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The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules.From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology.We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p0.001; AUC 0.98 vs 0.55, p0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p0.001).The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions.

Background Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2. Methods We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity. Results The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p Conclusions Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.

Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment. We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC). In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795). High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC. • Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.

Precise nodule segmentation in thyroid ultrasound images is important for clinical quantitative analysis and diagnosis. Fully supervised deep learning method can effectively extract representative features from nodules and background. Despite the great success, deep learning–based segmentation methods still face a critical hindrance: the difficulty in acquiring sufficient training data due to high annotation costs. To this end, we propose a weakly supervised framework called uncertainty to fine generative adversarial network (U2F-GAN) for nodule segmentation in thyroid ultrasound images that exploits only a handful of rough bounding box annotations to successfully generate reliable labels from these weak supervisions. Based on feature-matching GAN, the proposed method alternates between generating masks and learning a segmentation network in an adversarial manner. Super-pixel processing mechanism is adopted to reflect low-level image structure features for learning and inferring semantic segmentation, which largely improve the efficiency of training process. In addition, we introduce a similarity comparison module and a distributed loss function with constraints to effectively remove noise in localization annotations and enhance the generalization capability of the network, thus strengthen the overall segmentation performance. Compared to existing weakly supervised approaches, our proposed U2F-GAN yields a significant performance boost. The segmentation results are also comparable to fully supervised methods, but the annotation burden is much lower. Also, the training speed of the network model is much faster than other methods with weak supervisions, which enables the network to be updated in time, thus is beneficial to high-throughput medical image setting.

BACKGROUND: Elucidation the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer is important for informing therapeutic decisions. This study aimed at evaluating the potential value of contrast-enhanced ultrasound (CEUS) parameters in predicting breast cancer responses to NAC. METHODS: We performed CEUS examinations before and after two cycles of NAC. Quantitative CEUS parameters [maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (mTT)], tumor diameter, and their changes were measured and compared to histopathological responses, according to the Miller-Payne Grading (MPG) system (score 1, 2, or 3: minor response; score 4 or 5: good response). Prediction models for good response were developed by multiple logistic regression analysis and internally validated through bootstrap analysis. The receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. RESULTS: A total of 143 patients were enrolled in this study among whom 98 (68.5%) achieved a good response and while 45 (31.5%) exhibited a minor response. Several imaging variables including diameter, IMAX, changes in diameter (Δdiameter), IMAX (ΔIMAX) and TTP (ΔTTP) were found to be significantly associated with good therapeutic responses (P

Within the context of China’s Urban Employees’ Basic Pension Insurance (UEBPI), this paper constructs an actuarial model to analyze the financial imbalance risk of contribution rate reduction and to investigate the possibility of further reducing the contribution rate. It is found that the UEBPI fund would show financial imbalance risk in 2024 if the contribution rate is 16%, and no control strategy is introduced. In the case of single strategy (the collection system reform, delay of retirement age, or the introduction of external finance), the financial sustainability of the UEBPI fund could be improved to some extent, whereas the financial imbalance risk remains huge. In the case of a package of control strategies being implemented, the UEBPI fund could be able to continue its operation until 2060, and the contribution rate can be further reduced by 0–4 percentage. Therefore, the implementation of a package of control strategies presents a prerequisite for controlling the financial imbalance risk and further reducing the contribution rate.

Segmentation of the breast ultrasound (BUS) image is an important step for subsequent assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have achieved satisfactory performance in many computer vision tasks, especially in medical image segmentation. Nevertheless, those methods always require a large number of pixel-wise labeled data that is expensive in medical practices. In this study, we propose a new segmentation method by dense prediction and local fusion of superpixels for breast anatomy with scarce labeled data. First, the proposed method generates superpixels from the BUS image enhanced by histogram equalization, a bilateral filter, and a pyramid mean shift filter. Second, using a convolutional neural network (CNN) and distance metric learning-based classifier, the superpixels are projected onto the embedding space and then classified by calculating the distance between superpixels’ embeddings and the centers of categories. By using superpixels, we can generate a large number of training samples from each BUS image. Therefore, the problem of the scarcity of labeled data can be better solved. To avoid the misclassification of the superpixels, $K$ -nearest neighbor (KNN) is used to reclassify the superpixels within every local region based on the spatial relationships among them. Fivefold cross-validation was taken and the experimental results show that our method outperforms several often used deep-learning methods under the condition of the absence of a large number of labeled data (48 BUS images for training and 12 BUS images for testing).

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods., A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario.

Purpose To determine the overall performance of contrast-enhanced ultrasound (CEUS) in differentiating between benign and malignant breast lesions and in predicting the pathologic response to neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC). Materials and Methods Articles published up to April 2019 were systematically searched in Medline, Web of Science, and China National Knowledge Infrastructure. The sensitivities and specificities across studies, the calculations of positive and negative likelihood ratios (LR+ and LR−), diagnostic odds ratio (OR), and constructed summary receiver operating characteristic curves were determined. Methodologic quality was assessed using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies) tool. Subgroup analyses and metaregression were performed on prespecified study-level characteristics. Results Fifty-one studies involving 4875 patients with 5246 breast lesions and 10 studies involving 462 patients with BC receiving NAC were included. Methodologic quality was relatively high, and no publication bias was detected. The overall sensitivity, specificity, diagnostic OR, LR+, and LR− for CEUS were 0.88 (95% confidence interval [CI], 0.86-0.89), 0.82 (95% CI, 0.80-0.83), 30.55 (95% CI, 21.40-43.62), 4.29 (95% CI, 3.51-5.25), and 0.16 (95% CI, 0.13-0.21), respectively, showing statistical heterogeneity. Multivariable metaregression analysis showed contrast mode to be the most significant source of heterogeneity. The overall sensitivity, specificity, LR+, LR, and diagnostic OR of CEUS imaging in predicting the overall pathologic response to NAC in patients with BC were 0.89 (95% CI, 0.83-0.93), 0.83 (95% CI, 0.78-0.88), 4.49 (95% CI, 3.04-6.62), 0.16 (95% CI, 0.10-0.24,), and 32.21 (95% CI, 16.74-62.01), respectively, showing mild heterogeneity. Conclusion Our data confirmed the excellent performance of breast CEUS in differentiating between benign and malignant breast lesions as well as pathologic response prediction in patients with BC receiving NAC.

