Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19

Hae-Min Jung; Rochelle Yang; Warren B Gefter; Florin C Ghesu; Boris Mailhe; Awais Mansoor; Sasa Grbic; Dorin Comaniciu; Sebastian Vogt; Eduardo J Mortani Barbosa Jr

doi:10.1117/1.JMI.9.3.034003

Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19

J Med Imaging (Bellingham). 2022 May;9(3):034003. doi: 10.1117/1.JMI.9.3.034003. Epub 2022 Jun 17.

Affiliations

¹ University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
² Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, United States.
³ Siemens Healthineers, X-Ray Products, Malvern, Pennsylvania, United States.

Abstract

Purpose: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. Approach: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). Results: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ( $AUC = 0.67$ [0.58 to 0.76]; $accuracy = 77 %$ ). Model II performed the best ( $AUC = 0.78$ [0.71 to 0.86]; $accuracy = 81 %$ ). Model III was equivalent ( $AUC = 0.75$ [0.67 to 0.84]; $accuracy = 80 %$ ). Both models II and III outperformed model I ( $AUC difference = 0.11$ [0.02 to 0.19], $p = 0.01$ ; $AUC difference = 0.08$ [0.01 to 0.15], $p = 0.04$ , respectively). Model II and III results did not change significantly when POv was replaced by POa. Conclusions: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.

Keywords: COVID-19; artificial intelligence; chest imaging; prognosis.