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Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.
J Digit Imaging. 2021 Apr; 34(2):320-329.JD

Abstract

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.

Authors+Show Affiliations

Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA. fhomayounieh@mgh.harvard.edu.MGH & BWH Center for Clinical Data Science, Boston, MA, USA.Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA. MGH & BWH Center for Clinical Data Science, Boston, MA, USA.MGH & BWH Center for Clinical Data Science, Boston, MA, USA.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany.Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany.Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany.Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany.Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.

Pub Type(s)

Journal Article
Multicenter Study
Observational Study

Language

eng

PubMed ID

33634416

Citation

Homayounieh, Fatemeh, et al. "Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome." Journal of Digital Imaging, vol. 34, no. 2, 2021, pp. 320-329.
Homayounieh F, Bezerra Cavalcanti Rockenbach MA, Ebrahimian S, et al. Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. J Digit Imaging. 2021;34(2):320-329.
Homayounieh, F., Bezerra Cavalcanti Rockenbach, M. A., Ebrahimian, S., Doda Khera, R., Bizzo, B. C., Buch, V., Babaei, R., Karimi Mobin, H., Mohseni, I., Mitschke, M., Zimmermann, M., Durlak, F., Rauch, F., Digumarthy, S. R., & Kalra, M. K. (2021). Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. Journal of Digital Imaging, 34(2), 320-329. https://doi.org/10.1007/s10278-021-00430-9
Homayounieh F, et al. Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. J Digit Imaging. 2021;34(2):320-329. PubMed PMID: 33634416.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. AU - Homayounieh,Fatemeh, AU - Bezerra Cavalcanti Rockenbach,Marcio Aloisio, AU - Ebrahimian,Shadi, AU - Doda Khera,Ruhani, AU - Bizzo,Bernardo C, AU - Buch,Varun, AU - Babaei,Rosa, AU - Karimi Mobin,Hadi, AU - Mohseni,Iman, AU - Mitschke,Matthias, AU - Zimmermann,Mathis, AU - Durlak,Felix, AU - Rauch,Franziska, AU - Digumarthy,Subba R, AU - Kalra,Mannudeep K, Y1 - 2021/02/25/ PY - 2020/07/29/received PY - 2021/02/02/accepted PY - 2021/01/08/revised PY - 2021/2/27/pubmed PY - 2021/8/19/medline PY - 2021/2/26/entrez KW - COVID-19 pneumonia KW - CT KW - Deep learning KW - Motion artifacts KW - Patient outcome SP - 320 EP - 329 JF - Journal of digital imaging JO - J Digit Imaging VL - 34 IS - 2 N2 - 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. SN - 1618-727X UR - https://www.unboundmedicine.com/medline/citation/33634416/Multicenter_Assessment_of_CT_Pneumonia_Analysis_Prototype_for_Predicting_Disease_Severity_and_Patient_Outcome_ L2 - https://doi.org/10.1007/s10278-021-00430-9 DB - PRIME DP - Unbound Medicine ER -