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BackgroundHepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.ObjectiveThis study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.MethodsA literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.ResultsRadiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.ConclusionRadiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

We established a reference range for the CBD diameter on MRCP for an asymptomatic population. The CBD diameter is correlated with age. Respiration did not affect the non-dilated CBD diameter.

The operating temperature of the magnetic coupler in wireless power transfer (WPT) systems for electric vehicles (EVs) determines the system reliability and service life. This paper studies the modeling and analysis of temperature characteristics of a general magnetic coupler. Firstly, a thermal circuit model is established to show the heat transfer of the magnetic coupler in the air. Secondly, the heating mechanism of the coil, core and aluminum (Al) shield plate is studied. The thermal power of each component is calculated, and the temperature distribution is qualitatively given based on the proposed thermal circuit model. Then, the temperature distribution of each component is simulated, and the results are consistent with the theoretical analysis. Finally, a 6.6 kW WPT prototype is set up, and the temperature of each component is measured after 30 minutes of operation. The temperature of the coil, 34.6 ℃, is the highest among that of the coil, core, Al plate and coil base. The agreement between experimental results and the theoretical simulation results shows that the thermal field simulation can accurately predict the temperature of the magnetic coupler by reasonably setting the thermal power addition method. INDEX TERMS Magnetic coupler, Temperature characteristic, Thermal circuit model, Wireless power transfer system (WPT).

ObjectivesTo investigate the CT changes of different clinical types of COVID-19 pneumonia.MethodsThis retrospective study included 50 confirmed patients with COVID-19 from 16 January 2020 to 25 February 2020. We analyzed the clinical and CT characteristics of the patients between the moderate group and the severe and critical group, and the dynamic changes of severity with the CT follow-up time.ResultsThere were no differences in the occurrence rate of CT characteristics between the moderate group (n=34) and the severe and critical group (n=16) in the initial CT (all p >0.05). There were differences in the CT score of right lung and total CT score at the initial CT between the two groups (all p <0.05). There was a quadratic relationship between total CT score and CT follow-up time in the severe and critical group (r2=0.137, p=0.008), the total CT severity score peaked at the second follow-up CT. There was no correlation between total CT score and CT follow-up time in the moderate group (p >0.05). The total CT score of the severe and critical group was different between the initial and first follow-up, the second and third follow-ups, the third and fourth follow-ups, and the fourth and fifth follow-ups CT (all p<0.05). The total CT score of the moderate group was different between the second and third follow-ups CT (p<0.05).ConclusionsCOVID-19 pneumonia with the severe and critical types progressed rapidly with the greatest severity at the second follow-up CT, and the moderate type was relatively stable.

Pancreatic cancer is one of the most common malignant tumors and remains a treatment-refractory cancer with a poor prognosis. Currently, the diagnosis of pancreatic neoplasm depends mainly on imaging and which methods are conducive to detecting small lesions. Compared to the other techniques, magnetic resonance imaging (MRI) has irreplaceable advantages and can provide valuable information unattainable with other noninvasive or minimally invasive imaging techniques. Advances in MR hardware and pulse sequence design have particularly improved the quality and robustness of MRI of the pancreas. Diffusion MR imaging serves as one of the common functional MRI techniques and is the only technique that can be used to reflect the diffusion movement of water molecules in vivo. It is generally known that diffusion properties depend on the characterization of intrinsic features of tissue microdynamics and microstructure. With the improvement of the diffusion models, diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique to the more complex. In this review, the various diffusion MRI techniques for pancreatic cancer are discussed, including conventional diffusion weighted imaging (DWI), multi-b DWI based on intra-voxel incoherent motion theory, diffusion tensor imaging and diffusion kurtosis imaging. The principles, main parameters, advantages and limitations of these techniques, as well as future directions for pancreatic diffusion imaging are also discussed.

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