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Abstract Background Hypoxia-activated prodrug (HAP) is a promising candidate for highly tumor-specific chemotherapy. However, the oxygenation heterogeneity and dense extracellular matrix (ECM) of tumor, as well as the potential resistance to chemotherapy, have severely impeded the resulting overall efficacy of HAP. Results A HAP potentiating strategy is proposed based on ultrasound responsive nanodroplets (PTP@PLGA), which is composed of protoporphyrin (PpIX), perfluoropropane (PFP) and a typical HAP, tirapazamine (TPZ). The intense vaporization of PFP upon ultrasound irradiation can magnify the sonomechanical effect, which loosens the ECM to promote the penetration of TPZ into the deep hypoxic region. Meanwhile, the PpIX enabled sonodynamic effect can further reduce the oxygen level, thus activating the TPZ in the relatively normoxic region as well. Surprisingly, abovementioned ultrasound effect also results in the downregulation of the stemness of cancer cells, which is highly associated with drug-refractoriness. Conclusions This work manifests an ideal example of ultrasound-based nanotechnology for potentiating HAP and also reveals the potential acoustic effect of intervening cancer stem-like cells. Graphical Abstract

This article summarizes the treatment and integrated traditional Chinese and western medicine nursing experience of a patient with Guillain-Barre syndrome complicated with dissociative disorder. Through syndrome differentiation and nursing, Traditional Chinese Medicine characteristic nursing methods such as bamboo cupping therapy, moxibustion and five-tone music therapy were given according to the syndrome types and clinical symptoms, combined with psychotherapy and routine nursing interventions. The combined treatment of Traditional Chinese Medicine nursing techniques are effective to improve the muscle strength and relieve the uncomfortable symptoms of patients with Guillain-Barre syndrome complicated with dissociative disorder. (本文总结1例吉兰-巴雷综合征合并分离性障碍患者的中西医结合护理经验。基于护理评估和中医辨证, 在病情观察、生活指导、饮食护理等常规护理基础上, 采取竹罐、艾灸、五音疗法等中医护理适宜技术, 有效提高患者四肢肌力, 改善了患者不适症状。)

Abstract Background The purpose of this research is to study the sonographic and clinicopathologic characteristics that associate with axillary lymph node metastasis (ALNM) for pure mucinous carcinoma of breast (PMBC). Methods A total of 176 patients diagnosed as PMBC after surgery were included. According to the status of axillary lymph nodes, all patients were classified into ALNM group (n = 15) and non-ALNM group (n = 161). The clinical factors (patient age, tumor size, location), molecular biomarkers (ER, PR, HER2 and Ki-67) and sonographic features (shape, orientation, margin, echo pattern, posterior acoustic pattern and vascularity) between two groups were analyzed to unclose the clinicopathologic and ultrasonographic characteristics in PMBC with ALNM. Results The incidence of axillary lymph node metastasis was 8.5% in this study. Tumors located in the outer side of the breast (upper outer quadrant and lower outer quadrant) were more likely to have lymphatic metastasis, and the difference between the two group was significantly (86.7% vs. 60.3%, P = 0.043). ALNM not associated with age (P = 0.437). Although tumor size not associated with ALNM(P = 0.418), the tumor size in ALNM group (32.3 ± 32.7 mm) was bigger than non-ALNM group (25.2 ± 12.8 mm). All the tumors expressed progesterone receptor (PR) positively, and 90% of all expressed estrogen receptor (ER) positively, human epidermal growth factor receptor 2 (HER2) were positive in two cases of non-ALNM group. Ki-67 high expression was observed in 36 tumors in our study (20.5%), and it was higher in ALNM group than non-ALNM group (33.3% vs. 19.3%), but the difference wasn’t significantly (P = 0.338). Conclusions Tumor location is a significant factor for ALNM in PMBC. Outer side location is more easily for ALNM. With the bigger size and/or Ki-67 higher expression status, the lymphatic metastasis seems more likely to present.

