Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

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.

<b><i>Background:</i></b> Radiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). <b><i>Methods:</i></b> Two hundred and eight patients with pathologically confirmed HCC (training cohort: <i>n</i> = 146; validation cohort: <i>n</i> = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility. <b><i>Results:</i></b> Higher α-fetoprotein level (<i>p</i> = 0.046), nonsmooth tumor margin (<i>p</i> = 0.003), arterial peritumoral enhancement (<i>p &#x3c;</i> 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (<i>p &#x3c;</i> 0.001) and HBP T1 maps (<i>p &#x3c;</i> 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; <i>p</i> = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; <i>p</i> = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895–0.976) and 0.864 (95% CI 0.761–0.967) in the training and validation cohort, fitting well in calibration curves (<i>p</i> &#x3e; 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. <b><i>Conclusions:</i></b> The nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.