The role of tissue elasticity in the differential diagnosis of benign and malignant breast lesions using shear wave elastography.

BACKGROUND: Elastography is a promising way to evaluate tissue differences regarding stiffness, and the stiffness of the malignant breast lesions increased at the lesion margin. However, there is a lack of data on the value of the shear wave elastography (SWE) parameters of the surrounding tissue (shell) of different diameter on the diagnosis of benign and malignant breast lesions. Therefore, the purpose of our study was to evaluate the diagnostic performance of shell elasticity in the diagnosis of benign and malignant breast lesions using SWE. METHODS: Between September 2016 and June 2017, women with breast lesions underwent both conventional ultrasound (US) and SWE. Elastic values of the lesions peripheral tissue were determined according to the shell size, which was automatically drawn along the edge of the lesion using the following software guidelines: (1): 1 mm; (2): 2 mm; and (3): 3 mm. Quantitative elastographic features of the inner lesions and shell, including the elasticity mean (Emean), elasticity maximum (Emax), and elasticity minimum (Emin), were calculated using an online-available software. The receiver operating characteristic curves (ROCs) of the elastographic features was analyzed to assess the diagnostic performance, and the area under curve (AUC) of each elastographic feature was obtained. Logistic regression analysis was used to predict significant factors of malignancy, permitting the design of predictive models. RESULTS: This prospective study included 63 breast lesions of 63 women. Of the 63 lesions, 33 were malignant and 30 were benign. The diagnostic performance of Emax-3shell was the highest (AUC = 0.76) with a sensitivity of 60.6% and a specificity of 83.3%. According to stepwise logistic regression analysis, the Emax-3shell and the Emin-3shell were significant predictors of malignancy (p < 0.05). The AUC of the predictive equation was 0.86. CONCLUSIONS: SWE features, particularly the combination of Emax-3shell and Emin-3shell can improve the diagnosis of breast lesions.

Classification-Based Evaluation of Multi-Ingredient Dish Using Graphene-Modified Interdigital Electrodes.

A taste sensor with global selectivity can be used to discriminate taste of foods and provide evaluations. Interfaces that could interact with broad food ingredients are beneficial for data collection. Here, we prepared electrochemically reduced graphene oxide (ERGO)-modified interdigital electrodes. The interfaces of modified electrodes showed good sensitivity towards cooking condiments in mixed multi-ingredients solutions under electrochemical impedance spectroscopy (EIS). A database of EIS of cooking condiments was established. Based on the principal component analysis (PCA), subsets of three taste dimensions were classified, which could distinguish an unknown dish from a standard dish. Further, we demonstrated the effectiveness of the electrodes on a typical dish of scrambled eggs with tomato. Our kind of electronic tongue did not measure the quantitation of each ingredient, instead relying on the database and classification algorithm. This method is facile and offers a universal approach to simultaneously identifying multiple ingredients.

A Diagnostic Model for Breast Lesions With Enlarged Enhancement Extent on Contrast-Enhanced Ultrasound Improves Malignancy Prediction.

OBJECTIVE: The aim of the work described here was to develop a diagnostic model based on contrast-enhanced ultrasound (CEUS) features to improve performance in predicting the probability of malignancy for breast lesions with an enlarged enhancement extent on CEUS. METHODS: In total, 299 consecutive patients who underwent CEUS examination and had confirmed pathological results were retrospectively enrolled. Among the 299 patients, an enlarged enhancement extent on CEUS was found in 142 patients. In this special cohort, we analyzed the association of malignant pathologic results with perfusion patterns emphatically by reclassifying the patterns. RESULTS: A diagnostic model was developed and presented as a nomogram, assessed with discrimination and calibration. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of the conventional perfusion and modified perfusion patterns were 0.58 and 0.76 (p < 0.001), respectively. A diagnostic model was built and exhibited good discrimination with a C-index of 0.95 (95% confidence interval: 0.91-0.98), which was confirmed to be 0.93 via internal bootstrapping validation. CONCLUSION: The nomogram based on CEUS features provides radiologists with a quantitative tool to predict the probability of malignancy in this special cohort of breast lesions.

A nomogram based on combining clinical features and contrast enhanced ultrasound is not able to identify Her-2 over-expressing cancer from other breast cancers.

Objective: The aim of this study was to evaluate whether a predictive model based on a contrast enhanced ultrasound (CEUS)-based nomogram and clinical features (Clin) could differentiate Her-2-overexpressing breast cancers from other breast cancers. Methods: A total of 152 pathology-proven breast cancers including 55 Her-2-overexpressing cancers and 97 other cancers from two units that underwent preoperative CEUS examination, were included and divided into training (n = 102) and validation cohorts (n = 50). Multivariate regression analysis was utilized to identify independent indicators for developing predictive nomogram models. The area under the receiver operating characteristic (AUC) curve was also calculated to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff nomogram value were compared. Results: In the training cohort, 7 clinical features (menstruation, larger tumor size, higher CA153 level, BMI, diastolic pressure, heart rate and outer upper quarter (OUQ)) + enlargement in CEUS with P < 0.2 according to the univariate analysis were submitted to the multivariate analysis. By incorporating clinical information and enlargement on the CEUS pattern, independently significant indicators for Her-2-overexpression were used for further predictive modeling as follows: Model I, nomogram model based on clinical features (Clin); Model II, nomogram model combining enlargement (Clin + Enlargement); Model III, nomogram model based on typical clinical features combining enlargement (MC + BMI + diastolic pressure (DP) + outer upper quarter (OUQ) + Enlargement). Model II achieved an AUC value of 0.776 at nomogram cutoff score value of 190, which was higher than that of the other models in the training cohort without significant differences (all P>0.05). In the test cohort, the diagnostic efficiency of predictive model was poor (all AUC<0.6). In addition, the sensitivity and specificity were not significantly different between Models I and II (all P>0.05), in either the training or the test cohort. In addition, Clin exhibited an AUC similar to that of model III (P=0.12). Moreover, model III exhibited a higher sensitivity (70.0%) than the other models with similar AUC and specificity, only in the test cohort. Conclusion: The main finding of the study was that the predictive model based on a CEUS-based nomogram and clinical features could not differentiate Her-2-overexpressing breast cancers from other breast cancers.

Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

Amphiphilic Indoles as Efficient Phase-Transfer Catalysts for Bromination in Water.

Brominated compounds are important, but they are usually prepared in organic solvents. Here, efficient amphiphilic indole-based phase-transfer organocatalysts were developed for environmentally benign bromination reactions in water. As test reactions, hydroxybromination of olefins and aromatic bromination could be conducted in a greener and more sustainable manner compared with methods using organic solvents, producing the corresponding bromides in good yields. Some pure products could be obtained without column chromatography.

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer-A Multicenter Study.

Purpose: To develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound. Materials and Methods: A total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated. Results: A significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the "stiff rim" sign, minimum elastic modulus of the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0-4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617-0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%-62.1%), and a specificity of 68.99% (95% CI, 64.5%-73.3%) in predicting axillary LN metastasis. Conclusion: A 0-4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.

The features of contrast enhanced ultrasound and BRAF V600E in papillary thyroid carcinoma.

BACKGROUND: BRAF V600E mutation was proved to be associated with thyroid cancer. Papillary thyroid carcinoma (PTC) with positive BRAF mutation might have a more aggressive behavior. We investigated the correlation of the contrast-enhanced ultrasound (CEUS) features with BRAF 600VE in PTC. METHODS: The medical records of 1,199 patients with 1,315 nodules who underwent CEUS prior to fine needle aspiration (FNA) from January 2016 to March 2018 were retrospectively reviewed. The features of their enhancement were assessed from eight aspects: degree of enhancement, method of enhancement, homogeneity of enhancement, completeness of enhancement, boundary of the enhanced lesions, shape of the enhanced lesions, size of the enhanced lesions, and wash out period of the enhanced lesions. The patients then examined for the BRAF V600E mutation using specimens obtained from FNA. RESULTS: BRAF mutations were found in 888 of 1,315 nodules. The CEUS features were significantly different between BRAF-positive and BRAF-negative nodules. The BRAF mutation positive nodules were more often with larger size, hypo-enhancement, centripetal enhancement, inhomogeneous enhancement, complete enhancement, blurred boundary, irregular shape, and with wash out period at preoperative CEUS than those without BRAF mutations (P<0.001). However, no significant correlation was showed in Spearman's rank correlation between the CEUS features and BRAF mutation, except for degree of enhancement, method pattern of enhancement, and completeness of complete enhancement. Multivariate analysis showed that centripetal (OR: 1.465, 95% CI: 1.129-1.903) and no significant enhancement (OR: 0.790, 95% CI: 0.639-0.977) were predictive for the presence of BRAF mutations. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of no significant enhancement and centripetal enhancement of CEUS for predicting BRAF mutation were 68.3%, 40.0%, 91.6%, 11.7%, and 72.4%, 35.1%, 37.8%, 70.0%, respectively. CONCLUSIONS: Our study indicated that preoperative thyroid nodule characteristics on CEUS might serve as a useful tool to BRAF mutation in PTC.