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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.

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

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

ObjectiveTo explore the feasibility of shear wave elastography (SWE) parameters for assessing the biological behavior of breast cancer.Materials and MethodsIn this prospective study, 224 breast cancer lesions in 216 female patients were examined by B-mode ultrasound and shear wave elastography in sequence. The maximum size (Smax) of the lesion was measured by B-mode ultrasound, and then shear wave elastography was performed on this section to obtain relevant parameters, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation of elasticity (SD), and the area ratio of shear wave elastography to B-mode ultrasound (AR). The relationship between SWE parameters and pathological type, histopathological classification, histological grade, lymphovascular invasion status (LVI), axillary lymph node status (ALN), and immunohistochemistry of breast cancer lesions was performed according to postoperative pathology.ResultsIn the univariate analysis, the pathological type and histopathological classification of breast cancer were not significantly associated with SWE parameters; with an increase in the histological grade of invasive ductal carcinoma (IDC), SD (p = 0.016) and Smax (p = 0.000) values increased. In the ALN-positive group, Smax (p = 0.004) was significantly greater than in the ALN-negative group; Smax (p = 0.003), Emax (p = 0.034), and SD (p = 0.045) were significantly higher in the LVI-positive group than in the LVI-negative group; SD (p = 0.043, p = 0.047) and Smax (p = 0.000, p = 0.000) were significantly lower in the ER+ and PR+ groups than in the ER- and PR- groups, respectively; AR (p = 0.032) was significantly higher in the ER+ groups than in the ER- groups, and Smax (p = 0.002) of the HER2+ group showed higher values than that of the HER2- group; Smax (p = 0.000), SD (p = 0.006), and Emax (p = 0.004) of the Ki-67 high-expression group showed significantly higher values than those of the Ki-67 low-expression group. In the multivariate analysis, Ki-67 was an independent factor of Smax (p = 0.005), Emax (p = 0.004), and SD (p = 0.006); ER was an independent influencing factor of Smax (p = 0.000) and AR (p = 0.032). LVI independently influences Smax (p = 0.006).ConclusionsThe SWE parameters Emax, SD, and AR can be used to evaluate the biological behavior of breast cancer.

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.

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.

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,* 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.

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.

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.

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.

B-mode ultrasound (BUS) imaging is widely used for diagnosis of breast tumors. In recent years, another ultrasound modality, namely ultrasound elastography (UE), has also been successfully applied in clinical practice. Although the combination of bimodal ultrasound imaging can improve the diagnosis accuracy for breast tumors, the single-modal ultrasound-based diagnosis is more popular in clinical practice, especially in the rural areas. Since transfer learning (TL) can transfer knowledge from a source domain to a target domain to help train more effective model, we propose to develop a single-modal ultrasound-based computer-aided diagnosis (CAD) with the transferred information from an additional modality in this work. We propose a projective model (PM) based multilayer kernel extreme learning machine (ML-KELM-PM) algorithm, which performs the parameter transfer approach in classifier to perform TL in CAD. The experimental results on a bimodal ultrasound image dataset show that the proposed ML-KELM-PM algorithm outperforms all the compared algorithms for the single-modal ultrasound-based CAD for breast tumors.

Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808-0.824) in the primary cohort and 0.858 (95% CI, 0.849-0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785-0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.

Xi Lin,1 Cai Chang,2,3 Changjun Wu,4 Qin Chen,5 Yulan Peng,6 Baoming Luo,7 Lina Tang,8 Jing Li,9 Jihui Zheng,10 Ruhai Zhou,11 Guanghe Cui,12 Ao Li,13 Xuemei Wang,14 Linxue Qian,15 Jianxing Zhang,16 Chaoyang Wen,17 Joel Gay,18 Huili Zhang,18 Anhua Li,1 Yaling Chen2,3 1State Key Laboratory of Oncology in Southern China, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China; 2Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai 200032, P. R. China; 3Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P. R. China; 4Department of Ultrasound, First Affiliated Hospital of Harbin Medical University, Harbin 150001, P. R. China; 5Department of Ultrasound, Sichuan Academy of Medical Sciences, Sichuan Provincial People’s Hospital, Chengdu 610072, P. R. China; 6Department of Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, P. R. China; 7Department of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510235, P. R. China; 8Department of Ultrasound, Fujian Provincial Cancer Hospital, Fuzhou 350014, P. R. China; 9Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang 110004, P. R. China; 10Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, P. R. China; 11Department of Ultrasound, Yinzhou Hospital Affiliated to Medical School of Ningbo University, Ningbo 315040, P. R. China; 12Department of Ultrasound, Binzhou Medical University Hospital, Binzhou 256603, P. R. China; 13Department of Ultrasound, Jiangsu Province Hospital, Nanjing 210029, P. R. China; 14Department of Ultrasound, The First Hospital of China Medical University, Shenyang 110001, P. R. China; 15Department of Ultrasound, Beijing Friendship Hospital of Capital Medical University, Beijing 100050, P. R. China; 16Department of Ultrasound, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510120, P. R. China; 17Department of Ultrasound, First Affiliated Hospital of PLA General Hospital, Beijing 100037, P. R. China; 18Supersonic Imagine, Aix-en-Provence, France Purpose: This study aimed to investigate the value of shear wave elastography (SWE) for characterization of breast masses in a Chinese population. Patients and methods: Two thousand two hundred seventy-three women consented to be prospectively enrolled for the characterization of breast masses with ultrasound and SWE. Breast masses were known from symptoms, palpability, and/or previous imaging screening with mammography and/or ultrasound. Correlation of SWE qualitative and quantitative features with malignancy risk and impact on diagnostic performance of combining SWE features were assessed, and the Breast Imaging Reporting and Data System (BI-RADS) scoring was calculated using histopathology as reference. Results: Data of 2,262 masses (median size: 13 mm; range: 1.3–50) from 2,262 patients (median age: 43 years; range: 18–91) were investigated, of which 752 (33.3%) were malignant. Sensitivity and specificity of BI-RADS diagnosis were 97.5% (733/752) and 54.8% (827/1,510), respectively. By logistic regression, the combination of maximum elasticity (Emax) measurements with BI-RADS assessments increased the area under the receiver operating characteristic curve from 0.908 (95% CI: 0.896–0.920) to 0.954 (95% CI: 0.944–0.962). Using Emax of 30 kPa or lower to selectively downgrade BI-RADS 4a masses to follow-up, and Emax of 160 kPa or higher to selectively upgrade BI-RADS 3 lesions to biopsy, specificity significantly increased from 54.8% (827/1,510) to 66.1% (998/1,510) (P

Background To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery. Methods Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 2:1 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to estimate the performance of the predictive models. The robustness of the models was evaluated by a 3-fold cross-validation. Results Clinical features were not significantly different between the training set and the test set (P value >0.05). The area under the receiver operating characteristic curve of our models ranged from 0.724 to 0.804. The sensitivity, specificity, and accuracy of the optimal model were 0.733, 0.750, and 0.742, respectively. The three-fold cross-validation results showed that the model was very robust. Conclusions The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.

In this study, we aimed to evaluate the clinical and pathological factors that associated with sonographic appearances of triple-negative (TN) invasive breast carcinoma. With the ethical approval, 560 patients who were pathologically confirmed as invasive breast carcinoma were reviewed for ultrasound, clinical, and pathological data. Logistic regression analysis was used to identify the typical sonographic features for TN invasive breast carcinomas. The effect of clinical and pathological factors on the sonographic features of TN invasive breast carcinoma was studied. There were 104 cases of TN invasive breast carcinoma. The independent sonographic features for the TN subgroup included regular shape (odds ratio, OR = 1.73, p = 0.033), no spiculated/angular margin (OR = 2.09, p = 0.01), posterior acoustic enhancement (OR = 2.09, p = 0.004), and no calcifications (OR = 2.11, p = 0.005). Higher pathological grade was significantly associated with regular tumor shape of TN breast cancer (p = 0.012). Higher Ki67 level was significantly associated with regular tumor shape (p = 0.023) and absence of angular/spiculated margin (p = 0.005). Higher human epidermal growth factor receptor 2 (HER2) score was significantly associated with the presence of calcifications (p = 0.033). We conclude that four sonographic features are associated with TN invasive breast carcinoma. Heterogeneity of sonographic features was associated with the pathological grade, Ki67 proliferation level and HER2 score of TN breast cancers.

