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      Radiomics‐based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS‐CoV‐2) and influenza A infected pneumonia

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          Abstract

          Clinicians have been faced with the challenge of differentiating between severe acute respiratory syndrome associated coronavirus 2 (SARS‐CoV‐2) infected pneumonia (NCP) and influenza A infected pneumonia (IAP), a seasonal disease that coincided with the outbreak. We aim to develop a machine‐learning algorithm based on radiomics to distinguish NCP from IAP by texture analysis based on computed tomography (CT) imaging. Forty‐one NCP and 37 IAP patients admitted from January to February 6, 2019 admitted to two hospitals in Wenzhou, China. All patients had undergone chest CT examination and blood routine tests prior to receiving medical treatment. NCP was diagnosed by real‐time RT‐PCR assays. Eight of 56 radiomic features extracted by LIFEx were selected by least absolute shrinkage and selection operator regression to develop a radiomics score and subsequently constructed into a nomogram to predict NCP with area under the operating characteristics curve of 0.87 (95% confidence interval: 0.77‐0.93). The nomogram also showed excellent calibration with Hosmer‐Lemeshow test yielding a nonsignificant statistic ( P = .904). The novel nomogram may efficiently distinguish between NCP and IAP patients. The nomogram may be incorporated to existing diagnostic algorithm to effectively stratify suspected patients for SARS‐CoV‐2 pneumonia.

          Abstract

          Clinicians have been faced with the challenge of differentiating between severe acute respiratory syndrome‐associated coronavirus 2 (SARS‐CoV‐2) infected pneumonia (NCP) and influenza A infected pneumonia (IAP). We developed a machine‐learning algorithm based on radiomics to distinguish NCP fromIAP by texture analysis based on computed tomography (CT) imaging. Eight of 56 radiomic features extracted by LIFEx were selected to develop a radiomics score and subsequently constructed into a nomogram to predict NCP. The novel nomogram may efficiently distinguish between NCP and IAP patients.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity

            Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786-9. ©2018 AACR.
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              Influenza-associated excess respiratory mortality in China, 2010–15: a population-based study

               Li Li,  Yunning Liu,  Peng Wu (2019)
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                Author and article information

                Contributors
                zhengmh@wmu.edu.cn
                Journal
                MedComm (Beijing)
                MedComm (Beijing)
                10.1002/(ISSN)2688-2663
                MCO2
                Medcomm
                John Wiley and Sons Inc. (Hoboken )
                2688-2663
                13 August 2020
                Affiliations
                [ 1 ] Clinical Research Center The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China
                [ 2 ] NAFLD Research Center, Department of Hepatology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
                [ 3 ] School of the First Clinical Medical Sciences Wenzhou Medical University Wenzhou China
                [ 4 ] Department of Critical Care Medicine Wenzhou Central Hospital Wenzhou China
                [ 5 ] Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
                [ 6 ] Institute of Hepatology Wenzhou Medical University Wenzhou China
                [ 7 ] Key Laboratory of Diagnosis and Treatment for The Development of Chronic Liver Disease in Zhejiang Province Wenzhou China
                Author notes
                [* ] Correspondence

                Ming‐Hua Zheng, NAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou 325000, China.

                Email: zhengmh@ 123456wmu.edu.cn

                Article
                MCO214
                10.1002/mco2.14
                7436469
                © 2020 The Authors. MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 6, Tables: 1, Pages: 9, Words: 4386
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                Original Article
                Original Articles
                Custom metadata
                2.0
                corrected-proof
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.7 mode:remove_FC converted:19.08.2020

                diagnosis, covid‐19, sars‐cov‐2

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