A novel coronavirus has resulted in an ongoing outbreak of viral pneumonia in China. 1-3 Person-to-person transmission has been demonstrated, 1 but, to our knowledge, transmission of the novel coronavirus that causes coronavirus disease 2019 (COVID-19) from an asymptomatic carrier with normal chest computed tomography (CT) findings has not been reported. Methods | In January 2020, we enrolled a familial cluster of 5 patients with fever and respiratory symptoms who were admitted to the Fifth People's Hospital of Anyang, Anyang, China, and 1 asymp-tomatic family member. This study was approved by the local institutional review board, and written informed consent was obtained from all patients. A detailed analysis of patient records was performed. All patients underwent chest CT imaging. Real-time reverse transcriptase polymerase chain reaction (RT-PCR) tests for COVID-19 nucleic acid were performed using nasopharyngeal swabs (Novel Coronavirus PCR Fluorescence Diagnostic Kit, BioGerm Medical Biotechnology).

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.

Early imaging or blood biomarkers of tumor response are desperately needed to customize antiangiogenic therapy for cancer patients. Anti-vascular endothelial growth factor (VEGF) therapy can ''normalize'' brain tumor vasculature by decreasing vessel diameter and permeability, and thinning the abnormally thick basement membrane. We hypothesized that the extent of vascular normalization will be predictive of outcome of anti-VEGF therapy in glioblastoma. We used advanced magnetic resonance imaging methods to monitor vascular parameters and treatment response in 31 recurrent glioblastoma patients enrolled in a phase II trial of cediranib, an oral pan-VEGF receptor tyrosine kinase inhibitor. We evaluated the correlation between clinical outcome and magnetic resonance imaging-measured changes in vascular permeability/flow (i.e., K trans ) and in microvessel volume, and the change of circulating collagen IV levels, all after a single dose of cediranib. Here, we show that evaluation of biomarkers as early as after one day of anti-VEGF therapy with cediranib is predictive of response in patients with recurrent glioblastoma. Changes in K trans , microvessel volume, and circulating collagen IV correlated with duration of overall survival and/or progression-free survival (P < 0.05). When we combined these three parameters into a ''vascular normalization index,'' we found that it closely associated with overall survival (R = 0.54; P = 0.004) and progression-free survival (R = 0.6; P = 0.001). The vascular normalization index described here should be validated in randomized clinical trials. [Cancer Res 2009;69(13):5296-300]

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

The abnormal vasculature of the tumor microenvironment supports progression and resistance to treatment. Judicious application of anti-angiogenic therapy may normalize the structure and function of the tumor vasculature, promoting improved blood perfusion. However, there has been a lack of direct clinical evidence for improvements in blood perfusion after anti-angiogenic therapy. In this study, we used MRI to assess tumor blood perfusion in 30 recurrent glioblastoma patients who were undergoing treatment with cediranib, a pan-VEGF receptor tyrosine kinase inhibitor. Tumor blood perfusion increased durably for more than one month in 7 of 30 patients where it was associated with longer survival. Together, our findings offer direct clinical evidence in support of the hypothesis that vascular normalization promotes tumor regression and longer patient survival.

).q RSNA, 2015 Purpose:To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas. Materials andMethods:This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusionweighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations. Results:ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P , .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P , .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P , .05). Conclusion:Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.q RSNA, 2015

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

1Coronavirus disease 2019 has spread globally, and medical 2 resources become insufficient in many regions. Fast diagnosis of COVID-19, and 3 finding high-risk patients with worse prognosis for early prevention and medical 4 resources optimization is important. Here, we proposed a fully automatic deep 5 learning system for COVID-19 diagnostic and prognostic analysis by routinely used 6 computed tomography.