Prognostic role of COVID-19 pneumonia signs and other CT-biomarkers for survival in patients with malignant neoplasms: the ARILUS project
1
Oncology, Radiotherapy and Radiology, Northern State Medical University, Arkhangelsk, Russian Federation
2
Clinical Oncological Dispensary, Arkhangelsk, Russian Federation
3
Reaviz University (Private Educational Institution of Higher Education), Saint Petersburg, Russian Federation
4
M.K. Ammosov North-Eastern Federal University, Yakutsk, Russian Federation
5
Al-Farabi Kazakh National University (Non-commercial joint-stock company), Almaty, Kazakhstan
6
IRA Labs LLC, Moscow, Russian Federation
7
Artificial Intelligence Institute (Autonomous non-profit organization, Moscow, Russian Federation
8
I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation
Popul. Med. 2026;8(Supplement Supplement 1):A892
ABSTRACT
INTRODUCTION:
Clinically manifested pneumonia associated with COVID-19 infection in cancer patients has been linked to worse prognosis. The prognostic significance of subclinical pulmonary infiltration is poorly understood.
AIM:
To estimate survival of cancer patients with asymptomatic pneumonia detected by a multitarget artificial intelligence (AI) algorithm on chest computed tomography (CT) during the COVID-19 pandemic, and to evaluate factors associated with risk of death.
METHODS:
This population-based cohort study, conducted within the ARILUS project, included 1,147 CT examinations of cancer patients between August 2020 and May 2021. Signs of pneumonia on CT were detected by the AI algorithm in 556 (48.4%) patients with malignant neoplasms (MN) who had no clinical manifestations of infection at the time of examination. Overall survival (OS) was assessed using the life table and Kaplan-Meier methods. Multivariate Cox proportional hazards regression analysis was used to identify independent predictors of death and assess the influence of potential confounding factors.
RESULTS:
During follow-up, 680 deaths were recorded, of which 575 were due to MN progression. Three-year OS differed significantly: in patients without pulmonary infiltration on CT, it was 60.7% (95% CI 56.6%–64.7%), whereas in patients with AI-detected signs of pneumonia, it was 45.6% (95% CI 41.3%–49.8%) (p<0.001). In multivariable regression, independent factors associated with an unfavorable prognosis were pulmonary infiltration (adjusted hazard ratio (HR) 1.31; 95% CI 1.09–1.58), male sex (HR 1.25; 95% CI 1.00–1.57), and MN stage (HR 1.78 for stage II, HR 2.93 for stage III, HR 4.74 for stage IV vs stage I). Several CT markers of comorbidity were significant only in univariable analysis.
CONCLUSIONS:
Asymptomatic COVID-19 pneumonia detected by AI on CT is an independent predictor of reduced overall survival in MN patients. Automated AI screening for such changes may be recommended in routine practice.
Clinically manifested pneumonia associated with COVID-19 infection in cancer patients has been linked to worse prognosis. The prognostic significance of subclinical pulmonary infiltration is poorly understood.
AIM:
To estimate survival of cancer patients with asymptomatic pneumonia detected by a multitarget artificial intelligence (AI) algorithm on chest computed tomography (CT) during the COVID-19 pandemic, and to evaluate factors associated with risk of death.
METHODS:
This population-based cohort study, conducted within the ARILUS project, included 1,147 CT examinations of cancer patients between August 2020 and May 2021. Signs of pneumonia on CT were detected by the AI algorithm in 556 (48.4%) patients with malignant neoplasms (MN) who had no clinical manifestations of infection at the time of examination. Overall survival (OS) was assessed using the life table and Kaplan-Meier methods. Multivariate Cox proportional hazards regression analysis was used to identify independent predictors of death and assess the influence of potential confounding factors.
RESULTS:
During follow-up, 680 deaths were recorded, of which 575 were due to MN progression. Three-year OS differed significantly: in patients without pulmonary infiltration on CT, it was 60.7% (95% CI 56.6%–64.7%), whereas in patients with AI-detected signs of pneumonia, it was 45.6% (95% CI 41.3%–49.8%) (p<0.001). In multivariable regression, independent factors associated with an unfavorable prognosis were pulmonary infiltration (adjusted hazard ratio (HR) 1.31; 95% CI 1.09–1.58), male sex (HR 1.25; 95% CI 1.00–1.57), and MN stage (HR 1.78 for stage II, HR 2.93 for stage III, HR 4.74 for stage IV vs stage I). Several CT markers of comorbidity were significant only in univariable analysis.
CONCLUSIONS:
Asymptomatic COVID-19 pneumonia detected by AI on CT is an independent predictor of reduced overall survival in MN patients. Automated AI screening for such changes may be recommended in routine practice.
| eISSN: | 2654-1459 |
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