tetano
Editor, Senior Moderator
Radiol Phys Technol
. 2025 Apr 28.
doi: 10.1007/s12194-025-00906-1. Online ahead of print. Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties
Takahiro Iwasaki[SUP] 1 2 [/SUP], Hidetaka Arimura[SUP] 3 [/SUP], Shohei Inui[SUP] 4 [/SUP], Takumi Kodama[SUP] 5 [/SUP], Yun Hao Cui[SUP] 5 [/SUP], Kenta Ninomiya[SUP] 6 [/SUP], Hideyuki Iwanaga[SUP] 7 [/SUP], Toshihiro Hayashi[SUP] 8 [/SUP], Osamu Abe[SUP] 4 [/SUP]
Affiliations
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.
Keywords: Accumulated Betti number map; COVID-19; Predictive model; Severity; Topological features.
. 2025 Apr 28.
doi: 10.1007/s12194-025-00906-1. Online ahead of print. Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties
Takahiro Iwasaki[SUP] 1 2 [/SUP], Hidetaka Arimura[SUP] 3 [/SUP], Shohei Inui[SUP] 4 [/SUP], Takumi Kodama[SUP] 5 [/SUP], Yun Hao Cui[SUP] 5 [/SUP], Kenta Ninomiya[SUP] 6 [/SUP], Hideyuki Iwanaga[SUP] 7 [/SUP], Toshihiro Hayashi[SUP] 8 [/SUP], Osamu Abe[SUP] 4 [/SUP]
Affiliations
- PMID: 40293683
- DOI: 10.1007/s12194-025-00906-1
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.
Keywords: Accumulated Betti number map; COVID-19; Predictive model; Severity; Topological features.