Objectives: To assess the performance of elastography (ES) and ultrasound (US) in predicting the malignancy of breast lesions and to compare their combined diagnostic value with that of magnetic resonance imaging (MRI). Materials and Methods: The study prospectively enrolled 242 female patients with dense breasts treated in 35 heath care facilities in China between November 2018 and October 2019. Based on conventional US and elastography, radiologists classified the degree of suspicion of breast lesions according to the US Breast Imaging Reporting and Data System (BI-RADS) criteria. The diagnostic value was compared between US BI-RADS and MRI BI-RADS, with pathological results used as the reference standard. Results: The results demonstrated that irregular tumor shape, a nonparallel growth orientation, indistinct margins, angular contours, microcalcifications, color Doppler flow and ES score on US imaging were significantly related to breast cancer in dense breasts (P=0.001; P=0.001; P=0.008; P

OBJECTIVES To investigate the correlation between ultrasound (US) appearances of invasive breast cancers and tumor proliferation and invasiveness measured according to the histologic grade, Ki-67 expression, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI). METHODS This study evaluated 676 patients who underwent primary surgical treatment of invasive breast cancers. The preoperative US reports and postoperative pathologic and immunohistochemical results of the patients were retrospectively reviewed. Ultrasound characteristics were evaluated according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon. Logistic regression analyses were used to identify independent predictive US features that were correlated with tumor proliferation and invasiveness of breast cancers. Odds ratios (ORs) were calculated. RESULTS Posterior acoustic enhancement and calcifications on US images were independent predictive factors of a higher histologic grade and a higher Ki-67 level (OR, 1.69-6.54; P

Background and Objectives: The breast undergoes extensive physiologic changes during pregnancy/lactation. We aimed to review the ultrasound (US) imaging characteristics of breast lesions during pregnancy/lactation and to demonstrate ultrasonography as an excellent imaging modality in this patient population. Materials and Methods: We performed a retrospective study involving 195 patients with 206 pathologically confirmed breast lesions during pregnancy/lactation over the period of January 2010 to December 2018; 51 were diagnosed with breast cancer, including 50 invasive ductal carcinomas and 1 low malignant potential phyllodes tumor, whereas 144 were diagnosed with 155 benign lesions, including 103 mastitis/abscesses, 45 fibroadenomas, 2 intraductal papillomas, 2 sclerosing lesions, and 3 benign phyllodes tumors. The patients' age, lesion diameter, and US characteristics were analyzed. Results: The breast lesions of patients during pregnancy/lactation were compared with those during nonpregnancy/lactation. Patients with breast cancer or fibroadenomas during pregnancy/lactation were younger than those during nonpregnancy/lactation. The average lesion diameter was significantly higher among pregnant/lactating patients compared with controls of childbearing age for fibroadenomas, but not for malignant lesions. The fibroadenomas and mastitis/abscesses during pregnancy/lactation usually have higher BI-RADS categories than those during nonpregnancy/lactation. Conclusions: The imaging features of breast cancer during pregnancy/lactation did not differ much from those of nonpregnancy/lactation; however, some benign lesions had suspicious sonographic features, and US-guided core biopsies were necessary for differentiating benign from malignant lesions.

We propose to construct the finite modular groups from the quotient of two principal congruence subgroups as $\Gamma(N')/\Gamma(N")$, and the modular group $SL(2,\mathbb{Z})$ is extended to a principal congruence subgroup $\Gamma(N')$. The original modular invariant theory is reproduced when $N'=1$. We perform a comprehensive study of $\Gamma'_6$ modular symmetry corresponding to $N'=1$ and $N"=6$, five types of models for lepton masses and mixing with $\Gamma'_6$ modular symmetry are discussed and some example models are studied numerically. The case of $N'=2$ and $N"=6$ is considered, the finite modular group is $\Gamma(2)/\Gamma(6)\cong T'$, and a benchmark model is constructed., Comment: 39 pages, 4 figures

Objective To assess the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography. Methods Women with breast lesions identified as having mammography BI-RADS 4 lesions and underwent US examination were included in China. US features and US BI-RADS assessment were recorded in real-time and prospectively reported. The pathological result was referred to as the gold standard. The performance of US in the mammographic BI-RADS category 4 lesions was evaluated. Diagnostic performances of US BI-RADS, SE and combined both were compared. Results 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. For mammographic findings, 530 (70.6%) were true-positive and 221 (29.4%) were false-positive. Conventional US achieved higher positive predictive value (PPV) than mammography (78.5% vs 70.6%, p=0.001). The specificity increased from 34.4% to 47.1% (p Conclusions Conventional US combined with SE is a helpful tool for the non-invasive examination of breast lesions classified as BI-RADS category 4 on mammography. It helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography. Micro Abstract We assessed the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography. 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. Conventional US combined with SE helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography.

BackgroundPapillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors.MethodsIn total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis.ResultsThe radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (pConclusionsThe radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.

Background To describe the gray scale ultrasound features of metastatic ovarian tumors on the basis of the origin of the primary tumor in a large study population. This retrospective study included 112 patients with 188 histopathologically confirmed metastatic ovarian tumors. Among the patients, 102 were detected with 143 masses via ultrasonography. Patient age, mass bilaterality, and maximum tumor diameter were recorded. Results Ultrasound images were classified into three subtypes: (type A) multilocular-solid, (type B) purely solid, and (type C) solid with several round or oval cysts. The metastatic tumors in the ovaries were mostly bilateral but had a lower rate of bilaterality when detected by ultrasound. Breast cancer metastasis had the highest misdetection rate (69.6%) because its focal metastasis could only be recognized by histology or immunohistochemistry. Stomach was the most common origin of metastasis: 45.7% and 51.0% via pathology and ultrasonography, respectively. Metastases that originated from colon mostly belonged to type A (65.9%) and closely mimicked primary epithelial ovarian tumor morphologically. Metastases that originated from stomach predominantly belonged to types B (31.9%) and C (58.3%). Signet-ring cell carcinoma (SRCC) corresponded to types B and C only regardless of its origin. Conclusions The novel typing method developed herein provides more vivid images for classifying ovary metastasis compared with existing typing methods. Given that no specific sonographic parameters have been established to distinguish metastatic ovarian masses from primary invasive ovarian tumors, these images can be helpful in diagnosing these masses.

