Relation of myocardial dysfunction to biomarkers, COVID-19 severity and all-cause mortality.

AIMS: The COVID-19 infection has been described as affecting myocardial injury. However, the relation between left ventricular global longitudinal strain (GLS), global circumferential strain (GCS) and global radial strain (GRS), disease severity and all-cause mortality in COVID-19 is unclear. METHODS AND RESULTS: The study consisted of 220 patients with COVID-19, including 127 (57.5%) with mild, 43 (19.5%) with moderate and 50 (22.7%) with severe/critical conditions. Myocardial dysfunction was analysed by GLS, GCS and GRS using two-dimensional speckle-tracking echocardiography. Hazard ratios and Kaplan-Meier curves were produced to assess the association between strains and cardiac biomarker indices with a composite outcome of all-cause mortality. With an average follow-up period of 11 days, 19 patients reached the endpoint (death). Significant associations were found for the three strain parameters and the levels of blood urea nitrogen (BUN) (r = 0.206, 0.221 and 0.355, respectively). Cardiac troponin I (cTnI) was closely related to the GLS and GCS (r = 0.240 and 0.324, respectively). In multivariable Cox regression, GCS > -21.6% was associated with all-cause death {hazard ratio, 4.007 [95% confidence interval (CI), 11.347-11.919]}. CONCLUSIONS: GLS, GCS and GRS are significantly related to myocardial dysfunction in patients with COVID-19. Worsening GCS poses an increased risk of death in COVID-19.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.

Objective: In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods: Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. Results: A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. Conclusions: The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.

PURPOSE: To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. RESULTS: For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. CONCLUSION: The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ.

OBJECTIVE: To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment. METHODS: Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established. RESULTS: A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867. CONCLUSION: Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

Predictive value of contrast-enhanced ultrasonography and ultrasound elastography for management of BI-RADS category 4 nonpalpable breast masses.

PURPOSE: The objective of this study was to investigate the independent risk factors and associated predictive values of contrast-enhanced ultrasound (CEUS), shear wave elastography (SWE), and strain elastography (SE) for high-risk lesions (HRL) and malignant tumors (MT) among nonpalpable breast masses classified as BI-RADS category 4 on conventional ultrasound. METHODS: This prospective study involved consecutively admitted patients with breast tumors from January 2018, aiming to explore the management of BI-RADS category 4 breast tumors using CEUS and elastography. We conducted a retrospective review of patient data, focusing on those with a history of a nonpalpable mass as the primary complaint. Pathologic findings after surgical resection served as the gold standard. The CEUS arterial-phase indices were analyzed using contrast agent arrival-time parametric imaging processing mode, while quantitative and qualitative indices were examined on ES images. Independent risk factors were identified through binary logistic regression multifactorial analysis. The predictive efficacy of different modalities was compared using a receiver operating characteristics curve. Subsequently, a nomogram for predicting the risk of HRL/MT was established based on a multifactorial logistic regression model. RESULTS: A total of 146 breast masses from 146 patients were included, comprising 80 benign tumors, 12 HRLs, and 54 MTs based on the final pathology. There was no significant difference in pathologic size between the benign and HRL/MT groups [8.00(6.25,10.00) vs. 9.00(6.00,10.00), P = 0.506]. The diagnostic efficacy of US plus CEUS exceeded that of US plus SWE/SE for BI-RADS 4 nonpalpable masses, with an AUC of 0.954 compared to 0.798/0.741 (P ï¼œ 0.001). Further stratified analysis revealed a more pronounced improvement for reclassification of BI-RADS 4a masses (AUC: 0.943 vs. 0.762/0.675, P ï¼œ 0.001) than BI-RADS 4b (AUC:0.950 vs. 0.885/0.796, P＞0.05) with the assistance of CEUS than SWE/SE. Employing downgrade CEUS strategies resulted in negative predictive values ranging from 95.2 % to 100.0 % for BI-RADS 4a and 4b masses. Conversely, using upgrade nomogram strategies, which included the independent predictive risk factors of irregular enhanced shape, poor defined enhanced margin, earlier enhanced time, increased surrounding vessels, and presence of contrast agent retention, the diagnostic performance achieved an AUC of 0.947 with good calibration. CONCLUSION: After investigating the potential of CEUS and ES in improving risk assessment and diagnostic accuracy for nonpalpable BI-RADS category 4 breast masses, it is evident that CEUS has a more significant impact on enhancing classification compared to ES, particularly for BI-RADS 4a subgroup masses. This finding suggests that CEUS may offer greater benefits in improving risk assessment and diagnostic accuracy for this specific subgroup of breast masses.

Predicting axillary metastasis in breast cancer using lymphatic contrast-enhanced ultrasound-guided fine-needle aspiration of one lymph node.

OBJECTIVES: Fine-needle aspiration (FNA) is a microinvasive method to diagnose lymph nodes. This study aims to determine the capability of lymphatic contrast-enhanced ultrasound (LCEUS)-guided FNA in predicting the axillary metastasis with the target of one lymph node (LN) in patients with breast cancer. METHODS: LCEUS was prospectively performed in 105 patients with breast cancer. The most suspicious LN was targeted based on the characters of LCEUS. FNA was performed in the LN, followed by localization using a guide wire. The detection of lymph cells and/or tumour cells was recognized as a puncture success. Cytologic diagnosis was compared with histologic diagnosis of wire-marked LN for diagnosing accuracy and compared with histologic diagnosis of axillary LNs for predicting accuracy. RESULTS: LCEUS-guided FNA was performed in all 105 female patients who underwent axillary dissection. The puncture success rates were 74.3%, 91.4%, and 97.1% for three sequential groups (P = .010). In diagnosing LN metastasis, the sensitivity, specificity, and accuracy values of LCEUS-guided FNA were 89.7%, 100%, and 95.7%, respectively. In predicting axillary metastasis, the sensitivity, specificity, and accuracy values of LCEUS-guided FNA were 81.4%, 100%, and 91.3%, respectively. CONCLUSIONS: The microinvasive LCEUS-guided FNA of one lymph node can be an accurate method and may help predict axillary metastasis in patients with breast cancer. ADVANCES IN KNOWLEDGE: This study presented that LCEUS combined with FNA would be practical in clinic. The characters of LCEUS could indicate the suspicious LNs and promote the accuracy in predicting axillary metastasis.

Arterial Stiffness, Body Mass Index and Cardiovascular Disease Risk in Chinese Females at Various Ages.

Background: This study investigated the correlation in parameters of arterial stiffness and cardiovascular disease (CVD) risk on age and body mass index (BMI) in Chinese females. Methods: This cross-sectional study enrolled 2220 females. Arterial stiffness was assessed by the measurement of arterial velocity pulse index (AVI) and arterial pressure volume index (API). Individual 10-year cardiovascular risk was calculated for each patient using the Framingham cardiovascular risk score (FCVRS). Results: API and AVI had a significant J-shaped relationship with age. Beginning at the age of 30 years, the API started to increase, while after 49 years, the increase in API was even steeper. AVI increased from the age of 32 years, and increased more rapidly after 56 years. The linear association between API and BMI following adjustment for age was significant ( ß = 0.324, 95% CI 0.247-0.400, p < 0.001). In the total study cohort, FCVRS scores increased by 0.16 scores for every 1 kg/ m 2 increase in BMI and by 0.11 scores for each 1 value increase in API in the age adjusted model. Conclusions: API and BMI correlate with 10-year cardiovascular risk at various ages in females. Regardless of age, overweight females have a higher risk of increased API. Therefore API can be used for the early detection of CVD so that preventive therapy can be instituted in these high risk patients. Clinical Trial Registration: Registered on the official website of the China Clinical Trial Registration Center (20/08/2020, ChiCTR2000035937).

A Novel Index System for Assessing Ventricular-Vascular Coupling.

Background: To explore the value of a novel ventricular-vascular coupling index (VVI) system in relation to age, gender and body mass index (BMI). Methods: A total of 239 volunteers with single-center and cross-sectional health screening were enrolled in the study. Subjects were divided according to age (young [18-44 years], middle-age [45-59 years], old [60-80 years]), gender (male, female), and BMI (overweight/obese [BMI ≥ 24], control [BMI < 24]). The left ventricle end-diastolic volume (LVEDV) and left ventricle end-systolic volume (LVESV) provided the left ventricular structure index, while the TDI e ' provided the functional index. Also derived from routine echocardiography were the effective arterial elastance (Ea), left ventricular end-systolic elastance (Ees), and VVI. The novel VVI systems were arterial velocity pulse index (AVI), left ventricular global longitudinal strain (LVGLS), and the AVI to LVGLS ratio (AVI/LVGLS). Results: (1) Middle-age and elderly subjects had higher Ea and lower LVGLS compared to young subjects. AVI and AVI/LVGLS increased progressively from young to middle-age to old subjects. (2) Females had higher Ea, Ees and LVGLS than male subjects. No significant differences in AVI and AVI/LVGLS were observed between males and females. (3) No significant differences in Ea, Ees, VVI, AVI, LVGLS and AVI/LVGLS were observed between the overweight/obese and control groups. (4) AVI/LVGLS was negatively correlated with LVEDV and LVESV and with TDI e ' . LVEDV, LVESV and TDI e ' were independent predictors of AVI/LVGLS. (5) The diagnostic performance of AVI/LVGLS was higher than that of VVI in the young and middle-age groups. The diagnostic efficacy of AVI/LVGLS was higher than that of VVI in the young and old groups, and the diagnostic efficacy of AVI was higher than that of Ea. The difference in diagnostic efficacy between LVGLS and Ees was not statistically significant. The differences in diagnostic efficacy between AVI/LVGLS and VVI, AVI and Ea, and LVGLS and Ees were not statistically significant in the middle-age and old groups. Conclusions: The novel index system of ventricular-vascular coupling described here (AVI, LVGLS, and AVI/LVGLS) was more effective than traditional indexes in detecting differences in cardiovascular function between different ages groups. Clinical Trial Registration: The study protocol was registered on the official website of China Clinical Trial Registration Center (ChiCTR2000035937).

Association of Arterial Stiffness Indices with Framingham Cardiovascular Disease Risk Score.

Purpose: The new non-invasive arterial stiffness indices, arterial velocity pulse index (AVI) and arterial pressure volume index (API) are known to be associated with cardiovascular disease risk. The present study aimed to examine the "dose-response" associations between AVI, API and Framingham cardiovascular disease risk score (FCVRS). Methods: This survey included individuals with arterial stiffness indices collected at age 18 years and older. We used Pearson's correlation coefficients and multivariate linear analyses to evaluate associations of AVI and API to other variables. The associations between FCVRS and AVI, API were analyzed by restrictive cubic spline. Results: 4311 people were included in the full study population, including 2091 males and 2220 females. In restricted cubic spline regression models, AVI or API had significant U-shaped associations with FCVRS, with the lowest risk score of cardiovascular disease was 8 units or 18 units, respectively. After AVI increased to 12 units, FCVRS increased rapidly until AVI was 27 units, and the FCVRS increased relatively flat afterward. For API, results were similar. When API increased to 23 units, the FCVRS increased rapidly, and after API was 52 units, FCVRS increased relatively flat. Conclusions: AVI or API had U-shaped associations with FCVRS. The associations may provide a new perspective for early treatment or lifestyle modifications to prevent cardiovascular diseases.