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Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.
| Content Provider | Europe PMC |
|---|---|
| Author | Arru, Chiara Ebrahimian, Shadi Falaschi, Zeno Hansen, Jacob Valentin Pasche, Alessio Lyhne, Mads Dam Zimmermann, Mathis Durlak, Felix Mitschke, Matthias Carriero, Alessandro Nielsen-Kudsk, Jens Erik Kalra, Mannudeep K. Saba, Luca |
| Copyright Year | 2021 |
| Abstract | PurposeComparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.MethodsThe multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >−200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis.ResultsModerate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission.ConclusionDL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8247202&blobtype=pdf |
| ISSN | 08997071 |
| Journal | Clinical Imaging [Clin Imaging] |
| Volume Number | 80 |
| DOI | 10.1016/j.clinimag.2021.06.036 |
| PubMed Central reference number | PMC8247202 |
| PubMed reference number | 34246044 |
| e-ISSN | 18734499 |
| Language | English |
| Publisher | Published by Elsevier Inc. |
| Publisher Date | 2021-07-01 |
| Access Restriction | Open |
| Rights License | Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. © 2021 Published by Elsevier Inc. |
| Subject Keyword | DL, Deep learning ANOVA, Analysis or variance RT-PCR, Reverse transcriptase-polymerase chain reaction CXR, Chest X-ray CT, Computed tomography SARS-CoV-2 Chest CT Pneumonia Deep learning Radiomics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging |
