Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnoses and treatment assessment of the disease. Herein, we review the use of imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography -CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play an important role in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

Nano-radiosensitization has been a hot concept for the past ten years, and the nanomaterial-mediated tumor radiosensitization method is mainly focused on increasing intracellular radiation deposition by high atomic number (high Z) nanomaterials, particularly gold (Au)-mediated radiation enhancement. Recently, various new nanomaterial-mediated radiosensitive approaches have been successively reported, such as catalyzing reactive oxygen species (ROS) generation, consuming intracellular reduced glutathione (GSH), overcoming tumor hypoxia, and various synergistic radiotherapy ways. These strategies may open a new avenue for enhancing the radiotherapeutic effect and avoiding its side effects. Nevertheless, reviews systematically summarizing these newly emerging methods and their radiosensitive mechanisms are still rare. Therefore, the general strategies of nanomaterial-mediated tumor radiosensitization are comprehensively summarized, particularly aiming at introducing the emerging radiosensitive methods. The strategies are divided into three general parts. First, methods on account of the intrinsic radiosensitive properties of nanoradiosensitizers for radiosensitization are highlighted. Then, newly developed synergistic strategies based on multifunctional nanomaterials for enhancing radiotherapy efficacy are emphasized. Third, nanomaterial-mediated radioprotection approaches for increasing the radiotherapeutic ratio are discussed. Importantly, the clinical translation of nanomaterial-mediated tumor radiosensitization is also covered. Finally, further challenges and outlooks in this field are discussed.

Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32 +/- 0.62 mm and mean absolute distance error of 0.54 +/- 0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36 +/- 0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve.