Contrast-enhanced ultrasound image sequences based on radiomics analysis for diagnosis of metastatic cervical lymph nodes from thyroid cancer.

Background: Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ultrasonography (US) is the first-line imaging method for evaluating the thyroid gland and CLNs. However, this assessment relies mainly on the subjective judgment of the sonographer and is very much dependent on the sonographer's experience. This prospective study was designed to construct a machine learning model based on contrast-enhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis in patients with TC. Methods: Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2-minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lymph node imaging video, three machine learning models, namely, support vector machine, linear discriminant analysis (LDA), and decision tree (DT), were constructed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). Results: A total of 75 lymph nodes were included in the study, with 42 benign cases and 33 malignant cases. Among the machine learning models constructed, the support vector machine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%. Conclusions: The machine learning model based on US video is helpful for the diagnosis of whether metastasis occurs in the CLNs of TC patients.

Diagnosis of bilateral pharyngoesophageal diverticula with using swallow contrast-enhanced ultrasound: a case report.

Pharyngoesophageal diverticulum (PED) is a rare disease of the esophagus that is usually asymptomatic and often found incidentally during a thyroid ultrasound examination. Due to its anatomical location close to the thyroid, it is easily misdiagnosed as a thyroid nodule, which leads to unnecessary thyroid biopsies and surgical treatment. The occurrence of a single esophageal diverticula is common, while the existence of multiple diverticula is rare. Left side diverticula are more common than right sided ones, while bilateral occurrences are rarely reported. We report an extremely rare case of bilateral pharyngeal esophageal diverticula. The patient was a 55-year-old asymptomatic man who came to our hospital after thyroid nodules were identified in another hospital. Due to the extensive clinical experience of the ultrasound physician at our facility, the patient was suspected to have bilateral esophageal diverticula, which was confirmed by using swallow contrast-enhanced ultrasound (CEUS). Consequently, unnecessary thyroid treatments were avoided in this patient. This study shows that although bilateral pharyngeal diverticula are unusual, the possibility of their existence should be considered if nodules are located posterior to the bilateral thyroid glands and have suspicious imaging characteristics. Particular attention should be given to nodules located on the right side of the thyroid, which are sometimes overlooked easily due to their very low incidence. If real-time ultrasound cannot be used in making the diagnosis, PED can be further identified using swallowing CEUS to avoid unnecessary thyroid fine needle aspiration (FNA) and surgical treatment.

Contrast-enhanced ultrasound findings of primary hepatic non-Hodgkin's lymphoma: a case report.

Primary hepatic lymphoma (PHL) is rare, and its early diagnosis is difficult. This article presents a primary hepatic non-Hodgkin's lymphoma (NHL) case report. A 52-year-old woman was admitted to the hospital due to a fever. After undergoing laboratory examination, contrast-enhanced computed tomography (CT), ultrasound, and contrast-enhanced ultrasound (CEUS), only CEUS suggested malignancy. Then, the patient underwent a laparoscopic liver biopsy, which diagnosed NHL. Previous studies have shown that hepatic lymphoma is a hypoglycemic tumor, and the enhanced CT and magnetic resonance imaging (MRI) scans are mostly mildly intensified. At the same time, the two-dimensional and color Doppler ultrasonography are mostly atypical. CEUS has unique advantages in displaying micro-vessels, which can be helpful in the diagnosis of primary hepatic lymphoma.

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ.

Background: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods: The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results: In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions: Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Diagnostic value of contrast-enhanced ultrasound in the diagnosis of papillary thyroid microcarcinoma: A systematic review and meta-analysis.

BACKGROUND: Using meta-analysis to evaluate the diagnostic value of contrast-enhanced ultrasound (CEUS) in the diagnosis of papillary thyroid microcarcinoma (PTMC). METHODS: For this systematic review and meta-analysis, we searched PubMed, Cochrane Library, Web of Science, WanFang Data, VPCS Data, and China National Knowledge Infrastructure electronic databases for diagnostic studies on PTMC by CEUS from January 2013 to November 2022. Data were not available or incomplete such as case reports, nonhuman studies, etc, were excluded. Random-effects meta-analyses were used to evaluate the diagnostic accuracy of CEUS in diagnosing PTMC. The quality of the evidence was assessed with the QUADAS-2 scale. This study is registered on PROSPERO, number CRD42023409417. RESULTS: Of 1064 records identified, 33 were eligible. The results showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CEUS in diagnosing PTMC were 0.84 (95% confidence interval [CI] = 0.83-0.86), 0.82 (95% CI = 0.80-0.83), 3.90 (95% CI = 3.23-4.72), 0.21 (95% CI = 0.18-0.25), and 20.01 (95% CI = 14.97-26.74), respectively, and the area under the summary receiver operating characteristic curve was 0.8930 (the Q index was 0.8239). The Deek funnel plot indicated publication bias (P ˂.01). CONCLUSION: This meta-analysis provides an overview of diagnostic accuracy of CEUS in diagnosing PTMC which indicates CEUS has a good diagnostic value for PTMC. The limitations of this study are publication bias and strong geographical bias.

Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial.

OBJECTIVE: To reduce the number of biopsies performed on benign breast lesions categorized as BI-RADS 4-5, we investigated the diagnostic performance of combined two-dimensional and three-dimensional shear wave elastography (2D + 3D SWE) with standard breast ultrasonography (US) for the BI-RADS assessment of breast lesions. METHODS: A total of 897 breast lesions, categorized as BI-RADS 3-5, were subjected to standard breast US and supplemented by 2D SWE only and 2D + 3D SWE analysis. Based on the malignancy rate of less than 2% for BI-RADS 3, lesions assessed by standard breast US were reclassified with SWE assessment. RESULTS: After standard breast US evaluation, 268 (46.1%) participants underwent benign biopsies in BI-RADS 4-5 lesions. By using separated cutoffs for upstaging BI-RADS 3 at 120 kPa and downstaging BI-RADS 4a at 90 kPa in 2D + 3D SWE reclassification, 123 (21.2%) participants underwent benign biopsy, resulting in a 54.1% reduction (123 versus 268). CONCLUSION: Combining 2D + 3D SWE with standard breast US for reclassification of BI-RADS lesions may achieve a reduction in benign biopsies in BI-RADS 4-5 lesions without sacrificing sensitivity unacceptably. CLINICAL RELEVANCE STATEMENT: Combining 2D + 3D SWE with US effectively reduces benign biopsies in breast lesions with categories 4-5, potentially improving diagnostic accuracy of BI-RADS assessment for patients with breast lesions. TRIAL REGISTRATION: ChiCTR1900026556 KEY POINTS: • Reduce benign biopsy is necessary in breast lesions with BI-RADS 4-5 category. • A reduction of 54.1% on benign biopsies in BI-RADS 4-5 lesions was achieved using 2D + 3D SWE reclassification. • Adding 2D + 3D SWE to standard breast US improved the diagnostic performance of BI-RADS assessment on breast lesions: specificity increased from 54 to 79%, and PPV increased from 54 to 71%, with slight loss in sensitivity (97.2% versus 98.7%) and NPV (98.1% versus 98.7%).

Correlation between Baseline Conventional Ultrasounds, Shear-Wave Elastography Indicators, and Neoadjuvant Therapy Efficacy in Triple-Negative Breast Cancer.

In patients with triple-negative breast cancer (TNBC)-the subtype with the poorest prognosis among breast cancers-it is crucial to assess the response to the currently widely employed neoadjuvant treatment (NAT) approaches. This study investigates the correlation between baseline conventional ultrasound (US) and shear-wave elastography (SWE) indicators and the pathological response of TNBC following NAT, with a specific focus on assessing predictive capability in the baseline state. This retrospective analysis was conducted by extracting baseline US features and SWE parameters, categorizing patients based on postoperative pathological grading. A univariate analysis was employed to determine the relationship between ultrasound indicators and pathological reactions. Additionally, we employed a receiver operating characteristic (ROC) curve analysis and multivariate logistic regression methods to evaluate the predictive potential of the baseline US indicators. This study comprised 106 TNBC patients, with 30 (28.30%) in a nonmajor histological response (NMHR) group and 76 (71.70%) in a major histological response (MHR) group. Following the univariate analysis, we found that T staging, dmax values, volumes, margin changes, skin alterations (i.e., thickening and invasion), retromammary space invasions, and supraclavicular lymph node abnormalities were significantly associated with pathological efficacy (p < 0.05). Combining clinical information with either US or SWE independently yielded baseline predictive abilities, with AUCs of 0.816 and 0.734, respectively. Notably, the combined model demonstrated an improved AUC of 0.827, with an accuracy of 76.41%, a sensitivity of 90.47%, a specificity of 55.81%, and statistical significance (p < 0.01). The baseline US and SWE indicators for TNBC exhibited a strong relationship with NAT response, offering predictive insights before treatment initiation, to a considerable extent.

Comparative Pollen Morphology of Selected Species of Blumea DC. and Cyathocline Cass. and Its Taxonomic Significance.

The pollen morphology of 20 species from Blumea and Cyathocline Cass. was investigated using a light microscope (LM) and scanning electron microscopy (SEM) to explore their taxonomic significance. This study showed that pollen grains of these species were usually tricolporate, rarely tetracolporate (B. sinuata). Nine pollen types were distinguishable through the exine sculpture characters and the number of apertures. It was easily distinguished Cyathocline from species of Blumea s. str. by its much smaller size (15.04 µm × 15.07 µm) and sparse and longer spines (24 spines, spine length 4.23 µm) with acute apex, which suggest that C. purpurea might not belong to the genus Blumea s. str. The palynological characteristics indicated that Section Macrophllae and Section Paniculatae of Blumea were not monophyletic groups. The pollen morphology differentiation of B. lacera clade is consistent with the interspecific relationship revealed by the molecular phylogenetic tree. However, the pollen morphology of the Blumea densiflora clade is inconsistent with the interspecific relationship based on molecular phylogenetic analysis. This palynology research can only partly support the previously published molecular phylogeny of Blumea s. str.

Evaluating the effect of PD-1 inhibitors on left ventricular function in lung cancer with noninvasive myocardial work.

Background: The treatment of advanced lung cancer has been revolutionized by immune checkpoint inhibitors (ICIs) in recent years, largely driven by programmed cell death-1 (PD-1) inhibitors. However, patients with lung cancer who are treated with PD-1 inhibitors are prone to immune-related adverse events (irAEs), especially cardiac adverse events. Noninvasive myocardial work is a novel technique used to assess left ventricular (LV) function, which can effectively predict myocardial damage. Here, noninvasive myocardial work was used to evaluate changes in LV systolic function during PD-1 inhibitor therapy and to assess ICIs-related cardiotoxicity. Methods: From September 2020 to June 2021, 52 patients with advanced lung cancer in the Second Affiliated Hospital of Nanchang University were prospectively enrolled. In total, 52 patients underwent PD-1 inhibitor therapy. The cardiac markers, noninvasive LV myocardial work, and conventional echocardiographic parameters were measured at pretherapy (T0) and posttreatment after the first (T1), second (T2), third (T3), and fourth (T4) cycles. Following this, the trends of the above parameters were analyzed using analysis of variance with repeated measures and the Friedman nonparametric test. Furthermore, the relationships between disease characteristics (tumor type, treatment regimen, cardiovascular risk factors, cardiovascular drugs, and irAEs) and noninvasive LV myocardial work parameters were assessed. Results: Throughout the follow-up, the cardiac markers and conventional echocardiographic parameters showed no significant changes. Based on the normal reference ranges, patients with PD-1 inhibitor therapy had increased values of LV global waste work (GWW) and decreased global work efficiency (GWE) that began at T2. Compared with T0, GWW increased from T1 to T4 (42%, 76%, 87%, and 87%, respectively), while global longitudinal strain (GLS), global work index (GWI), and global constructive work (GCW) decreased in varying degrees (P<0.001). Most of the disease characteristics had no effect on the LV myocardial work parameters; however, the numbers of irAEs were closely associated with GLS (P=0.034), GWW (P<0.001), and GWE (P<0.001). Patients with 2 or more irAEs had higher values of GWW and lower GLS and GWE. Conclusions: Noninvasive myocardial work can accurately reflect myocardial function and energy utilization in patients with lung cancer who are undergoing PD-1 inhibitor treatment and may thus benefit the management of patients with ICIs-related cardiotoxicity.

Prediction of clinical response to neoadjuvant therapy in advanced breast cancer by baseline B-mode ultrasound, shear-wave elastography, and pathological information.

Background: Neoadjuvant therapy (NAT) is the preferred treatment for advanced breast cancer nowadays. The early prediction of its responses is important for personalized treatment. This study aimed at using baseline shear wave elastography (SWE) ultrasound combined with clinical and pathological information to predict the clinical response to therapy in advanced breast cancer. Methods: This retrospective study included 217 patients with advanced breast cancer who were treated in West China Hospital of Sichuan University from April 2020 to June 2022. The features of ultrasonic images were collected according to the Breast imaging reporting and data system (BI-RADS), and the stiffness value was measured at the same time. The changes were measured according to the Response evaluation criteria in solid tumors (RECIST1.1) by MRI and clinical situation. The relevant indicators of clinical response were obtained through univariate analysis and incorporated into a logistic regression analysis to establish the prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the prediction models. Results: All patients were divided into a test set and a validation set in a 7:3 ratio. A total of 152 patients in the test set, with 41 patients (27.00%) in the non-responders group and 111 patients (73.00%) in the responders group, were finally included in this study. Among all unitary and combined mode models, the Pathology + B-mode + SWE model performed best, with the highest AUC of 0.808 (accuracy 72.37%, sensitivity 68.47%, specificity 82.93%, P<0.001). HER2+, Skin invasion, Post mammary space invasion, Myometrial invasion and Emax were the factors with a significant predictive value (P<0.05). 65 patients were used as an external validation set. There was no statistical difference in ROC between the test set and the validation set (P>0.05). Conclusion: As the non-invasive imaging biomarkers, baseline SWE ultrasound combined with clinical and pathological information can be used to predict the clinical response to therapy in advanced breast cancer.

Multimodal ultrasonography findings of extramammary granular cell tumors: Two case reports.

Extramammary masses are infrequently encountered in breast examinations. They may occur in the chest wall and axilla as neighbors of the breast. It is important to determine the nature of the lesion. However, some benign tumors, such as granular cell tumors (GCTs), also show malignant characteristics, which leads to misdiagnosis. To the best of our knowledge, multimodal ultrasound features of GCT have not been elucidated. We report two cases of women with GCTs encountered upon breast cancer screening; the tumor was not located in breast tissue. The first patient was a 37-year-old woman who presented with a slow-growing mass in the right breast and the GCT was located in the pectoralis major muscle. The second patient was a 52-year-old woman who presented with a palpable left axillary mass and the GCT was located in the axilla. Mammography failed to detect the masses in the two patients upon breast cancer screening. However, two-dimensional ultrasonography revealed a solid heterogeneous hypoechoic mass. Shear wave elastography showed that the masses had an increased hardness compared with the surrounding tissue. Further contrast-enhanced ultrasonography showed that the contrast patterns of the two masses were different. In case one, contrast-enhanced ultrasonography showed an inhomogeneous annular high enhancement, and the dynamic curve showed rapid enhancement and regression. In case two, contrast enhanced ultrasound showed slight enhancement around the lesion but no enhancement inside. Postoperative pathology confirmed that the GCT was benign in both cases. The patients showed no signs of recurrence at the 2-year follow-up. Here, we report two cases and present the multimodal ultrasonography findings of this tumor for the first time. Radiologists and surgeons should be aware of these imaging manifestations and include them in their differential diagnoses.

Unexpected breast cancer mimicking benign lesions on ultrasound-guided vacuum-assisted excision biopsy: A retrospective cross-sectional study over a 20-year period.

Objectives: This study investigated the occurrence rate of unexpected breast cancer (UEBC) mimicking benign lesions [Breast Imaging Reporting and Data System (BI-RADS) category 3 or 4a] using ultrasound-guided vacuum-assisted excision biopsy (US-VAEB), and explored the factors responsible for late diagnosis of T2 stage UEBC. Materials and methods: We collected clinicopathologic data and preoperative US imaging features within 3 months before US-VAEB of patients who were diagnosed with UEBC from January 2002 to September 2022. The UEBC were divided into T1 and T2 stageUEBC. The US imaging features as well as clinical and pathological information of T1 and T2 stage UEBC were compared to explore the factors responsible for late diagnosis of T2 stage UEBC. Results: Breast cancer was diagnosed in 91 of 19 306 patients who underwent US-VAEB. We excluded eight patients with breast cancer assigned to BI-RADS 4b category by preoperative US, and two for whom US imaging records were unavailable. Finally, we enrolled 81 patients. The occurrence rate of UEBC after US-VAEB was 0.42%(81/19296). Of the 81 cases of UEBC, 22 were at T2 stage. The ratio of T2 stage UEBC was 27.2%. The differences in risk factor of breast cancer and routine breast US screening between T1 and T2 stage UEBC were significant[96.6% (57/59) vs 81.8% (18/22), 44.1% (26/59) vs 13.6% (3/22), respectively, P<0.05). Conclusion: UEBC was rarely detected by US-VAEB. Most cases of T2 stage UEBC were diagnosed late because of the absence of routine US screening and risk factors for breast cancer. Stricter clinical management regulations for breast lesions and performing regular US screening may be helpful to reduce T2 stage UEBC.

Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach.

Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive.

Improvement of TNBC immune checkpoint blockade with a microwave-controlled ozone release nanosystem.

