Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging.
Purpose: In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 13; pp. 4339 - 4350 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153415955&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153415955 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Dec2021 vid: 48 iid: 13 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153415955 150948453 10.1007/s00259-021-05435-8 153415955 ppf: 4339 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging. aug: au: Wang, Cheng Huang, Lu Xiao, Sa Li, Zimeng Ye, Chaohui Xia, Liming Zhou, Xin affil: School of Physics, Huazhong University of Science and Technology, 430074, Wuhan, China sug: ab: Purpose: In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT. Methods: COVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions. Results: A total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2–12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R2 = 0.80; P < 0.001) and good consistency in the Bland–Altman plot (mean bias = 0.04 cm3). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R2 = 0.76; P < 0.001) with true lesion enlargements. Conclusion: The present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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