Prevalence of COVID-19-associated pneumonia signs on chest computed tomography in cancer patients: the ARILUS study

Aim. To study the prevalence of pneumonia features associated with 2019 coronavirus disease (COVID-19) in cancer patients based on chest computed tomography (CT) data using an artificial intelligence (AI) algorithm.
Materials and methods. A cross-sectional study was conducted as part of the ARILUS project. Using multitarget AI, CT images of 1148 patients examined at the Arkhangelsk Clinical Oncology Dispensary from 01.04.2020 to 31.12.2021 were analyzed. Patients were divided into groups: without signs of pneumonia (n = 592, 51.6%) and with signs of pneumonia (n = 556, 48.4%). In 95.3% of patients with pneumonia, the lesion volume was less than 25% (CT-1). Using multivariate Poisson regression, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were calculated.
Results. For demographic characteristics such as gender, age, place of residence, no relationship with the presence of signs of COVID-19 pneumonia was established. Topography of neoplasm is associated with the presence of signs of COVID-19 pneumonia (reference group – cancers of the female genital organs): lung cancer – aPR 1.87; 95% CI: 1.40–2.49; head and neck cancers – aPR 1.85; 95% CI: 1.32–2.58; upper gastrointestinal tract – aPR 1.51; 95% CI: 1.12–2.04; breast cancer – aPR: 1.38; 95% CI: 1.00–1.90; p < 0.01. The presence of pulmonary emphysema is associated with signs of COVID-19 pneumonia: aPR 1.25; 95% CI: 1.09–1.45, p = 0.002. With an increase in the Agatston score (AS) reflecting coronary artery calcification (reference group absence of calcification), the association with the presence of signs of COVID-19 pneumonia increased – for AS 1–99: aPR 1.24; 95% CI: 1.05–1.47; AS 100– 299: aPR 1.58; 95% CI: 1.33–1.87; AS 300 and above: aPR 1.61; 95% CI: 1.36–1.90; p < 0.001 for a linear trend.
Conclusion. Factors associated with the detection of COVID-19 pneumonia among cancer patients include the localization of neoplasms in the lungs, head and neck organs, upper gastrointestinal tract, breast, and as well as the presence of signs of emphysema and coronary calcification according to CT data

1. Dinmohamed A.G., Visser O., Verhoeven R.H.A., et al. Fewer cancer diagnoses during the COVID-19 epidemic in the Netherlands. Lancet Oncol. 2020 Jun; 21(6): 750–751. doi: 10.1016/S1470-2045(20)30265-5. Epub 2020 Apr 30. Erratum in: Lancet Oncol. 2020 Jun; 21(6): e304. https://doi.org/10.1016/S1470-2045(20)30267-9. PMID: 32359403

2. Barclay N.L., Pineda Moncusí M., Jödicke A.M., et al. The impact of the UK COVID-19 lockdown on the screening, diagnostics and incidence of breast, colorectal, lung and prostate cancer in the UK: a population-based cohort study. Front Oncol. 2024 Mar 27; 14: 1370862. https://doi.org/10.3389/fonc.2024.1370862. PMID: 38601756

3. Злокачественные новообразования в России в 2020 году (заболеваемость и смертность). Под ред. А.Д. Каприна, В.В. Старинского, А.О. Шахзадовой Злокачественные новообразования в России в 2020 году ( заболеваемость и смертность) – М.: МНИОИ им. П.А. Герцена – филиал ФГБУ «НМИЦ радиологии» Минздрава России, 2021. 252 с.

