± 1.2, 3.5 ± 0.9 on MRI. Based on the cut-off score of 3.5 and 2.5 calculated by ROC analysis, US and MRI had 92% and 87% sensitivity, 72% and 76% specificity, 86% and 89% accuracy, respectively. The combination of these two modalities achieved the sensitivity of 91%, specificity of 82%, and accuracy of 93%. Conclusions: Both US and MRI can provide valuable information about the differential diagnosis between benign and malignant soft tissue masses. The combination of the two imaging-based scoring systems can increase the diagnostic performance, especially in specificity.

Diagnostic value of thyroid imaging reporting and data system combined with BRAFV600E mutation analysis in Bethesda categories III-V thyroid nodules.

Fine needle aspiration biopsy is a crucial method for preoperative diagnosis of thyroid nodules. However, thyroid nodules classified as Bethesda categories III-V cannot obtain definite cytological results. Our aim was to study the diagnostic value of thyroid imaging reporting and data system combined with BRAFV600E mutation analysis in Bethesda categories III-V thyroid nodules, so as to provide more precise direction for the follow-up treatments. A total of 174 Bethesda categories III-V thyroid nodules performed TIRADS and BRAFV600E mutation analysis were included in the study. We retrospectively analyzed the ultrasound features as well as the results of BRAFV600E mutation of the 174 thyroid nodules. In the multiple regression analysis models, ultrasound features including lobulated or irregular margin, punctate echogenic foci, and shape with taller-than-wide were statistically significant in malignant nodules (p < 0.05). The area under the curve of the combination of TIRADS and BRAFV600E increased to 0.925, which were much higher than TIRADS (0.861) and BRAFV600E (0.804) separately. Combined diagnosis was of the greatest value to identify Bethesda III-V thyroid nodules definitely, especially with higher sensitivity (93%) and accuracy (90%).

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.

PURPOSE: To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions. METHODS: Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites. RESULTS: A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157). CONCLUSIONS: Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.