Despite revolutionary achievements have been made in clinical cancer therapy, the immune checkpoint blockade regimen still presents limited efficacy on tumors lack of neoantigens exposure. Here, we designed and synthesized an on-demand microwave-controlled ozone release nanosystem to specifically generate reactive oxygen species in tumor mass. By taking advantage of iRGD modification, the synthesized nanosystem can be specifically enriched in the tumor microenvironment and subsequently internalized by tumor cells. Triggered by the low-power microwave, ozone was released from the nanocarriers and inhibited tumor cell growth in vitro and in vivo. Molecular mechanism investigation further unraveled that the released-ozone induced cytolytic cell death through the rapid generation of reactive oxygen species such as hydroxyl radical. The tumor-specific neoantigen derived from this immunogenic cell death promoted cytotoxic T-lymphocytes infiltration, which provided a fundament for immune checkpoint blockade therapy. In the triple-negative breast cancer animal model, tumor-specific delivery of ozone significantly improved the systematical anti-tumor efficacy of the PD-1 blockade antibody. Notably, tumor-locally confined microwave-controlled release avoided systematic toxicity in the tested animals. Collectively, our nanosystem provides a novel controllable strategy for promoting immune checkpoint blockade therapy, especially in tumor types deficient in infiltrated T-lymphocytes.

[Fucoidan-Modified Phase-Transitional Contrast Agent for Ultrasound Imaging and Targeting of Hepatoma Cells].

Objective: To prepare a fucoidan-modified phase-transitional contrast agent (FPCA) and to evaluate its in vitro capabilities for ultrasound imaging and targeting of hepatoma cells. Methods: Nano-liposomes encapsulated with perfluoropentane were prepared using thin-film hydration and ultrasonic emulsification methods. Then, FPCA nanoparticles were prepared through chemical grafting of fucoidan and the characterization of their physical and chemical properties was performed. After applying external stimuli of heating with hot water bath and microwave irradiation, the phase-transition status of FPCA was observed with microscope. The imaging abilities of phase-transited FPCA on two-dimensional ultrasound and contrast-enhanced ultrasound were observed with ultrasonic diagnostic instrument. The ability of FPCA to target at hepatoma cells was evaluated and verified with fluorescence confocal observation and flow cytometry analysis. Results: The FPCA prepared in the study had an average diameter of (222.1±32.5) nm, displaying spherical appearance, good dispersion, good stability, and good biocompatibility. The phase-transition of FPCA was induced by both heating with hot water bath and microwave irradiation. For phase transition, the optimal temperature was found to be 50 ℃ and the preferred microwave power was 1.5 W/cm 2. Moreover, after phase transition, FPCA showed significant imaging enhancement on both two-dimensional ultrasonography and contrast-enhanced ultrasonography. Through fluorescein isothiocyanate (FITC) labeling, FPCA could specifically bind with hepatoma cells at a high binding rate of (96.19±1.62)%, while it rarely bound with normal liver cells, showing a binding rate of less than 10%. Conclusion: A new type of phase-transitional ultrasound contrast agent with good stability and biocompatibility was successfully prepared. It not only could enhance ultrasound imaging through phase transition, but also had specific active hepatoma cell-targeting properties.

Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging.

Background: The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images. Methods: The clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data. Results: The characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER-; P=0.003), progesterone receptor negative (PR-; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765. Conclusions: Deep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.

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Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.

Quantitative assessment of renal damage in rhesus monkeys with diabetic nephropathy using contrast-enhanced ultrasound.

Background: Diabetic nephropathy (DN) is a common chronic microvascular complication of diabetes. Noninvasive diagnosis of DN is difficult. Contrast-enhanced ultrasound (CEUS), as a functional imaging method, provides noninvasive real-time images and quantitative assessment of renal microvascular perfusion. This study investigated the efficacy of CEUS in discriminating between DN and normal kidneys in rhesus monkeys. Methods: A total of 12 male rhesus monkeys (DN model group, n=6; normal control group, n=6) were included in this study. The following parameters were evaluated: (I) blood biochemistry; (II) CEUS; and (III) ultrasound-guided renal biopsy. Results: Pathological and biochemical results showed that all subjects in the lesion group had serious renal damage. There were significant differences in the CEUS parameters, including the area under the curve, the time from peak to one half, and peak intensity between the lesion group and the normal group. The time to peak was slightly delayed in the lesion group. There was no significant difference in the rise time between the two groups. Conclusions: Although the precise CEUS parameters that may best predict renal damage still require systematic evaluation, the results of these animal studies suggest that CEUS may be used as a supplemental tool in diagnosing renal damage in rhesus monkeys with DN. We hope these findings can provide insights for the application of CEUS in DN.