Uniaxial compression testing was combined with the digital speckle correlation method (DSCM) to explore the deformation field evolution characteristics and the Poisson’s ratio and energy release responses of a geopolymer cemented coal gangue-fly ash backfill (GCGFB) sample under uniaxial compression. The results showed that in the initial stage of loading, the deformation of the GCGFB sample was small and evenly distributed, and no localized deformation zone formed in the sample. As the loading progressed, a localized deformed zone gradually appeared, and the formation rate gradually accelerated. The maximum shear strain value at the start of localized deformation was 0.06. In the post-peak loading process, the localized deformed zone gradually expanded, and the degree of unevenness of the localized deformation was much greater than that before the peak; the maximum shear strain value at this time was 0.22. During the localization of the deformation to the peak intensity stage, the growth rate of the Poisson’s ratio of the GCGFB sample was markedly increased, and the growth rate after the peak was notably slowed and exhibited unstable fluctuations. When the stress reached the peak, the Poisson’s ratio was 0.24. The peak strain of the GCGFB sample was large, and the failure characteristics of the GCGFB sample showed multiple shear deformation zones, which is indicative of a typical cylindrical splitting failure. The deformation energy density of the GCGFB sample exhibited a slow decline after the peak, and the rate of decline gradually decreased. The deformation energy density drop time of the GCGFB sample was 96 s, which is much longer than that of high-strength rock.

Background: To compare the abilities of ultrasonography (US) and Computed Tomography (CT) to identify calcifications and to predict probability of malignancy for Papillary Thyroid Carcinoma (PTC) and Papillary Thyroid Microcarcinoma (PTMC). Methods: We reviewed 1008 cases of PTC/PTMC with calcifications reported by pre-operative US, CT, or post-operative pathology. The size of the thyroid nodule was obtained from the US report and the maximum diameter (d) was documented. According to the nodule size (d), the PTC and PTMC groups were each divided into two subgroups, as follows: large PTC group (d ≥ 2 cm), small PTC group (1 cm < d < 2 cm), large PTMC group (0.6 cm ≤ d ≤ 1 cm), and small PTMC group (d < 0.6 cm). Results: In the 1008 patients, the ratio of females to males was 2.29 and the mean age was 40.9 years (standard deviation: 11.7 years). Of the 1008 records, 92.8% were found to have calcifications according to the US report, while 50.4% showed calcifications according to the CT report. This difference between US and CT reports was statistically significant (p < 0.0005). The percentages of US reports showing calcifications were similar for all four PTC and PTMC subgroups (93.7%, 94.3%, 92.1%, and 85.1%, respectively; p = 0.052), while the percentages of CT reports showing calcifications were significantly different among the PTC and PTMC subgroups (62.3%, 52.2%, 45.4%, and 31.3%, respectively; p < 0.0005). As for the prediction of malignancy, US was superior to CT in all four subgroups (large PTC group: 97.1% vs. 54.1%, small PTC group: 94.8% vs. 42.9%, large PTMC group: 97.2% vs. 32.0%, small PTMC group: 95.5% vs. 14.9%; p < 0.0005 for all pairwise comparisons). No significant difference was observed in terms of the ability of US to predict the malignancy of PTC versus PTMC (p = 0.31), while CT showed significant superiority in diagnosing PTC versus PTMC (p < 0.0005). The predictive value of CT for PTC declined as the nodule size decreased (p < 0.05 for all pairwise comparisons). Conclusion: Our results showed that US detected calcifications and predicted the malignancy of all nodule sizes of thyroid papillary carcinoma equally well, while the performance of CT declined with the reduction of nodule size.

Purpose Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in BUS images. Methods A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (DFCN) with a phase-based active contour (PBAC) model. The DFCN is an improved fully convolutional neural network with dilated convolution in deeper layers, fewer parameters, and batch normalization techniques; and has a large receptive field that can separate tumors from background. The predictions made by the DFCN are relatively rough due to blurry boundaries and variations in tumor sizes; thus, the PBAC model, which adds both region-based and phase-based energy functions, is applied to further improve segmentation results. The DFCN model is trained and tested in dataset 1 which contains 570 BUS images from 89 patients. In dataset 2, a 10-fold support vector machine (SVM) classifier is employed to verify the diagnostic ability using 460 features extracted from the segmentation results of the proposed method. Results Advantages of the present method were compared with three state-of-the-art networks; the FCN-8s, U-net, and dilated residual network (DRN). Experimental results from 170 BUS images show that the proposed method had a Dice Similarity coefficient of 88.97 ± 10.01%, a Hausdorff distance (HD) of 35.54 ± 29.70 pixels, and a mean absolute deviation (MAD) of 7.67 ± 6.67 pixels, which showed the best segmentation performance. In dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.795 which is similar to the classification using the manual segmentation results. Conclusions The proposed automatic method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.

OBJECTIVES To identify clinicopathologic and ultrasound (US) variables that were associated with a heavy nodal tumor burden, which was defined as 3 or more lymph nodes involved with metastasis to the axilla after invasive breast carcinoma. METHODS With ethical approval, 621 patients with a pathologic diagnosis of invasive breast carcinoma were retrospectively analyzed for clinical, pathologic, and US data. Pathologic findings were ascertained by the final paraffin pathologic analysis. Ultrasound characteristics were evaluated on the basis of the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). Univariate and multivariate logistic regression analyses were used to assess the clinicopathologic and US variables that were associated with a heavy nodal tumor burden at the axilla. RESULTS There were 107 cases (17.2%) of invasive breast carcinoma with a heavy tumor burden at the axilla. The independent clinicopathologic variables for a heavy tumor burden at the axilla included a tumor size of 2 to 5 cm (odds ratio [OR], 1.86; P = .036), the presence of lymphovascular invasion (OR, 23.52; P

BackgroundThe rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable on open excision. The purpose of the present study was to develop and validate a simple-to-use nomogram for predicting the upgrade of ADH diagnosed with ultrasound (US)-guided core needle biopsy in patients with US-detected breast lesions.MethodsTwo retrospective sets, the training set (n = 401) and the validation set (n = 186), from Fudan University Shanghai Cancer Center between January 2014 and December 2019 were retrospectively analyzed. Clinicopathological and US features were selected using univariate and multivariable logistic regression, and the significant features were incorporated to build a nomogram model. Model discrimination and calibration were assessed in the training set and validation set.ResultsOf the 587 ADH biopsies, 67.7% (training set: 267/401, 66.6%; validation set: 128/186, 68.8%) were upgraded to cancers. In the multivariable analysis, the risk factors were age [odds ratio (OR) 2.739, 95% confidence interval (CI): 1.525–5.672], mass palpation (OR 3.008, 95% CI: 1.624–5.672), calcifications on US (OR 4.752, 95% CI: 2.569–9.276), ADH extent (OR 3.150, 95% CI: 1.951–5.155), and suspected malignancy (OR 4.162, CI: 2.289–7.980). The model showed good discrimination, with an area under curve (AUC) of 0.783 (95% CI: 0.736–0.831), and good calibration (p = 0.543). The application of the nomogram in the validation set still had good discrimination (AUC = 0.753, 95% CI: 0.666–0.841) and calibration (p = 0.565). Instead of surgical excision of all ADHs, if those categorized with the model to be at low risk for upgrade were surveillanced and the remainder were excised, then 63.7% (37/58) of surgeries of benign lesions could have been avoided and 78.1% (100/128) malignant lesions could be treated in time.ConclusionsThis study developed a simple-to-use nomogram by incorporating clinicopathological and US features with the overarching goal of predicting the probability of upgrade in women with ADH. The nomogram could be expected to decrease unnecessary surgery by nearly two-third and to identify most of the malignant lesions, helping guide clinical decision making with regard to surveillance versus surgical excision of ADH lesions.

