The possibilities of machine learning in determining the variants of the course of viral pneumonia associated with COVID-19 based on computed tomography data

Introduction. In acute COVID-19 respiratory infection caused by SARS-CoV-2 coronavirus (2019-nCov), lung damage has a different course, which has not been studied so far. The purpose of the study. To study the variants of the course of COVID-19 viral pneumonia (VP) based on the analysis of the dynamics of lung damage, quantified by computed tomography.

Material and methods. Quantitative analysis of computed tomography (CT) data of the chest of 144 patients with VP was performed using the 3D Slicer software application. Cluster and comparative nonparametric analyses of the severity of lung damage (CT1, CT2, СТЗ, CT4) and the total volume of affected lungs (%) obtained during primary and two repeated CT studies (pCT, 1dCT and 2dCT) in the program "Statistica 12" were carried out.

Results. With a stable course, the total volume of the affected lungs is constant, within one degree of severity: CT1 - in 23.6 %, CT2 - in 14.6 %, CT3 - in 5.6 %, CT4 - in 8.3 %. With a progressive course, the volume of lung damage gradually increases from minimum to maximum CT1-CT2-CT3 in 3.5 %, or increases to the maximum level on the second CT, remaining stable on the third study (CT2-CT3-CT3) - in 4.2 %. With a regredient course, the volume of lung damage varies from the maximum at the primary examination (pCT) to the minimum at the third (2dCT): gradually from CT4 through CT3 to CT2 in 4.2 %, or by one degree of severity CT3-CT2-CT2 in 9.7 %, CT3-CT3-CT2 in 9.0 %. With a progressive-regredient course, the volume of lung damage first reaches a maximum on 1dCT, a minimum on 2dCT - CT2-CT3-CT2 - in 17.4 %. An intragroup comparison of three repeated, dependent indicators of the total volume of the affected lungs and an intergroup comparison on pCT, 1dCT and 2dCT showed a dynamic statistically significant difference between them for variants of the course of VP (p < 0.05).

Conclusions. Cluster analysis of the total volume of affected lungs on a series of three CT studies in dynamics allowed us to identify 5 variants of the course of COVID-19 - stable - light, stable-severe, progressive, regredient, progressive-regredient.

1. Bernheim A., Mei X., Huang М., Yang У., Fayad Z.A., Zhang N., et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology. 2020;295(3):200463. https://doi.Org/10.1148/radiol.2020200463

2. Pan F., Ye T., Sun P., Gui S., Liang B., Li L., et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020;295(3):715-721. https://doi.org/10.1148/radiol.2020200370

3. Wang Y., Dong C., Hu Y., Li C., Ren Q., Zhang X., et al. Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study. Radiology. 2020;296(2):E55-E64. https://doi.org/10.1148/radiol.2020200843

4. Zhang H., Liu X., Yu P., Cheng M., Wang W., Sun Y., et al. Dynamic CT assessment of disease change and prognosis of patients with moderate COVID-19 pneumonia. JXray Sci Techno!. 2020;28(5):851-861. https://doi.org/10.3233/XST-200711

5. Zhou S., Wang Y., Zhu T., Xia L. CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China. AJRAm J Roentgenol. 2020;214(6):1287-1294. https://doi.org/10.2214/AJR.20.22975

6. Grassi R., Cappabianca S., Urraro F., Granata V., Giacobbe G., Magliocchetti S., et al. Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool. J Pers Med. 2021; 11 (7):641. https://doi.Org/10.3390/jpm11070641

7. Morozov S.P. Protsenko D.N., Smetanina S.V., Andreychenko A. E., Ambrosi О. E., Balanyuk E. A., Radiation diagnostics of coronavirus disease (COVID-19): organization, methodology, interpretation of results: Preprint No. CDT-2020-II. Version 2 from 04/17/2020. /comp. The series "Best practices of radiation and instrumental diagnostics". Issue 65. Moscow, 2020. 60 p. (In Russ).

8. 8 Gashev S.N., Betlyaev F.Kh., Lupinos M.Yu. Mathematical methods in biology: analysis of biological data in the system “Statistica”. Tyumen, Tyumen State University Publishing House, 2014. 208 p. (In Russ)].

9. 9 Khalafyan А.А. Modern statistical methods of medical research. Ed. 3. URSS, 2014. 396 p. (In Russ)].

10. 10 Borovikov V.P. A popular introduction to modern data analysis in the STATISTICA system. Textbook for universities. Moscow, Hotline - Telecom, 2018. 288 p. (In Russ)].

11. 11 Burkov A. Machine learning without unnecessary words. St. Petersburg, Peter, 2020. 192 p. (In Russ).

12. 12 Kobzar1 A.I. Applied mathematical statistics. For engineers and scientists. Moscow, 2006. 816 p. (In Russ).

13. 13 Sysa A.G. Statistical analysis in biology and medicine. Minsk, 2018. 140 p (In Russ).

14. 14 Zhou S., Zhu T., Wang Y., Xia L. Imaging features and evolution on CT in 100 COVID-19 pneumonia patients in Wuhan, China. Eur Radiol. 2020;30(10):5446-5454. https://doi.org/10.1007/s00330-020-06879-6

15. 15 Zhan J., Li H., Yu H., Liu X., Zeng X., Peng D., et al. 2019 novel coronavirus (COVID-19) pneumonia: CT manifestations and pattern of evolution in 110 patients in Jiangxi, China. Eur Radiol. 2021 ;31 (2)4 059-1068. https://doi.org/10.1007/s00330-020-07201-0

16. 16 Petrikov S.S., Popova I.E., Muslimov R.Sh., Popugaev K.A., Kislukhina E.V., Kokov L.S. Possibilities of computed tomography in assessing the degree of lung damage in COVID-19 patients under dynamic observation. REJR. 202040(2)44-26. (In Russ). https://doi.org/10.21569/2222-7415-2020-10-2-14-26