Artificial Intelligence in Lung Cancer Detection: Clinical and Economic Assessment of Retrospective CT Analysis Two Years Post-COVID-19 Pandemic

Abstract

BACKGROUND: The use of chest computed tomography (CT) scans in Krasnoyarsk Krai, Russia, has increased since 2020, during the COVID-19 pandemic. This period also saw a 5.2% decrease in lung cancer (LC) incidence. The potential for missed LC cases has led to the investigation of new diagnostic methods, including the use of artificial intelligence (AI) for analyzing retrospective data.AIM:The study aimed to evaluate the effectiveness of an AI algorithm in identifying patients at high risk for LC using chest CT data from the COVID-19 pandemic.METHODS: A retrospective analysis was conducted on chest CTs from patients diagnosed with COVID-19 in the Krasnoyarsk region, using scans from November 1, 2020, to February 28, 2021. The AI algorithm "Chest-IRA" was applied to detect pulmonary nodules larger than 100 mm³. Radiologists classified the nodules detected by AI into three categories based on LC probability. The economic assessment of the AI algorithm included salary costs and potential savings from early LC treatment.RESULTS: Out of 10,500 CTs, the AI algorithm found nodules in 484 cases. Of these, 192 were highly likely to have LC, 103 showed no signs, and 60 were inconclusive. 112 patients with high or intermediate risk did not seek treatment. The AI confirmed lung cancer in 100 cases, 28.2% of those detected. Early-stage LC was found in 35% of cases, while 65% were at later stages. AI could save about 25.04 months of radiologist work, costing 2.43 million RUB. Early detection savings are estimated at 10.6 to 12.5 million RUB per 10,500 CTs, with a five-year economic impact of 259.4 to 305.1 million RUB.CONCLUSION: AI has proven effective in identifying pulmonary nodules amidst COVID-19, highlighting its potential to improve early detection and diagnostic accuracy, leading to earlier and more precise treatments.