Detection of Lung Cancer Cases in Chest CT Scans Utilizing Artificial Intelligence: A Retrospective Analysis of Data During the COVID-19 Pandemic

Abstract

Purpose: To evaluate the potential of using an artificial intelligence (AI) to detect patients with a high likelihood of lung cancer (LC) using CTs obtained during the COVID-19 pandemic. Methods: A multicenter, retrospective study in the Krasnoyarsk region, Russia analyzed CTs of COVID-19 patients using the automated algorithm, Chest-IRA by AIRA Labs. Pulmonary nodules larger than 100 mm3 were identified by the AI and assessed by four radiologists, who categorized them into three groups: "high probability of LC", "insufficiently convincing evidence of LC", "without evidence of LC". The study also estimated the economic impact of the AI by considering labor costs and savings on treatment for patients in the early stages compared to late stages, taking into account the saved life years and their potential contribution to the gross regional product. Results: An AI identified lung nodules in 484 out of 10,500 chest CTs, with 252 cases having high and intermediate probabilities of LC. Among 100 histologically verified LC cases, 35 were in stages I-II and 65 were in stages III-IV. Using AI instead of CT review by radiologists will save 2.43 million rubles (23,786 EUR/ 26690 USD/ 196,536 CNY) in direct salary, with expected savings to the regional budget of 8.22 million rubles (80,463 EUR/ 90,466 USD/ 666,162 CNY). The financial equivalent of the life years saved was 173.25 million rubles (1,695,892 EUR/ 1905750 USD/ 14,033,250 CNY). The total effect over five years is estimated at 183.9 million rubles (1,800,142 EUR/ 2,022,907 USD/ 14,895,949 CNY). Conclusion: Using AI to evaluate large volumes of chest CTs done for reasons unrelated to lung cancer screening may facilitate early and cost-effective detection of incidental pulmonary nodules that might otherwise be missed.