ObjectiveThe objective of this study was to develop a deep learning-and-radiomics-based ultrasound nomogram for the evaluation of axillary lymph node (ALN) metastasis risk in breast cancer patients ≥ 75 years.MethodsThe study enrolled breast cancer patients ≥ 75 years who underwent either sentinel lymph node biopsy or ALN dissection at Fudan University Shanghai Cancer Center. DenseNet-201 was employed as the base model, and it was trained using the Adam optimizer and cross-entropy loss function to extract deep learning (DL) features from ultrasound images. Additionally, radiomics features were extracted from ultrasound images utilizing the Pyradiomics tool, and a Rad-Score (RS) was calculated employing the Lasso regression algorithm. A stepwise multivariable logistic regression analysis was conducted in the training set to establish a prediction model for lymph node metastasis, which was subsequently validated in the validation set. Evaluation metrics included area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. The calibration of the model’s performance and its clinical prediction accuracy were assessed using calibration curves and decision curves respectively. Furthermore, integrated discrimination improvement and net reclassification improvement were utilized to quantify enhancements in RS.ResultsHistological grade, axillary ultrasound, and RS were identified as independent risk factors for predicting lymph node metastasis. The integration of the RS into the clinical prediction model significantly improved its predictive performance, with an AUC of 0.937 in the training set, surpassing both the clinical model and the RS model alone. In the validation set, the integrated model also outperformed other models with AUCs of 0.906, 0.744, and 0.890 for the integrated model, clinical model, and RS model respectively. Experimental results demonstrated that this study’s integrated prediction model could enhance both accuracy and generalizability.ConclusionThe DL and radiomics-based model exhibited remarkable accuracy and reliability in predicting ALN status among breast cancer patients ≥ 75 years, thereby contributing to the enhancement of personalized treatment strategies’ efficacy and improvement of patients’ quality of life.

BackgroundPleomorphic adenoma (PA), often with the benign-like imaging appearances similar to Warthin tumor (WT), however, is a potentially malignant tumor with a high recurrence rate. It is worse that pathological fine-needle aspiration cytology (FNAC) is difficult to distinguish PA and WT for inexperienced pathologists. This study employed deep learning (DL) technology, which effectively utilized ultrasound images, to provide a reliable approach for discriminating PA from WT.Methods488 surgically confirmed patients, including 266 with PA and 222 with WT, were enrolled in this study. Two experienced ultrasound physicians independently evaluated all images to differentiate between PA and WT. The diagnostic performance of preoperative FNAC was also evaluated. During the DL study, all ultrasound images were randomly divided into training (70%), validation (20%), and test (10%) sets. Furthermore, ultrasound images that could not be diagnosed by FNAC were also randomly allocated to training (60%), validation (20%), and test (20%) sets. Five DL models were developed to classify ultrasound images as PA or WT. The robustness of these models was assessed using five-fold cross-validation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was employed to visualize the region of interest in the DL models.ResultsIn Grad-CAM analysis, the DL models accurately identified the mass as the region of interest. The area under the receiver operating characteristic curve (AUROC) of the two ultrasound physicians were 0.351 and 0.598, and FNAC achieved an AUROC of only 0.721. Meanwhile, for DL models, the AUROC value for discriminating between PA and WT in the test set was from 0.828 to 0.908. ResNet50 demonstrated the optimal performance with an AUROC of 0.908, an accuracy of 0.833, a sensitivity of 0.736, and a specificity of 0.904. In the test set of cases that FNAC failed to provide a diagnosis, DenseNet121 demonstrated the optimal performance with an AUROC of 0.897, an accuracy of 0.806, a sensitivity of 0.789, and a specificity of 0.824.ConclusionFor the discrimination of PA and WT, DL models are superior to ultrasound and FNAC, thereby facilitating surgeons in making informed decisions regarding the most appropriate surgical approach.

In clinical practice, we noticed that triple negative breast cancer (TNBC) patients had higher shear-wave elasticity (SWE) stiffness than non-TNBC patients and a higher α-SMA expression was found in TNBC tissues than the non-TNBC tissues. Moreover, SWE stiffness also shows a clear correlation to neoadjuvant response efficiency. To elaborate this phenomenon, TNBC cell membrane-modified polylactide acid-glycolic acid (PLGA) nanoparticle was fabricated to specifically deliver artesunate to regulate SWE stiffness through inhibiting CAFs functional status. As tested in MDA-MB-231 and E0771 orthotopic tumor models, CAFs functional status inhibited by 231M-ARS@PLGA nanoparticles (231M-AP NPs) had reduced the SWE stiffness as well as attenuated hypoxia of tumor as tumor soil loosening agent which amplified the antitumor effects of paclitaxel and PD1 inhibitor. Single-cell sequencing indicated that the two main CAFs (extracellular matrix and wound healing CAFs) that produces extracellular matrix could influence the tumor SWE stiffness as well as the antitumor effect of drugs. Further, biomimetic nanoparticles inhibited CAFs function could attenuate tumor hypoxia by increasing proportion of inflammatory blood vessels and oxygen transport capacity. Therefore, our finding is fundamental for understanding the role of CAFs on affecting SWE stiffness and drugs antitumor effects, which can be further implied in the potential clinical theranostic predicting in neoadjuvant therapy efficacy through non-invasive analyzing of SWE imaging.

The current use of elastography ultrasound faces challenges, including vulnerability to subjective manipulation, echo signal attenuation, unknown risks of elastic pressure and high imaging hardware cost. Here, the author shows a virtual elastography to empower low-end ultrasound devices with state-of-art elastography function.

