Simulated low-dose dark-field radiography for detection of COVID-19 pneumonia – ScienceOpen
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      Simulated low-dose dark-field radiography for detection of COVID-19 pneumonia

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          Abstract

          Background

          Dark-field radiography has been proven to be a promising tool for the assessment of various lung diseases.

          Purpose

          To evaluate the potential of dose reduction in dark-field chest radiography for the detection of the Coronavirus SARS-CoV-2 (COVID-19) pneumonia.

          Materials and methods

          Patients aged at least 18 years with a medically indicated chest computed tomography scan (CT scan) were screened for participation in a prospective study between October 2018 and December 2020. Patients were included if they had a CO-RADS (COVID-19 Reporting and Data System) score ≥ 4 (COVID-19 group) or if they had no pathologic lung changes (controls). A total of 89 participants with a median age of 60 years (interquartile range 48 to 68 yrs.) were included in this study. Dark-field and attenuation-based radiographs were simultaneously obtained by using a prototype system for dark-field radiography. By modifying the image reconstruction algorithm, low-dose radiographs were simulated based on real participant images. The simulated radiographs corresponded to 50%, 25%, and 13% of the full dose (41.9 μSv, median value). Four experienced radiologists served as blinded readers assessing both image modalities, displayed side by side in random order. The presence of COVID-19-associated lung changes was rated on a scale from 1 to 6. The readers’ diagnostic performance was evaluated by analyzing the area under the receiver operating characteristic curves (AUC) using Obuchowski’s method. Also, the dark-field images were analyzed quantitatively by comparing the dark-field coefficients within and between the COVID-19 and the control group.

          Results

          The readers’ diagnostic performance in the image evaluation, as described by the AUC value (where a value of 1 corresponds to perfect diagnostic accuracy), did not differ significantly between the full dose images (AUC = 0.86) and the simulated images at 50% (AUC = 0.86) and 25% of the full dose(AUC = 0.84) (p>0.050), but was slightly lower at 13% dose (AUC = 0.82) (p = 0.038). For all four radiation dose levels, the median dark-field coefficients within groups were identical but different significantly by 15% between the controls and the COVID-19 pneumonia group (p<0.001).

          Conclusion

          Dark-field imaging can be used to diagnose the Coronavirus SARS-CoV-2 (COVID-19) pneumonia with a median dose of 10.5 μSv, which corresponds to 25% of the original dose used for dark-field chest imaging.

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          Most cited references25

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          Receiver operating characteristic curve in diagnostic test assessment.

          The performance of a diagnostic test in the case of a binary predictor can be evaluated using the measures of sensitivity and specificity. However, in many instances, we encounter predictors that are measured on a continuous or ordinal scale. In such cases, it is desirable to assess performance of a diagnostic test over the range of possible cutpoints for the predictor variable. This is achieved by a receiver operating characteristic (ROC) curve that includes all the possible decision thresholds from a diagnostic test result. In this brief report, we discuss the salient features of the ROC curve, as well as discuss and interpret the area under the ROC curve, and its utility in comparing two different tests or predictor variables of interest.
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            Answering the Call for a Standard Reliability Measure for Coding Data

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              CO-RADS – A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation

              Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for standardized assessment of pulmonary involvement of COVID-19 on non-enhanced chest CT and report its initial interobserver agreement and performance. Methods The Dutch Radiological Society (NVvR) developed CO-RADS based on other efforts for standardization, such as Lung-RADS or BI-RADS. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients presenting with moderate to severe symptoms of COVID-19. The system was evaluated using 105 chest CTs of patients admitted to the hospital with clinical suspicion of COVID-19 in whom RT-PCR was performed (62 +/- 16 years, 61 men, 53 with positive RT-PCR). Eight observers assessed the scans using CO-RADS. Fleiss’ kappa was calculated, and scores of individual observers were compared to the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared to results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss’ kappa was 0.47 (95% confidence interval (CI) 0.45-0.47), with the highest kappa for CO-RADS categories 1 (0.58, 95% CI 0.54-0.62) and 5 (0.68, 95% CI 0.65-0.72). The average AUC was 0.91 (95% CI 0.85-0.97) for predicting RT-PCR outcome and 0.95 (95% CI 0.91-0.99) for clinical diagnosis. The false negative rate for CO-RADS 1 was 9/161 (5.6%, 95% CI 1.0-10%), and the false positive rate for CO-RADS 5 was 1/286 (0.3%, 95% CI 0-1.0%). Conclusions CO-RADS is a categorical assessment scheme for pulmonary involvement of COVID-19 on non-enhanced chest CT providing very good performance for predicting COVID-19 in patients with moderate to severe symptoms and has a substantial interobserver agreement, especially for categories 1 and 5.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Supervision
                Role: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                27 December 2024
                2024
                : 19
                : 12
                : e0316104
                Affiliations
                [1 ] Chair of Biomedical Physics, Department of Physics & School of Natural Sciences, Technical University of Munich, Garching bei München, Germany
                [2 ] Munich Institute of Biomedical Engineering, Technical University of Munich, Garching bei München, Germany
                [3 ] Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, München, Germany
                [4 ] Philips Research, Hamburg, Germany
                [5 ] Munich Institute for Advanced Study, Technical University of Munich, Garching bei München, Germany
                [6 ] Kantonsspital Münsterlingen, Münsterlingen, Switzerland
                Cameroon National Radiation Protection Agency, CAMEROON
                Author notes

                Competing Interests: NO authors have competing interests.

                ‡ RCS and HB authors contributed equally to the manuscript and share first authorship. DP and FP authors contributed equally to the manuscript and share senior authorship

                Author information
                https://orcid.org/0000-0001-7650-7009
                https://orcid.org/0000-0001-5642-0694
                https://orcid.org/0000-0002-0352-8180
                https://orcid.org/0000-0002-1986-0057
                https://orcid.org/0000-0003-4126-462X
                https://orcid.org/0000-0002-6602-1360
                Article
                PONE-D-24-35869
                10.1371/journal.pone.0316104
                11676568
                39729472
                199e886e-c54b-4816-9cef-ca4ccbefc737
                © 2024 Schick et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 August 2024
                : 4 December 2024
                Page count
                Figures: 4, Tables: 2, Pages: 13
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Research and Analysis Methods
                Microscopy
                Electron Microscopy
                Transmission Electron Microscopy
                Dark Field Imaging
                Medicine and Health Sciences
                Pulmonology
                Pneumonia
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Pulmonary Imaging
                Research and Analysis Methods
                Imaging Techniques
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                Pulmonary Imaging
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                Custom metadata
                As our data involves human research participants, we cannot share them without restrictions due to ethical reasons. Our data contain potentially identifying patient information. As the data were collected from a small group of participants at our institution, even indirect identifiers such as sex and age may risk the identification of study participants. Access will be given upon reasonable request by contacting our institution: Chair of Biomedical Physics Prof. Dr. Franz Pfeiffer (contact via franz.pfeiffer@ 123456tum.de ) TUM Department of Physics TUM Faculty of Medicine Munich Institute of Biomedical Engineering (contact via office@ 123456bioengineering.tum.de ) Institute for Advanced Study Technical University of Munich James-Franck-Straße 1 85748 Garching, Germany.

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