Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.

Autor: Homayounieh F; Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA. fhomayounieh@mgh.harvard.edu., Bezerra Cavalcanti Rockenbach MA; MGH & BWH Center for Clinical Data Science, Boston, MA, USA., Ebrahimian S; Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA., Doda Khera R; Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA., Bizzo BC; Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.; MGH & BWH Center for Clinical Data Science, Boston, MA, USA., Buch V; MGH & BWH Center for Clinical Data Science, Boston, MA, USA., Babaei R; Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran., Karimi Mobin H; Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran., Mohseni I; Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran., Mitschke M; Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany., Zimmermann M; Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany., Durlak F; Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany., Rauch F; Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany., Digumarthy SR; Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA., Kalra MK; Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.
Jazyk: angličtina
Zdroj: Journal of digital imaging [J Digit Imaging] 2021 Apr; Vol. 34 (2), pp. 320-329. Date of Electronic Publication: 2021 Feb 25.
DOI: 10.1007/s10278-021-00430-9
Abstrakt: To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.
(© 2021. Society for Imaging Informatics in Medicine.)
Databáze: MEDLINE
Externí odkaz: