Intrinsic Wave Velocity Propagation:A Novel Parameter for Assessing the Effect of Anthracycline Chemotherapy Agents on Cardiac Diastolic Function in Breast Cancer Patients

Objective Anthracycline chemotherapeutic agents have significant cardiotoxicity.The present study emphasized the effect of anthracycline chemotherapy drugs on left ventricular(LV)myocardial stiffness in breast cancer patients by measuring the intrinsic wave velocity propagation(IVP),and evaluating the potential clinical value of IVP in detecting early LV diastolic function impairment.Methods A total of 68 newly diagnosed breast cancer patients,who were treated with anthracycline-based chemotherapy,were analyzed.Transthoracic echocardiography was performed at baseline(T0),and after 1,2,3,4 and 8 chemotherapeutic cycles(T1,T2,T3,T4 and T5,respectively).Then,the IVP,LV strain parameters[global longitudinal strain(GLS),longitudinal peak strain rate at systole(LSRs),longitudinal peak strain rate at early diastole(LSRe),longitudinal peak strain rate at late diastole(LSRa),and the E/LSRe ratio],and conventional echocardiographic parameters were obtained and further analyzed.A relative reduction of>15%in GLS was considered a marker of early LV subclinical dysfunction.Results Compared to the T0 stage,IVP significantly increased at the T1 stage.However,there were no significant changes in GLS,LSRs,or LSRe between the T0 and T1 stages.These parameters significantly decreased from the T2 stage.LSRa started to significantly decrease at the T5 stage,and the E/LSRe ratio started to significantly increase at the T3 stage(all P<0.05).At the T0 stage,IVP(AUC=0.752,P<0.001)had a good predictive value for LV subclinical dysfunction after chemotherapy.Conclusions IVP is a potentially sensitive parameter for the early clinical assessment of anthracycline-related cardiac diastolic impairment.

Artificial intelligence in medical imaging of the liver

Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.

Artificial intelligence in breast ultrasound

Artificial intelligence(AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women,severely threatening women's health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

Effects of Norepinephrine on Renal Cortical and Medullary Blood Flow in Atherosclerotic Rabbits

Objective The aim of this study was to explore the effect of norepinephrine(NE)on renal cortical and medullary blood flow in atherosclerotic rabbits without renal artery stenosis.Methods Atherosclerosis was induced in 21 New Zealand white rabbits by feeding them a cholesterol-rich diet for 16 weeks.Thirteen healthy New Zealand white rabbits were randomly selected as controls.After atherosclerosis induction,standard ultrasonography was performed to confirm that there was no plaque or accelerated flow at the origin of the renal artery.Contrast-enhanced ultrasound(CEUS)was performed at baseline and during intravenous injection of NE.The degree of contrast enhancement of renal cortex and medulla after the injection of contrast agents was quantified by calculating the enhanced intensity.Results The serum nitric oxide(NO)level in atherosclerotic rabbits was higher than that in healthy rabbits(299.6±152 vs.136.5±49.5,P<0.001).The infusion of NE induced a significant increase in the systolic blood pressure(112±14 mmHg vs.84±9 mmHg,P=0.016)and a significant decrease in the enhanced intensity in renal cortex(17.78±2.07 dB vs.21.19±2.03 dB,P<0.001)and renal medulla(14.87±1.82 dB vs.17.14±1.89 dB,P<0.001)during CEUS.However,the enhanced intensity in the cortex and medulla of healthy rabbits after NE infusion showed no significant difference from that at baseline.Conclusion NE may reduce renal cortical and medullary blood flow in atherosclerotic rabbits without renal artery stenosis,partly by reducing the serum NO level.