Objective: The data regarding the mutation landscape in Chinese patients with thyroid cancer are limited. The diagnostic performance of thyroid nodul es by fine-needle aspiration (FNA) cytology needs optimization, especially in indeterminate nodules. Methods: A total of 1039 FNA and surgical resection samples tested using the targeted multigene next-generation sequencing (NGS) panel were retrospectively collected. The features of gene alterations in different thyroid tumors wer e analyzed, and the diagnostic efficacy was evaluated. Results: Among 1039 samples, there were 822 FNA and 217 surgical FFPE samples. Among 207 malignant thyroid resections, a total of 181 out of 193 papillary thyroid carcinomas (PTCs) were NGS-positive (93.8%), with a high prevalence of BRAF mutations (81.9%, 158/193) and a low prevalence of RAS (1.0%, 2/193) and TERT promoter mutations (3.6%, 7/193). Gene fusions, involving the RET and NTRK3 genes, were present in 20 PTCs (10.4%) and mutually exclusive with other driver mutations. Two of three follicular thyroid carcinomas harbored multiple mutations. RET gene point mutations were common in medullary thyroid carcinoma (8/11, 72.7%). The combination of cytology and DNA–RNA-based NGS analysis demonstrated superior diagnostic value (98.0%) in FNA samples. For indeterminate thyroid nodules, the diagnostic sens itivity and specificity of NGS testing were 79.2 (38/48) and 80.0% (8/10), respectively. Two mutation-positive benign cases harbored NRAS and TSHR mutations, respectively. Conclusions: Our study revealed the distinct molecular profile of thyroid tu mors in the Chinese population. The combination of NGS testing and FNA cytology could facilitate the accurate diagnosis of thyroid nodules, especially for indeterminate nodules.

Abstract Background Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists. Methods A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods. Results The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p

Abstract Background An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. Methods A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. Results The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. Conclusion The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

PurposeThe detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for breast cancer patients and to improving their prognosis. We developed a deep learning radiomics (DLR) model combining time-frequency domain features of ultrasound (US) video of breast lesions with clinical parameters for predicting HER2 expression status.Patients and MethodsData for this research was obtained from 807 breast cancer patients who visited from February 2019 to July 2020. Ultimately, 445 patients were included in the study. Pre-operative breast ultrasound examination videos were collected and split into a training set and a test set. Building a training set of DLR models combining time-frequency domain features and clinical features of ultrasound video of breast lesions based on the training set data to predict HER2 expression status. Test the performance of the model using test set data. The final models integrated with different classifiers are compared, and the best performing model is finally selected.ResultsThe best diagnostic performance in predicting HER2 expression status is provided by an Extreme Gradient Boosting (XGBoost)-based time-frequency domain feature classifier combined with a logistic regression (LR)-based clinical parameter classifier of clinical parameters combined DLR, particularly with a high specificity of 0.917. The area under the receiver operating characteristic curve (AUC) for the test cohort was 0.810.ConclusionOur study provides a non-invasive imaging biomarker to predict HER2 expression status in breast cancer patients.

Abstract 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

Abstract Background In comparison with traditional therapeutics, it is highly preferable to develop a combinatorial therapeutic modality for nanomedicine and photothermal hyperthermia to achieve safe, efficient, and localized delivery of chemotherapeutic drugs into tumor tissues and exert tumor-activated nanotherapy. Biocompatible organic–inorganic hybrid hollow mesoporous organosilica nanoparticles (HMONs) have shown high performance in molecular imaging and drug delivery as compared to other inorganic nanosystems. Disulfiram (DSF), an alcohol-abuse drug, can act as a chemotherapeutic agent according to its recently reported effectiveness for cancer chemotherapy, whose activity strongly depends on copper ions. Results In this work, a therapeutic construction with high biosafety and efficiency was proposed and developed for synergistic tumor-activated and photothermal-augmented chemotherapy in breast tumor eradication both in vitro and in vivo. The proposed strategy is based on the employment of HMONs to integrate ultrasmall photothermal CuS particles onto the surface of the organosilica and the molecular drug DSF inside the mesopores and hollow interior. The ultrasmall CuS acted as both photothermal agent under near-infrared (NIR) irradiation for photonic tumor hyperthermia and Cu2+ self-supplier in an acidic tumor microenvironment to activate the nontoxic DSF drug into a highly toxic diethyldithiocarbamate (DTC)-copper complex for enhanced DSF chemotherapy, which effectively achieved a remarkable synergistic in-situ anticancer outcome with minimal side effects. Conclusion This work provides a representative paradigm on the engineering of combinatorial therapeutic nanomedicine with both exogenous response for photonic tumor ablation and endogenous tumor microenvironment-responsive in-situ toxicity activation of a molecular drug (DSF) for augmented tumor chemotherapy. Graphical abstract

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

Inspired by the distinct metabolic pathway of tumor cells, here a new anti-cancer therapy (i.e., Cu-TCPP(Fe) metal-organic frameworks (MOFs) with sonosensitizer for sonodynamic therapy (SDT) and tumor microenvironment (TME)-specific release of copper/iron for chemical catalysis) is introduced and verified. Upon exposure to ultrasound, the porphyrin structure in the MOFs yields reactive oxygen species (ROS), and the copper/iron induces the Fenton reaction in the TME existing H2O2 and acid. Thus, highly toxic ROS are generated through these TME-specific reactions to trigger the apoptosis and death of tumor cells. The current work paves a new avenue to design SDT with catalytic nanomedicine for the selective and effective treatment of tumors.

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.

2.5D shallow cross-linked Cf/Al composites with volume fraction of 50% were fabricated by vacuum pressure infiltration. The microstructure, bending and shear properties at room and high temperature were studied. The failure mechanism of bending and shear properties of the composites were analyzed. The results indicate that the warp and weft microstructures of 2.5D shallow cross-linked Cf/Al composites have some defects such as microvoid and fiber aggregation. The bending strength, bending modulus and shear strength of the composites at room temperature are 268.4 MPa,75.2 GPa and 41.0 MPa,respectively. The bending strength, bending modulus and shear strength of the composites at 350 ℃ are 139 MPa,70.9 GPa and 39.2 MPa,respectively. The bending strength,bending modulus and shear strength of the composites at 400 ℃ are 97.6 MPa,68.5 GPa and 29.9 MPa,respectively.The bending failure is mainly caused by the compressive stress on the inner surface, which leads to press fracture of the warp fiber bundle and the extrusion deformation of the weft fiber bundle. Tensile failure of composites is not obvious with the increase of testing temperature at the outer side tension. Shear failure occurs first in the damage interface of the matrix and fiber bundle, the fiber bundle is pulled out and the fracture surface is not at the same level at room temperature, the fracture of the fiber bundle shows 45° failure at 350,400 ℃.The buckling of the warp fiber bundle and the extrusion deformation degree of the weft fiber bundle become more and more serious with the increase of the test temperature.

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

Objectives: To study the role of thrombospondin-1 (THBS1) in papillary thyroid cancer (PTC) prognosis and the immune microenvironment. Methods: A retrospective cohort study was designed, and data from The Cancer Genome Atlas database and PTC tissues from Fudan University Shanghai Cancer Center were used. Weighted gene co-expression network analysis was performed to build a THBS1 -immune-related gene prognostic index (T-I index). Results: High THBS1 expression was correlated with advanced TNM stage, higher recurrence risk, and shorter progression-free interval. High THBS1 expression correlated with MAPK and PD1 pathways indicating a tumor promoting and immunity-inhibiting tendency. The T-I index showed a powerful capacity to predict progression-free survival and immunotherapy benefit. Conclusion: High expression of THBS1 leads to a poor prognosis in PTCs and suppresses the anti-tumor immune microenvironment.

PurposeNo non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis.Patients and MethodsConsecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1st, 2016 and June 30th, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set.ResultsA total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, BRAF mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 (95%CI: 0.905-0.960, p < 0.001), 0.816, 0.966 and 0.914 in the training set and 0.967 (95%CI: 0.943-0.991, p < 0.001), 0.897, 0.959 and 0.936 in the internal validation set.ConclusionAge, BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.

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.

Abstract 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%.

The binding energies, electronic structures and mechanical properties of M doped in α-Fe(N) system were investigated by using first principle calculations, where M represents Ti, V, Cr, Mn, Co,and Ni. The results show that Ti and V occupy preferentially the corner of α-Fe(N) unit cell, Cr and Mn occupy preferentially the body-centered position of the cell, the structure is the most stable when Co and Ni are not adjacent to the N atom. The doped elements Ti and V strengthen the stability of α-Fe(N); Cr, Mn and Ni have an opposite effect; Co does not affect the stability. The metallic and ionic bonds coexist in the doped systems. The bonding orbitals are created by the interaction of M3d, Fe4s3p3d and N2p. Compared with the pure α-Fe system, the material rigidity is stronger for doped systems. The calculation results show that the elastic modulus E,shear modulus G and bulk modulus B of α-Fe(N)-V are the maximum. It reveals that doped V can significantly improve the overall mechanical properties of steel and it is the most effective nitrogen fixing element. It is consistent with the experimental results of high nitrogen steel smelting.

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’ (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 (P

PurposeThe 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 MethodsCore 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.ResultsA 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

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.

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

Abstract 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

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 AUC1 0.534 (P = .6668), AUC2 0.604 (P = .1893), and AUC3 0.674(P =. 0.027), respectively; for ΔSIZE 42.6%, at time points t1 to t3, AUC1 0.505 (P = .9121), AUC2 0.645 (P = .0115), and AUC3 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.

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.