BackgroundTo determine a correlation between mRNA and lncRNA signatures, sonographic features, and risk of recurrence in triple-negative breast cancers (TNBC).MethodsWe retrospectively reviewed the data from 114 TNBC patients having undergone transcriptome analysis. The risk of tumor recurrence was determined based on the correlation between transcriptome profiles and recurrence-free survival. Ultrasound (US) features were described according to the Breast Imaging Reporting and Data System. Multivariate logistic regression analysis determined the correlation between US features and risk of recurrence. The predictive value of sonographic features in determining tumor recurrence was analyzed using receiver operating characteristic curves.ResultsThree mRNAs (CHRDL1, FCGR1A, and RSAD2) and two lncRNAs (HIF1A-AS2 and AK124454) were correlated with recurrence-free survival in patients with TNBC. Among the three mRNAs, two were upregulated (FCGR1A and RSAD2) and one was downregulated (CHRDL1) in TNBCs. LncRNAs HIF1A-AS2 and AK124454 were upregulated in TNBCs. Based on these signatures, an integrated mRNA–lncRNA model was established using Cox regression analysis to determine the risk of tumor recurrence. Benign-like sonographic features, such as regular shape, circumscribed margin, posterior acoustic enhancement, and no calcifications, were associated with HIF1A-AS2 expression and high risk of tumor recurrence (PConclusionsSonographic features and mRNA–lncRNA signatures in TNBCs represent the risk of tumor recurrence. Taken together, US may be a promising technique in determining the prognosis of patients with TNBC.

Purpose: The positivity of sentinel lymph node (SLN) metastasis is relatively low in ductal carcinoma in situ (DCIS) patients. The aim of this study was to investigate factors associated with SLN metastasis and build a model to predict the potential risk of SLN metastasis in patients with a preoperative diagnosis of DCIS. Patients and methods: Core needle biopsy-proved DCIS patients who underwent SLN biopsy and breast surgery were retrospectively reviewed and selected. Univariate analysis was used to identify the variables correlated with SLN metastasis. A model to predict SLN metastasis was developed using a multivariate logistic regression in the training set and then validated in an internal set. Results: A total of 407 patients with a preoperative diagnosis of DCIS were included. Upstaging to invasive/microinvasive cancer occurred in 225 patients after surgery. SLN metastasis was found in 42 patients, including 32 patients upstaging to invasive disease, 8 to microinvasive disease, and 2 pure DCIS. Tumor size based on US examination, axillary ultrasound finding, multifocality, surgery, upstaging, and Ki-67 expression were significantly related to SLN metastasis. The model incorporating tumor size, axillary ultrasound finding and multifocality yielded an AUC of 0.805 (95% CI: 0.715-0.895, p

We consider for the first time level 7 modular invariant flavour models where the lepton mixing originates from the breaking of modular symmetry and couplings responsible for lepton masses are modular forms. The latter are decomposed into irreducible multiplets of the finite modular group $\Gamma_7$, which is isomorphic to $PSL(2,Z_{7})$, the projective special linear group of two dimensional matrices over the finite Galois field of seven elements, containing 168 elements, sometimes written as $PSL_2(7)$ or $\Sigma(168)$. At weight 2, there are 26 linearly independent modular forms, organised into a triplet, a septet and two octets of $\Gamma_7$. A full list of modular forms up to weight 8 are provided. Assuming the absence of flavons, the simplest modular-invariant models based on $\Gamma_7$ are constructed, in which neutrinos gain masses via either the Weinberg operator or the type-I seesaw mechanism, and their predictions compared to experiment., Comment: 45 pages, 3 figures

The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called joint-training annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.

Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decision-making and clinical management of patients with papillary thyroid carcinoma (PTC). The aim of this study was to develop an ultrasound (US)-based radiomic nomogram to preoperatively predict the lateral LNM in PTC patients.In this retrospective study, a total of 886 patients were enrolled and randomly divided into 2 groups. Radiomic features were extracted from the preoperative US images. A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set. Multivariate logistic regression was performed to develop the radiomic nomogram, which incorporating the radiomic signature and the selected clinical characteristics. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness in both the training and validation sets.The radiomic signature was significantly associated with the lateral LNM in both cohorts (p0.001). The nomogram that consisted of radiomic signature, US-reported cervical lymph node (CLN) status, and CT-reported CLN status demonstrated good discrimination and calibration in the training and validation sets with an AUC of 0.946 and 0.914, respectively. The decision curve analysis indicated that the radiomic nomogram was worthy of clinical application.The radiomic nomogram proposed here has good performance for noninvasively predicting the lateral LNM and might be used to facilitate clinical decision-making and potentially improve the survival outcome in selected patients.

The noise immune characteristics of chaotic systems can effectively detect useful signals in the background of strong noise. Aiming at the problem that the state of phase space is judged by human eye observation as the main means and lack of reliable theoretical support, a small sample SVM algorithm for intelligent recognition of chaotic images is proposed in this paper. Firstly, the detection flow of Duffing oscillator under the background of strong acoustic noise while drilling is given, and the weak signal detection models of different state space are given based on phase space and Lyapunov index. The simulation is carried out under different SNR and measured data. Finally, the SVM model is used to intelligently identify chaos and other state space results. The simulation results show that the chaotic system can detect the target signal effectively under different SNR. The minimum SNR is about-80dB and the recognition accuracy of SVM intelligent model is 95%. This conclusion verifies the strong noise immunity of Duffing oscillator system and the effectiveness of SVM model recognition image.