4. Lohfeld L., Sharma M., Bennett D., et al. Impact of the COVID-19 pandemic on breast cancer patient pathways and outcomes in the United Kingdom and the Republic of Ireland – a scoping review. Br J Cancer. 2024 Sep; 131(4): 619–626. https://doi.org/10.1038/s41416-024-02703-w. Epub 2024 May 4. Erratum in: Br J Cancer. 2024 Sep; 131(4): 778. https://doi.org/10.1038/s41416-024-02791-8. PMID: 38704477

5. Валькова Л.Е., Дяченко А.А., Мерабишвили В.М. и др. Влияние пандемии COVID-19 на показатели заболеваемости злокачественными опухолями, подлежащими скринингу в рамках диспансеризации (популяционное исследование). Сибирский онкологический журнал. 2022; 21(6): 7–16. https://doi.org/10.21294/1814-4861-2022-21-6-7-16. EDN: COFCHN

6. Котляров П.М., Сергеев Н.И., Солодкий В.А., Солдатов Д.Г. Мультиспиральная компьютерная томография в ранней диагностике пневмонии, вызванной SARS-CoV-2, Пульмонология. 2020; 30(5): 561–568. https://doi.org/10.18093/0869-0189-2020-30-5-561-568. EDN: RJGOCV

7. Чернина В.Ю., Беляев М.Г., Силин А.Ю. и др. Диагностическая и экономическая оценка применения комплексного алгоритма искусственного интеллекта, направленного на выявление десяти патологических находок по данным компьютерной томографии органов грудной клетки. Digital Diagnostics. 2023; 4(2): 105–132. https://doi.org/10.17816/DD321963. EDN: UGUJWJ

8. Allemani C., Matsuda T., Di Carlo V., et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018 Mar 17; 391(10125): 1023–1075. https://doi.org/10.1016/S0140-6736(17)33326-3. Epub 2018 Jan 31. PMID: 2939526

9. Allemani C., Weir H.K., Carreira H., et al. Global surveillance of cancer survival 1995-2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet. 2015 Mar 14; 385(9972): 977–1010. https://doi.org/10.1016/S0140-6736(14)62038-9. Epub 2014 Nov 26. Erratum in: Lancet. 2015 Mar 14; 385(9972): 946. PMID: 25467588

10. Barchuk A., Tursun-Zade R., Nazarova E., et al. Completeness of regional cancer registry data in Northwest Russia 2008-2017. BMC Cancer. 2023 Oct 18; 23(1): 994. https://doi.org/10.1186/s12885-023-11492-z. PMID: 37853404

11. Вальков М.Ю., Гржибовский А.М., Кудрявцев А.В. и др. Использование искусственного интеллекта для прогнозирования и предотвращении неонкологической смертности у онкологических больных: протокол исследования АРИЛИС. Экология человека. 2024; 31(4): 314–330. https://doi.org/10.17816/humeco635357. EDN: DDFTVK

12. Морозов С.П., Гомболевский В.А., Чернина В.Ю. и др. Прогнозирование летальных исходов при COVID-19 по данным компьютерной томографии органов грудной клетки. Туберкулез и болезни легких. 2020; 98(6): 7–14. https://doi.org/10.21292/2075-1230-2020-98-6-7-14. EDN: IBBYVG /

13. Jazieh A.R., Bounedjar A., Abdel-Razeq H., et al. Impact of COVID-19 on Management and Outcomes of Oncology Patients: Results of MENA COVID-19 and Cancer Registry (MCCR). J Immunother Precis Oncol. 2024 May 2; 7(2): 82–88. https://doi.org/10.36401/JIPO-23-38. PMID: 38721403

14. Keene S., Abbasizanjani H., Torabi F., et al. Risks of major arterial and venous thrombotic diseases after hospitalisation for influenza, pneumonia, and COVID-19: A population-wide cohort in 2.6 million people in Wales. Thromb Res. 2025 Jan; 245: 109213. https://doi.org/10.1016/j.thromres.2024.109213. Epub 2024 Nov 19. PMID: 39608301

15. Attaway A.H., Scheraga R.G., Bhimraj A., et al. Severe covid-19 pneumonia: pathogenesis and clinical management. BMJ. 2021 Mar 10; 372: n436. https://doi.org/10.1136/bmj.n436. PMID: 33692022

16. Fan L., Wu S., Wu Y., et al. Clinical data and quantitative CT parameters combined with machine learning to predict short-term prognosis of severe COVID-19 in the elderly. Heliyon. 2024 Sep 7; 10(18): e37096. https://doi.org/10.1016/j.heliyon.2024.e37096. PMID: 39309817

17. Hu Z., Song C., Xu C., et al. Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China. Sci China Life Sci. 2020 May; 63(5): 706–711. https://doi.org/10.1007/s11427-020-1661-4. Epub 2020 Mar 4. PMID: 32146694

18. Wang Y., Liu Y., Liu L., et al. Clinical outcomes in 55 patients with Severe Acute Respiratory Syndrome Coronavirus 2 who were asymptomatic at hospital admission in Shenzhen, China. J Infect Dis. 2020 May 11; 221(11): 1770–1774. https://doi.org/10.1093/infdis/jiaa119. PMID: 32179910

19. Leung J.M., Yang C.X., Tam A., et al. ACE-2 expression in the small airway epithelia of smokers and COPD patients: implications for COVID-19. Eur Respir J. 2020 May 14; 55(5): 2000688. https://doi.org/10.1183/13993003.00688-2020. PMID: 32269089

20. Simons D., Shahab L., Brown J., Perski O. The association of smoking status with SARS-CoV-2 infection, hospitalization and mortality from COVID-19: a living rapid evidence review with Bayesian meta-analyses (version 7). Addiction. 2021 Jun; 116(6): 1319–1368. https://doi.org/10.1111/add.15276. Epub 2020 Nov 17. PMID: 33007104

21. Oliveira F.E.S., Oliveira M.C.L, Martelli Júnior H. et al. The impact of smoking on COVID-19-related mortality: a Brazilian national cohort study. Addict Behav. 2024 Sep; 156: 108070. https://doi.org/10.1016/j.addbeh.2024.108070. Epub 2024 May 25. PMID: 38796931

22. Griffith N.B., Baker T.B., Heiden B.T., et al. Cannabis, tobacco use, and COVID-19 outcomes. JAMA Netw Open. 2024 Jun 3; 7(6): e2417977. doi: 10.1001/jamanetworkopen.2024.17977. Erratum in: JAMA Netw Open. 2024 Jul 1; 7(7): e2427937. https://doi.org/10.1001/jamanetworkopen.2024.27937. PMID: 38904961

23. Боева Е.В., Беляков Н.А., Симакина О.Е. и др. Эпидемиология и течение инфекционных заболеваний на фоне пандемии COVID-19. Сообщение 2. Реализация интерференции между SARS-CoV-2 и возбудителями острых респираторных вирусных инфекций. Инфекция и иммунитет. 2022; 12(6): 1029– 1039. https://doi.org/10.15789/2220-7619-EAC-1960. EDN: ZMXIGW

24. Martinez-Fierro M.L., González-Fuentes C., Cid-Guerrero D., et al. Radiological findings increased the successful of COVID-19 diagnosis in hospitalized patients suspected of respiratory viral infection but with a negative first SARS-COV-2 RT-PCR result. Diagnostics (Basel). 2022 Mar 11; 12(3): 687. https://doi.org/10.3390/diagnostics12030687. PMID: 35328241

25. Sahutoğlu E., Kabak M., Çil B., et al. Radiologic severity index can be used to predict mortality risk in patients with COVID-19. Tuberk Toraks. 2024 Dec; 72(4): 280–287. English. https://doi.org/10.5578/tt.202404994. PMID: 39745227

26. Schalekamp S., Bleeker-Rovers C.P., Beenen L.F.M., et al. Chest CT in the emergency department for diagnosis of COVID-19 pneumonia: Dutch experience. Radiology. 2021 Feb; 298(2): E98–E106. https://doi.org/10.1148/radiol.2020203465. Epub 2020 Nov 17. PMID: 33201791