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      Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

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          To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

          Materials and Methods

          In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.


          Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO ( P < .001), 0.97 for PHO ( P < .001), 0.91 for LSS ( P < .001), 0.90 for LHOS ( P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.


          A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

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          Most cited references 9

          • Record: found
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          The National Lung Screening Trial: overview and study design.

          The National Lung Screening Trial (NLST) is a randomized multicenter study comparing low-dose helical computed tomography (CT) with chest radiography in the screening of older current and former heavy smokers for early detection of lung cancer, which is the leading cause of cancer-related death in the United States. Five-year survival rates approach 70% with surgical resection of stage IA disease; however, more than 75% of individuals have incurable locally advanced or metastatic disease, the latter having a 5-year survival of less than 5%. It is plausible that treatment should be more effective and the likelihood of death decreased if asymptomatic lung cancer is detected through screening early enough in its preclinical phase. For these reasons, there is intense interest and intuitive appeal in lung cancer screening with low-dose CT. The use of survival as the determinant of screening effectiveness is, however, confounded by the well-described biases of lead time, length, and overdiagnosis. Despite previous attempts, no test has been shown to reduce lung cancer mortality, an endpoint that circumvents screening biases and provides a definitive measure of benefit when assessed in a randomized controlled trial that enables comparison of mortality rates between screened individuals and a control group that does not undergo the screening intervention of interest. The NLST is such a trial. The rationale for and design of the NLST are presented. © RSNA, 2010
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            Densely connected convolutional networks

              • Record: found
              • Abstract: not found
              • Article: not found

              Multinomial Goodness-Of-Fit Tests


                Author and article information

                Radiol Artif Intell
                Radiol Artif Intell
                Radiology. Artificial Intelligence
                Radiological Society of North America
                29 July 2020
                : 2
                : 4
                From the Hôpital Foch, Suresnes, France (P.G., F.M.), Donald and Barbara Zucker School of Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA (S.C., P.S.), Siemens Healthinners, Bangalore, India (A.B.), Siemens Healthineers, Forchheim, Germany (T.F., V.Z.), Siemens Healthineers, Princeton, NJ, USA (S.C., B.G., S.G., S.L., T.R., Z.X., Y.Y., D.C.), Siemens Healthineers, Paris, France (G.C.), University Hospital Basel, Clinic of Radiology & Nuclear medicine, Basel, Switzerland (A.W.S.), Vancouver General Hospital, Vancouver, Canada (N.M., S.N., W.P.)
                Author notes
                Address correspondence to S.C. (e-mail: shikha.chaganti@ ).
                2020 by the Radiological Society of North America, Inc.

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

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