Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.
Authors
Affiliations (8)
Affiliations (8)
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. [email protected].
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan. [email protected].
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
- Harry Perkins Institute of Medical Research, The University of Western Australia, Western Australia, Australia.
- Division of Financial Strategy Management, The University of Tokyo Hospital, Tokyo, Japan.
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
Abstract
Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-19 pneumonia could help clinical physicians describe the reasons for their decisions. We aimed to construct predictive models of SVD in patients with COVID-19 pneumonia at an early stage on computed tomography (CT) images using SVD-specific features that can be visualized on accumulated Betti number (BN) maps. BN maps (b0 and b1 maps) were generated by calculating the BNs within a shifting kernel in a manner similar to a convolution. Accumulated BN maps were constructed by summing BN maps (b0 and b1 maps) derived from a range of multiple-threshold values. Topological features were computed as intrinsic topological properties of COVID-19 pneumonia from the accumulated BN maps. Predictive models of SVD were constructed with two feature selection methods and three machine learning models using nested fivefold cross-validation. The proposed model achieved an area under the receiver-operating characteristic curve of 0.854 and a sensitivity of 0.908 in a test fold. These results suggested that topological image features could characterize COVID-19 pneumonia at an early stage as SVD.