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Detecting new lung cancer cases using artificial intelligence: clinical and economic evaluation of a retrospective analysis of computed tomography scans 2 years after the COVID-19 pandemic

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1. Title Title of document Detecting new lung cancer cases using artificial intelligence: clinical and economic evaluation of a retrospective analysis of computed tomography scans 2 years after the COVID-19 pandemic
2. Creator Author's name, affiliation, country Ruslan A. Zukov; Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University; Krasnoyarsk Regional Clinical Oncological Dispensary named after A.I. Kryzhanovskogo; Russian Federation
2. Creator Author's name, affiliation, country Ivan P. Safontsev; Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University; Krasnoyarsk Regional Clinical Oncological Dispensary named after A.I. Kryzhanovskogo; Russian Federation
2. Creator Author's name, affiliation, country Marina P. Klimenok; Krasnoyarsk Regional Clinical Oncological Dispensary named after A.I. Kryzhanovskogo; Russian Federation
2. Creator Author's name, affiliation, country Tatyana E. Zabrodskaya; Krasnoyarsk Regional Clinical Oncological Dispensary named after A.I. Kryzhanovskogo; Russian Federation
2. Creator Author's name, affiliation, country Natalya A. Merkulova; Krasnoyarsk Regional Clinical Oncological Dispensary named after A.I. Kryzhanovskogo; Russian Federation
2. Creator Author's name, affiliation, country Valeria Yu. Chernina; IRA Labs; Russian Federation
2. Creator Author's name, affiliation, country Mikhail G. Belyaev; IRA Labs; Russian Federation
2. Creator Author's name, affiliation, country Mikhail Yu. Goncharov; IRA Labs; Artificial Intelligence Research Institute AIRI; Skolkovo Institute of Science and Technology; Russian Federation
2. Creator Author's name, affiliation, country Vitaly V. Omelyanovskiy; Center for Expertise and Quality Control of Medical Care; Russian Medical Academy of Continuous Professional Education; Financial Research Institute; Russian Federation
2. Creator Author's name, affiliation, country Ksenia A. Ulianova; Ministry of Health of the Russian Federation; Russian Federation
2. Creator Author's name, affiliation, country Evgenia A. Soboleva; IRA Labs; Skolkovo Institute of Science and Technology; Russian Federation
2. Creator Author's name, affiliation, country Maria E. Blokhina; AstraZeneca Pharmaceuticals LLC; Russian Federation
2. Creator Author's name, affiliation, country Elena A. Nalivkina; AstraZeneca Pharmaceuticals LLC; Russian Federation
2. Creator Author's name, affiliation, country Victor A. Gombolevskiy; IRA Labs; Artificial Intelligence Research Institute AIRI; World-Class Research Center «Digital biodesign and personalized healthcare»; Sechenov First Moscow State Medical University; Russian Federation
3. Subject Discipline(s)
3. Subject Keyword(s) lung cancer; computed tomography; artificial intelligence; chest; health economics; performance evaluation
4. Description Abstract

BACKGROUND: Chest computed tomography (CT) is the main modality used to diagnose lung lesions caused by COVID-19 infection. Since 2020, the use of this modality in the Krasnoyarsk krai has increased. However, the incidence of lung cancer decreased by 5.2%. The current situation has raised concerns about missing radiographic signs typical of lung cancer and has stimulated the search for new diagnostic modalities using artificial intelligence (AI) for data analysis.

AIM: The aim of the study was to evaluate the feasibility of using an AI algorithm to search for lung nodules based on chest CT data obtained during the COVID-19 pandemic to identify lung cancer.

MATERIALS AND METHODS: The retrospective study included chest CT scans of patients from Krasnoyarsk krai diagnosed with COVID-19 reported in the PACS base between 1 November 2020 and 28 February 2021. The interval between chest CT and AI analysis ranged from two years and one month to two years and five months. Chest-IRA algorithm was used. AI detected lung nodules with a volume greater than 100 mm3. The radiologists divided the results into three groups based on the potential for lung cancer. The assessment of the economic benefits of using the AI algorithm considered the cost of wages and savings in the treatment of early stage lung cancer, which affects gross regional product.

RESULTS: The AI algorithm identified nodules in 484 out of 10,500 CT scans. A total of 192 patients with a high potential for lung cancer, 103 with no signs and 60 with inconclusive signs were identified, and 112 patients with a high and moderate potential for lung cancer did not seek medical care. AI confirmed 100 (28.2%) histologically proven cases of lung cancer, with stages I–II detected in 35%.

Using AI instead of radiologists would save 25 months and 4 days of work, which is equal to 2 million 430 thousand rubles. Expected budget savings due to early detection of lung cancer vary from 10 million 600 thousand to 12 million 500 thousand rubles for each 10,500 CTs. The total economic effect for a five year period would be from 259 million 400 thousand rubles to 305 million 100 thousand rubles.

CONCLUSIONS: The use of AI to evaluate chest CT scans demonstrates high performance in identifying lung nodules, including those in patients with COVID-19, confirming its potential use for early detection of incidental lung nodules that might otherwise be missed.

5. Publisher Organizing agency, location Eco-Vector
6. Contributor Sponsor(s)
7. Date (DD-MM-YYYY) 05.11.2024
8. Type Status & genre Peer-reviewed Article
8. Type Type Research Article
9. Format File format PDF (Rus), PDF (Rus),
10. Identifier Uniform Resource Identifier https://jdigitaldiagnostics.com/DD/article/view/630885
10. Identifier Digital Object Identifier (DOI) 10.17816/DD630885
10. Identifier Digital Object Identifier (DOI) (PDF (Rus)) 10.17816/DD630885-159482
11. Source Title; vol., no. (year) Digital Diagnostics; Vol 5, No 4 (2024)
12. Language English=en ru
13. Relation Supp. Files Fig. 1. Block diagram of the study. CT OGK — computed tomography of the chest organs; AI — artificial intelligence; ZNO — malignant neoplasm; C34 — malignant neoplasm of the bronchi and lungs in accordance with the International Classification of Diseases of the tenth revision. (268KB) doi: 10.17816/DD630885-4230394
Fig. 2. The principle of analyzing the results of computed tomography of the chest organs using artificial intelligence. CT OGK — computed tomography of the chest organs; AI — artificial intelligence; KKKOD — Krasnoyarsk Regional Clinical Oncological Dispensary named after A.I. Kryzhanovsky; ZNO — malignant neoplasm. 134 patients, highlighted by the red dotted line, did not contact medical organizations about malignant neoplasms. (336KB) doi: 10.17816/DD630885-4230395
Fig. 3. Pulmonary node in the left lung, identified using artificial intelligence. The detected node is marked with a red square. An image showing a high probability of lung cancer. (118KB) doi: 10.17816/DD630885-4230396
4. Pulmonary nodules in the right (a) and left (b) lungs, identified using artificial intelligence. The detected nodes are marked with a red square. Images with insufficiently convincing signs of lung cancer. (218KB) doi: 10.17816/DD630885-4230397
5. The results of computed tomography of the chest organs of patients with verified lung cancer (indicated by blue arrows). a — solid cystic formation of the left lung (stage Ia); b — solid formation of the right lung (stage Ib). (133KB) doi: 10.17816/DD630885-4230398
Fig. 6. Examples of the most common false-positive cases of artificial intelligence algorithm activation: a — fibrous changes are noted as a pulmonary node; b — a site of infiltration of lung tissue is marked as a pulmonary node. (192KB) doi: 10.17816/DD630885-4230399
7. Evolution of lung cancer screening in the Krasnoyarsk Territory. (251KB) doi: 10.17816/DD630885-4230400
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
15. Rights Copyright and permissions Copyright (c) 2024 Eco-Vector
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