BACKGROUND: Various sonographic features of triple-negative invasive breast carcinomas (TNBC) expected to be associated with the molecular subtypes based on transcriptomic analysis were examined. The effects of clinical, sonographic, pathological, and molecular features on survival outcome was also studied. METHODS: One hundred and fourteen patients with breast cancer with negative expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) were included in our retrospective study. Based on the transcriptomic profiles, four stable clusters named immunomodulatory (IM), luminal androgen receptor (LAR), mesenchymal-like (MES), and basal-like and immune-suppressed (BLIS) were identified. Ultrasound (US) images were reviewed by two US physicians according to Breast Imaging Reporting and Data System (BI-RADS). Multivariate Cox regression was used to determine the variables associated with recurrence-free survival (RFS) and overall survival (OS). RESULTS: There were 21 IM, 18 LAR, 36 MES, and 39 BLIS cases. The four molecular subtypes showed significant differences in terms of tumor shape (P=0.008) and posterior acoustic pattern (P=0.028). Compared with the subtypes LAR and MES, the IM and BLIS subtypes had higher probability of presenting benign-like sonographic features, such as regular shape, no angular/spiculated margin, and posterior acoustic enhancement (P

B-mode ultrasound and ultrasound elastography are two routine diagnostic modalities for breast cancer. Unfortunately, few efforts have paid attention to learn bi-modal ultrasound jointly. By combining multi-view deep mapping-based feature representation with SVM-based classification, we proposed a novel integrated deep learning model, multi-view deep neural network support vector machine (MDNNSVM), to achieve breast cancer diagnosis on bi-modal ultrasound. In particular, multi-view representation learning extracts and fuses the various ultrasound characteristics (also including hardness information of soft tissue) effectively to differentiate benign breast lesions from malignant. Further, the SVM-based objective function is used to learn a classifier jointly with DNN to improve diagnostic accuracy significantly. The experimental results on a real-world dataset of breast cancer verify the effectiveness of the MDNNSVM with the best value of classification accuracy (86.36%) and AUC (0.9079).

Kai Zhang,* Qian Zhu,* Danli Sheng, Jiawei Li, Cai Chang Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Cai ChangFudan University Shanghai Cancer Center, No. 270, Dong’an Road, Xuhui District, Shanghai 200032, People’s Republic of ChinaTel +86 180 1731 2605Fax +86 21 6417 4774Email changc62@126.comPurpose: Few models with good discriminative power have been introduced to predict the risk of non-sentinel lymph node (non-SLN) metastasis in breast cancer after neoadjuvant chemotherapy (NAC). We aimed to develop a new and simple model for predicting the probability of non-SLN metastasis in breast cancer and facilitate the selection of patients who could avoid unnecessary axillary lymph node dissection following NAC.Patients and Methods: A total of 298 patients diagnosed with invasive breast cancer, who underwent SLN biopsy after completing NAC and subsequently breast surgery, were included and classified into the training set (n=228) and testing set (n=70). Univariate and multivariate analyses were used to select factors that could be determined prior to breast surgery and significantly correlated with non-SLN metastasis in the training set. A logistic regression model was developed based on these factors and validated in the testing set.Results: Nodal status before NAC, post-NAC axillary ultrasound status, SLN number, and SLN metastasis number were independent predictors of non-SLN metastases in breast cancer after NAC. A predictive model based on these factors yielded an area under the curve of 0.838 (95% confidence interval: 0.774– 0.902, p< 0.001) in the training set. When applied to the testing set, this model yielded an area under the curve of 0.808 (95% confidence interval: 0.609– 1.000, p= 0.003).Conclusion: A new and simple model, which incorporated factors that could be determined prior to breast surgery, was developed to predict non-SLN metastasis in invasive breast cancer following NAC. Although this model performed excellently in internal validation, it requires external validation before it can be widely utilized in the clinical setting.Keywords: breast carcinoma, neoadjuvant chemotherapy, sentinel lymph node, ultrasonography

Kai Zhang,* Jiawei Li,* Qian Zhu, Cai Chang Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Cai ChangFudan University Shanghai Cancer Center, No. 270, Dong’an Road, Shanghai, Xuhui District 200032, People’s Republic of ChinaTel +86 180 1731 2605Fax +86 21 6417 4774Email changc62@126.comPurpose: To compare the diagnostic performance for pathologic complete response (pCR) in breast cancer after neoadjuvant chemotherapy (NAC) between ultrasound (US) and magnetic resonance imaging (MRI).Patients and Methods: A total of 1,219 breast cancer patients with 1,232 tumors who accepted US and/or MRI examination after NAC and before breast surgery were included. The diagnostic performance of US, MRI, and US plus MRI in predicting pCR was compared.Results: The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of US for pCR were 36.2%, 90.2%, 71.0%, 67.3%, and 71.9%, respectively,while for MRI they were 44.4%, 92.9%, 75.6%, 77.7%, and 75.0%, respectively. The combination of US and MRI had increased specificity (98.0%) and PPV (86.8%), decreased sensitivity (22.5%) and NPV (68.8%), but similar accuracy (70.5%) in comparison with US or MRI alone. The prediction of pCR by imaging differed in different histological, molecular subtypes and primary tumor size.Conclusion: Neither US nor MRI could predict a pCR with sufficient accuracy. The combination of US and MRI could not predict a pCR reliably either. The explanation of imaging for pCR should take into account histological, molecular subtypes, and primary tumor size.Keywords: breast neoplasms, neoadjuvant therapy, pathologic complete response, ultrasonography, magnetic resonance imaging

Breast calcifications indicate the high possibility of malignancy in the radiological assessment of breast lesions. However, it is difficult to detect them from traditional B-mode ultrasound images due to the resolution limit and speckle noise. In this paper, we proposed a novel automatic calcification detection method based on ultrasound radio frequency (RF) signals by quantitative multi-parameter fusion. The proposed method consists of four steps: selecting the region of interest (ROI), extracting multiple features on sliding windows that traverse the entire ROI, classifying the window with or without calcifications using the Adaptive Boosting classifier, and obtaining the detection result by a threshold filter. Experiments were conducted on a database of 130 experienced doctor-proven breast tumors with calcifications. Compared to manual annotation, the proposed method achieved an average accuracy of 88%. The experiments demonstrated that our computerized RF signals feature system was capable of helping radiologists detect tumor calcifications more accurately and provided more guidance for the final decision.

Elastography ultrasound (EUS) provides additional bio-mechanical information about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality imbalance problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer learning (TL) pay little attention to this special issue of clinical modality imbalance, that is, the source domain (EUS modality) has fewer labeled samples than those in the target domain (BUS modality). Moreover, these TL methods cannot fully use the label information to explore the intrinsic relation between two modalities and then guide the promoted knowledge transfer. To this end, we propose a novel doubly supervised TL network (DDSTN) that integrates the Learning Using Privileged Information (LUPI) paradigm and the Maximum Mean Discrepancy (MMD) criterion into a unified deep TL framework. The proposed algorithm can not only make full use of the shared labels to effectively guide knowledge transfer by LUPI paradigm, but also perform additional supervised transfer between unpaired data. We further introduce the MMD criterion to enhance the knowledge transfer. The experimental results on the breast ultrasound dataset indicate that the proposed DDSTN outperforms all the compared state-of-the-art algorithms for the BUS-based CAD.

PURPOSE: To investigate breast cancers total hemoglobin concentration (THC) characteristics and its association with clinical pathologic findings. MATERIALS AND METHODS: The study was approved by the institutional review board and all patients provided written informed consent. 447 breast cancer patients, totally 455 lesions were included in our study. The size and THC of breast lesions were measured by conventional ultrasound (US) and US-guided Diffuse Optical Tomography (DOT) 1–2 days before surgery. Clinical and pathology information of patients was collected. RESULT: The average THC values of ER- or PR- lesions were significantly higher than the positive ones (P = .005 and P = .01,respectively); The average THC values of axillar LN+ or LVI+ were higher than the negative ones (P = .042 and P = .043, respectively). No significant THC difference was found in groups of infiltrating vs. non-infiltrating, HER2+ vs. HER2-, Ki67 high vs. Ki67 low, and different menstrual phases (P = .457, P = .917, P = .417, P = .213, respectively).The incidence ages and the lesion-nipple distances of T3 patients were lower than that of T1 and T2 (P

Pursuing better imaging quality and miniaturizing imaging devices are two trends in the current development of ultrasound imaging. While the first one leads to more complex and expensive imaging equipment, poor image quality is a common problem of portable ultrasound imaging systems. In this paper, an image reconstruction method was proposed to break through the imaging quality limitation of portable devices by introducing generative adversarial network (GAN) model into the field of ultrasound image reconstruction. We combined two GAN generator models, the encoder-decoder model and the U-Net model to build a sparse skip connection U-Net (SSC U-Net) to tackle this problem. To produce more realistic output, stabilize the training procedure, and improve spatial resolution in the reconstructed ultrasound images, a new loss function which combines adversarial loss, L1 loss, and differential loss was proposed. Three datasets including 50 pairs of simulation, 40 pairs of phantom, and 72 pairs of in vivo images were used to evaluate the reconstruction performance. Experimental results show that our SSC U-Net is able to reconstruct ultrasound images with improved quality. Compared with U-Net, our SSC U-Net is able to preserve more details in the reconstructed images and improve full width at half maximum (FWHM) of point targets by 3.23%.

OBJECTIVES To investigate the value of ultrasound (US) feature-based models in predicting the proliferation and invasiveness of invasive breast cancer (IBC) and to compare the performance of models based solely on US features with models that combined US features, patient age, tumor size, and axilla status from US. METHODS With ethical approval, 746 patients with a pathologic diagnosis of IBC were reviewed for preoperative clinical, US, and postoperative pathologic data. The proliferation and invasiveness properties of the IBC included the histologic grade and Ki-67 status and lymphovascular invasion (LVI) and axillary lymph node metastasis (ALNM), respectively. Logistic regression analyses were used to identify independent risk factors for tumor proliferation and invasiveness. RESULTS Posterior echo enhancement, calcification, a tumor size larger than 2 cm, and suspicion of ALNM from axillary US were independent risk factors for a high histologic grade and high Ki-67 expression of IBC (P < .05). A posterior echo shadow, patient age younger than 45 years, and suspicious findings on axillary US imaging were independent variables for predicting the presence of LVI and ALNM in IBC (P < .05). Calcification was the independent factor for predicting LVI (P = .013). The predictive performance of the combined models was improved compared with the US feature-based models, with a higher accuracy rate and negative predictive value. The area under curve of the combined models was also significantly higher than that of the single models (P < .05). CONCLUSIONS Compared with the US feature-based models, the combined models yielded better predictive performance. This may provide a more robust model to predict the tumor biological properties of IBC before surgery.

The aim of this study was to investigate the effects of Astragaloside IV (AS) on cigarette smoke (CS)-induced chronic obstructive pulmonary disease (COPD). Our results showed that AS alleviated CS-induced pathological injury in lung tissue. AS also increased superoxide dismutase (SOD) and reduced the level of malondialdehyde (MDA) in serum and lung. AS also reduced cytokines including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β) in serum and lung. More, AS significantly reduced the protein expression of JAK3/STAT3/NF-κB pathway in CS-induced mice. In vitro, cigarette smoke extract (CSE) stimulation exposed to normal human bronchial epithelial (HBE) cells. Results further confirmed that AS significantly inhibited the protein levels of JAK3/STAT3/NF-κB pathway in CSE-induced HBE. Our result showed that AS might effectively ameliorate COPD via JAK3/STAT3/NF-κB pathway.

Ultrasound measurement of the inferior vena cava (IVC) is widely implemented in the clinic. However, the process is time consuming and labor intensive, because the IVC diameter is continuously changing with respiration. In addition, artificial errors and intra-operator variations are always considerable, making the measurement inconsistent. Research efforts were recently devoted to developing semiautomated methods. But most required an initial identification of the IVC manually. As a first step toward fully automated IVC measurement, in this paper, we present an intelligent technique for automated IVC identification and localization. Forty-eight ultrasound data sets were collected from eight pigs, each of which included two frames in B-mode and color mode (C-mode) collected at the inspiration, and two cine loops in B-mode and C-mode. Static and dynamic automation algorithms were applied to the data sets for identifying and localizing the IVC. The results were evaluated by comparing with the manual measurement of experienced clinicians. The automated approaches successfully identified the IVC in 47 cases (success rate: 97.9%). The automated localization of the IVC is close to the manual counterpart, with the difference within one diameter. The automatically measured diameters are close to those measured manually, with most differences below 15%. It is revealed that the proposed method can automatically identify the IVC with high success rate and localize the IVC with high accuracy. But the study with high accuracy was conducted under good control and without considering difficult cases, which deserve future explorations. The method is a first step toward fully automated IVC measurement, which is suitable for point-of-care applications.

PURPOSE: To prospectively investigate ultrasound-guided diffuse optical tomography (US-guided DOT) in predicting breast cancer response to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Eighty-eight breast cancer patients, with a total of 93 lesions, were included in our study. Pre– and post–last chemotherapy, size and total hemoglobin concentration (THC) of each lesion were measured by conventional US and US-guided DOT 1 day before biopsy (time point t0, THC THC0, SIZE S0) and 1 to 2 days before surgery (time point tL, THCL, SL). The relative changes in THC and SIZE of lesions after the first and last NAC cycles were considered as the variables ΔTHC and ΔSIZE. Receiver operating characteristic curve was performed to calculate ΔTHC and ΔSIZE cutoff values to evaluate pathologic response of 93 breast cancers to NAC, which were then prospectively used to predicate response of 61 breast cancers to NAC. RESULTS: The cutoff values of ΔTHC and ΔSIZE for evaluation of breast cancers NAC treatment response were 23.9% and 42.6%. At ΔTHC 23.9%, the predicted treatment response in 61 breast lesions for the time points t1 to t3 was calculated by area under the curve (AUC), which were AUC 1 0.534 ( P =.6668), AUC 2 0.604 ( P =.1893), and AUC 3 0.674( P =. 0.027), respectively; for ΔSIZE 42.6%, at time points t1 to t3, AUC 1 0.505 ( P =.9121), AUC 2 0.645 ( P =.0115), and AUC 3 0.719 ( P =.0018). CONCLUSION: US-guided DOT ΔTHC 23.9% and US ΔSIZE 42.6% can be used for the response evaluation and earlier prediction of the pathological response after three rounds of chemotherapy.

The Internet of Things (IOT) is considered to be the third wave of the world information industry after the computer and the Internet. In order to realize the ubiquitous access to the IOT, the cellular network can only be fused with the wireless sensor network physical social systems. Based on this, the purpose of this paper is to extract the general functions that necessary but unrelated with the specific application in the cellular network and wireless sensor fusion technology. These general functions, including terminal registration and serial number assignment, terminal authentication management, connection management, terminal fault management, terminal state management and monitoring, congestion control, terminal dormancy and activation, and so on. Aiming at these general functions, the author gives an exploratory solution to provide a referable shared function module for the fusion of cellular network and wireless sensor networks physical social systems. It is of great significance to improve the development progress of the IOT, reduce the difficulty and cost of development.

The bimodal ultrasound, namely B-mode ultrasound (BUS) and elastography ultrasound (EUS), provide complementary information to improve the diagnostic accuracy of breast cancers. However, in clinical practice, it is easier to acquire the labeled BUS images than the paired bimodal ultrasound data with shared labels due to the lack of EUS devices, especially in many rural hospitals. Thus, the single-modal BUS-based computer-aided diagnosis (CAD) generally has wide applications. Transfer learning (TL) can promote a BUS-based CAD model by transferring additional knowledge from EUS modality. To make full use of labeled paired bimodal data and the additional single-modal BUS images for knowledge transfer, a novel doubly supervised parameter transfer classifier (DSPTC) is proposed to well handle the TL between imbalanced modalities with the guidance of label information. Specifically, the proposed DSPTC consists of two loss functions corresponding to the paired bimodal ultrasound data with shared labels and the unpaired images with different labels, respectively. The former uses the loss function in the specially designed TL paradigm of support vector machine plus, while the latter adopts the Hilbert-Schmidt Independence Criterion (HSIC) for knowledge transfer between the unpaired images, which consist of the single-modal BUS images and the EUS images from the paired bimodal data. Consequently, the doubly supervised knowledge transfer is implemented by way of parameter transfer in a unified optimization framework. Two experiments are designed to evaluate the proposed DSPTC for the ultrasound-based diagnosis of breast cancers. The experimental results indicate that DSPTC outperforms all the compared algorithms, suggesting its wide potential applications.

Accurate tumor segmentation is important for aided diagnosis using breast ultrasound. Interactive segmentation methods can obtain highly accurate results by continuously optimizing the segmentation result via user interactions. However, traditional interactive segmentation methods usually require a large number of interactions to make the result meet the requirements due to the performance limitations of the underlying model. With greater ability in extracting image information, convolutional neural network (CNN)-based interactive segmentation methods have been shown to effectively reduce the number of user interactions. In this paper, we proposed a one-stage interactive segmentation framework (interactive segmentation using weighted distance transform, WDTISeg) for breast ultrasound image using weighted distance transform and shape-aware compound loss. First, we used a pre-trained CNN to attain an initial automatic segmentation, based on which the user provided interaction points of mis-segmented areas. Then, we combined Euclidean distance transform and geodesic distance transform to convert interaction points into weighted distance maps to transfer segmentation guidance information to the model. The same CNN accepted the input image, the initial segmentation, and weighted distance maps as a concatenation input and provided a refined result, without another additional segmentation network. In addition, a shape-aware compound loss function using prior knowledge was designed to reduce the number of user interactions. In the testing phase on 200 cases, our method achieved a dice of 82.86 ± 16.22 (%) for automatic segmentation task and a dice of 94.45 ± 3.26 (%) for interactive segmentation task after 8 interactions. The results of comparative experiments proved that our method could obtain higher accuracy with fewer simple interactions than other interactive segmentation methods.

Breast calcifications in radiographic images suggest a high likelihood of breast lesion malignancy. However, it is difficult to detect calcifications in traditional B-mode ultrasound images due to resolution limits and speckle noise. In this paper, we propose a unified deep learning framework for automatic calcification detection based on multichannel ultrasound radio frequency (RF) signals. First, beamforming is used during preprocessing to merge and blend multichannel signals into one-channel RF signals. Each scan line is converted into a spectrogram by the short-time Fourier transform (STFT) to utilize the frequency domain characteristics. Then, an improved fully convolutional neural network called the RF signal Spectrogram-Calcification-Detection-Net (SCD-Net) is proposed to detect calcifications from spectrograms. This method employs a deep learning architecture based on YOLOv3 and combines features via convolutional long short-term memory (ConvLSTM). Next, a Kalman filter for tracking calcifications between consecutive spectrograms based on SCD-Net detection results is applied since the spatial coherence of calcifications in neighboring frames can be taken into account. Finally, the detected calcification is mapped from the time domain of spectrograms to B-mode images for clinical diagnosis. Experiments were conducted on a database of 337 experienced doctor-marked breast tumors with calcifications. Compared to the state-of-the-art methods for detecting calcifications, the proposed method achieved an average precision (AP) of 88.25%, an accuracy of 84% and an F1 score of 91%. The experimental results demonstrate that the unified framework has great performance for tumor calcification detection. The system can be effectively applied in a portable ultrasound instrument to accurately help radiologists and provide guidance for breast tumor diagnosis. This implies that the proposed approach can be implemented in real practice for analyzing breast RF signals, which have many useful medical applications in clinical breast tumor diagnosis.

Objectives We aimed to investigate the diagnostic performance of shear wave elastography (SWE) combined with conventional ultrasonography (US) for differentiating between benign and malignant thyroid nodules of different sizes. Methods A total of 445 thyroid nodules from 445 patients were divided into 3 groups based on diameter (group 1, ≤ 10 mm; group 2, 10–20 mm; and group 3, > 20 mm). The mean elasticity index of the whole lesion was automatically calculated, and the threshold for differentiation between benign and malignant nodules was constructed by a receiver operating characteristic curve analysis. Diagnostic performances of conventional US and SWE were compared by using pathologic results as reference standards. Results The mean elasticity was significantly higher in malignant versus benign nodules for all size groups. The differences in mean elasticity in the size groups were not statistically significant for malignant or benign nodules. The specificity of US combined with SWE for group 1 was significantly higher than that for groups 2 and 3 (77.8% versus 62.9% and 53.3%; P

PURPOSE Digital Breast Imaging Reporting and Data System (BI-RADS) features extracted from ultrasound images are essential in computer-aided diagnosis, prediction, and prognosis of breast cancer. This study focuses on the reproducibility of quantitative high-throughput BI-RADS features in the presence of variations due to different segmentation results, various ultrasound machine models, and multiple ultrasound machine settings. METHODS Dataset 1 consists of 399 patients with invasive breast cancer and is used as the training set to measure the reproducibility of features, while dataset 2 consists of 138 other patients and is a validation set used to evaluate the diagnosis performances of the final reproducible features. Four hundred and sixty high-throughput BI-RADS features are designed and quantized according to BI-RADS lexicon. Concordance Correlation Coefficient (CCC) and Deviation (Dev) are used to assess the effect of the segmentation methods and Between-class Distance (BD) is used to study the influences of the machine models. In addition, the features jointly shared by two methodologies are further investigated on their effects with multiple machine settings. Subsequently, the absolute value of Pearson Correlation Coefficient (Rabs ) is applied for redundancy elimination. Finally, the features that are reproducible and not redundant are preserved as the stable feature set. A 10-fold Support Vector Machine (SVM) classifier is employed to verify the diagnostic ability. RESULTS One hundred and fifty-three features were found to have high reproducibility (CCC > 0.9 & Dev < 0.1) within the manual and automatic segmentation. Three hundred and thirty-nine features were stable (BD < 0.2) at different machine models. Two feature sets shared the same 102 features, in which nine features were highly sensitive to the machine settings. Forty-six features were finally preserved after redundancy elimination. For the validation in dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.915. CONCLUSIONS Three factors, segmentation results, machine models, and machine settings may affect the reproducibility of high-throughput BI-RADS features to various degrees. Our 46 reproducible features were robust to these factors and were capable of distinguishing benign and malignant breast tumors.

The present study was conducted to evaluate the radiological findings, particularly the ultrasonographic (US) characteristics of sclerosing adenosis (SA), and their correlation with histopathological results. A retrospective review identified 191 patients with a total of 200 lesions histopathologically confirmed as SA following breast surgery between July 2009 and December 2012. Of the 191 patients, 145 (151 lesions) with SA as the major component were included for US and mammographic (MG) analysis. All 145 patients analyzed were female, with a mean age ± standard deviation of 46.8±7.8 years (range, 25-71 years). All 145 patients underwent US examination and the imaging findings included heterogeneously echogenic areas in 9.3% (14/151), masses in 51.7% (78/151), masses with calcifications in 13.9% (21/151), focal acoustic shadowing in 4.0% (6/151) and were negative in 21.2% (32/151) patients. Among the 119 lesions with visible abnormalities, 87.4% (104/119) were hypoechoic, 58.0% (69/119) were irregular in shape, 52.1% (62/119) had an ill-defined margin, calcifications were found in 17.6% (21/119) and 7.6% (9/119) were hypervascular, while none of the characteristics mentioned above were significantly correlated with histopathology. A total of 136 patients underwent MG at the Fudan University Shanghai Cancer Center, and the imaging findings included microcalcifications in 31.6% (43/136), masses in 23.5% (32/136), asymmetric focal density in 14.7% (20/136), focal architectural distortion in 22.8% (31/136), and were negative in 7.4% (10/136). The mass lesions were fewer on MG compared with US (23.5 vs. 65.6%, respectively). The area under the curve of US distinguishing between benign and malignant lesions was significantly larger compared with that of MG (0.547 vs. 0.497, respectively; P=0.036). In the 60 lesions that were overestimated by Breast Imaging Reporting and Data System US category, one or more characteristics of malignancy were found on US imaging. The most common finding of SA was masses with or without calcifications on US and microcalcifications on MG. The accuracy of US was limited, but higher compared with that of MG; however, SA mimicking the characteristics of malignancy may contribute to misdiagnosis with US.

OBJECTIVES We aimed to determine whether the integration of shear wave elastography (SWE) with conventional ultrasonography (US) improves diagnostic performance for suspicious thyroid lesions. METHODS For 215 thyroid lesions in 185 patients classified as Thyroid Imaging Reporting and Data System category 4 or 5 according to the findings of conventional US, SWE elasticity indices were automatically calculated. A receiver operating characteristic curve analysis was used to determine the threshold. Thyroid Imaging Reporting and Data System categories were upgraded for high-stiffness nodules and unchanged for low- and normal-stiffness nodules. The diagnostic performances were assessed and compared with histologic findings. Intraobserver and interobserver variability of SWE was assessed. RESULTS Elasticity indices were significantly higher in malignant versus benign nodules (P≤ .001). The minimum elasticity index (cutoff, 40.7 kPa) of the stiffest part combined with conventional US showed the highest area under the curve (0.774; 95% confidence interval, 0.682-0.866) but was not superior to conventional US (0.791; 95% confidence interval, 0.706-0.876; P = .48). Combined with the standard deviation of the elasticity index for the whole lesion (cutoff, 6.8 kPa), US yielded the highest sensitivity (95.5%; P < .001) and lowest specificity (42.1%; P < .001). Sensitivity increased and specificity decreased by adding any other SWE elasticity index. The intraobserver and interobserver reliability of SWE was fair to excellent according to the interclass correlation coefficients, with correlation coefficients of 0.765 to 0.846 (all P < .001). CONCLUSIONS The SWE elasticity indices of malignant thyroid nodules were significantly high. Adding SWE to conventional US did not improve diagnostic performance.