Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification

Abstract

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.

Clinical and radiological characteristics of COVID‑19 patients without comorbidities

“…In one study GGO with consolidation was more frequently revealed in progress to severe group whereas pure GGO was more likely to be found in non-severe group [18].…”

SARS-Cov-2 pneumonia phenotyping on imaging exams of patients submitted to minimally invasive autopsy

“…Therefore, this distinction is important and may have immediate therapeutic repercussions, and imaging may help (26,27). There are also other studies showing that adequate characterization of the opacities and a pattern categorization on CT can be important, highlighting the presence and extent of consolidations as a potential prognostic feature in the imaging evaluation of the disease (28)(29)(30). At last, a very recent document (31) from the European Society of Thoracic Imaging (ESTI) and the European Society of Radiology (ESR) discusses the role of imaging in the longterm follow-up of COVID-19 patients.…”

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

“…A summary of the aforementioned examples and their evaluation matrices are listed in Table 1 . Furthermore, lung lesion characterized by chest computed tomography (CT) scans were also proposed to predict disease progression ( Liu et al, 2020 , Li et al, 2020 , Wang et al, 2020b). An algorithm combining the imaging, clinical and biological attributes has been further constructed based on deep convolutional neural networks to generate a holistic forecast model, which has an area under curve (AUC) of 0.86 and 0.76 for predicting short-term and long-term mortality, respectively ( Chassagnon et al, 2021 ).…”