Background Ultrasound shear-wave elastography (SWE) may increase specificity of breast lesion assessment with ultrasound, but elasticity measurements may change with transducer orientation, defined as anisotropy. In this study, we aimed to observe the anisotropy of SWE of breast lesions, and its correlation with clinical and histopathological findings. Methods This retrospective study was approved by institutional review board. From June 2014 to June 2015, a total of 276 women (mean age, 48.75 ± 12.12 years) with 276 breast lesions (174 malignant, 102 benign) were enrolled for conventional ultrasound and SWE before surgical excision. Elasticity modulus in the longest diameter and orthogonal diameter were recorded, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation (Esd) and ratio between mean elasticity of lesion and normal fatty tissue (Eratio). Anisotropy coefficients including anisotropic difference (AD) and anisotropy factors (AF) were calculated, and correlations with malignancy, tumor size, palpability, movability, lesion location and histopathology were analyzed. Results The average Emax, Emean, Esd and Eratio of the longest diameter were significantly higher than orthogonal diameter (P

In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and medical outcomes. To better reveal the underlying biology, an improved understanding between objective image features and biologic characteristics is urgently required.A total of 215 patients with definite histologic results were enrolled in our study. The tumors were automatically segmented using our phase-based active contour model. The high-throughput radiomics features were designed and extracted using a breast imaging reporting and data system and further selected using Student's t test, interfeature coefficients and a lasso regression model. The support vector machine classifier with threefold cross-validation was used to evaluate the relationship.The radiomics approach demonstrated a strong correlation between receptor status and subtypes (P .05; area under the curve, 0.760). The appearance of hormone receptor-positive cancer and human epidermal growth factor receptor 2-negative cancer on ultrasound scans differs from that of triple-negative cancer.Our approach could assist clinicians with the accurate prediction of prognosis using ultrasound findings, allowing for early medical management and treatment.

Objective To establish an ultrasound (US) radimoics nomogram for predicting central neck lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). Method A training cohort of 300 patients and validation cohort of 143 patients were established from January to December 2017. US radiomics score was built by using radiomics to analyze ultrasound image of PTC lesion. A nomogram was established by logistic regression from selection of gender, age, thyroglobulin (TG), thyroglobulin antibodies (TGAB), thyroid peroxidase antibody (TPOAB), US radiomics score and US report LNM status. C-index was calculated to evaluate discrimination of this nomogram. Also, calibration curve was drawn to evaluate calibration of it. Results The positive rates of LNM in two cohorts were 29.7% and 34.6%, respectively. In this study, 23 predictive US radiomics features (26:1) were screened out from 609 cases in the training set, which were nonzero coefficients in the LASSO logistic regression model. US radiomics score yield a C-index of 0.802 (95% CI, 0.791 to 0.815) in training cohort and 0.809 (95% CI, 0.801 to 0.816). Through binary logistic regression, indexes were filtered as age, TPOAB, US radiomics score and US report LNM status. The area under curve, accuracy, sensitivity and specificity of this nomogram was 0.801, 0.812, 0.628 and 0861, respectively, from validation cohort. The C-index was 0.811 (95% CI, 0.804 to 0.818). Conclusion US radiomics nomogram can predict LNM in PTC effectively.

Background: Papillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images. Methods: Four hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model. Results: Among 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710. Conclusion: Papillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.