Using artificial intelligence to predict and prevent non-cancer mortality in patients with cancer: ARILIS study protocol
Aim: To present the ARILIS study aimed at assessing the use of artificial intelligence to analyze chest computed tomography (CT) data to predict and prevent non-cancer mortality in patients with cancer. Material and methods: This cohort study will include patients with cancer diagnosed in the Arkhangelsk region (AR) within the 2019–2023 period. The COVID-19 patients with pneumonia, patients with general medical conditions, and the population of the Know Your Heart Study are planned to be enrolled as control groups. To detect and quantify the CT signs of the cardiovascular, pulmonary, and bone disorders, the thoracic СT scans of all the subjects will be processed using the multi-targeted AI algorithm provided by the IRA Labs company. From the date of processing of the thoracic CT scans using the multi-targeted AI algorithm, the study subjects will be followed for new clinical diagnoses and all-cause mortality. Expected results: T he study will determine the prevalence of CT signs of the cardiovascular, pulmonary, and bone disorders in patients with cancer compared with the Know Your Heart Study population sample. It will also assess the incidence of cardiovascular, pulmonary, and bone events and all-cause mortality in patients with cancer compared with control groups, explore the potential of the IRA Labs’ multi-targeted AI algorithm in the assessment and reclassification of assessed risks in patients with cancer, and provide a software product for using mtIA in healthcare practice.
Authors
- Sergey Nikolaevich Avdeev (ORCID: https://orcid.org/0000-0002-5999-2150)
- Maxim Pisov (ORCID: https://orcid.org/0000-0001-8727-5792)
- A M Grjibovski (ORCID: https://orcid.org/0000-0002-5464-0498)
- М. Yu. Valkov (ORCID: https://orcid.org/0000-0003-3230-9638)
- Mikhail Belyaev (ORCID: https://orcid.org/0000-0001-9906-6453)
- Zelimkhan G. M. Berikkhanov (ORCID: https://orcid.org/0000-0002-4335-3987)
- EA Petrash (ORCID: https://orcid.org/0000-0001-6572-5369)
- Alexander V. Kudryavtsev (ORCID: https://orcid.org/0000-0001-8902-8947)
- Eugenia Soboleva (ORCID: https://orcid.org/0009-0009-4037-6911)
- D. V. Bogdanov (ORCID: https://orcid.org/0000-0002-4105-326X)
- Alexey Shevtsov (ORCID: https://orcid.org/0000-0003-3085-4325)
- Victor А. Gombolevskiy (ORCID: https://orcid.org/0000-0003-1816-1315)
- Marina Sekacheva (ORCID: https://orcid.org/0000-0003-0015-7094)
- Y. Ashikhmin (ORCID: https://orcid.org/0000-0002-1243-5701)
- Andrey Dyachenko (ORCID: https://orcid.org/0000-0001-8421-5305)
- Zhanna Belaya (ORCID: https://orcid.org/0000-0002-6674-6441)
- V. V. Оmelyanovskiy (ORCID: https://orcid.org/0000-0003-1581-0703)
- Valeria Chernina (ORCID: https://orcid.org/0000-0002-0302-293X)
- Ekaterina Panina
- Mariia Donskova
- Farukh Yaushev (ORCID: https://orcid.org/0009-0006-1210-5311)
- Regina Gareeva
- Н.С. Серова (ORCID: https://orcid.org/0000-0001-6697-7824)
- Maria V. Basova (ORCID: https://orcid.org/0009-0000-3325-8452)
- Mariya N. Dugova (ORCID: https://orcid.org/0009-0004-5586-8015)
- M BOGDANOV (ORCID: https://orcid.org/0009-0002-3469-658X)
- Mikhail Yu. Goncharov (ORCID: https://orcid.org/0009-0009-8417-0878)
- Vilgelm V. Volman
- Aleksandr S. Gershtanskiy
Institutions
- Northern (Arctic) Federal University (RU)
- North-Eastern Federal University (RU)
- Moscow Institute of Physics and Technology (RU)
- Sechenov University (RU)
- Endocrinology Research Center (RU)
- Artificial Intelligence Research Institute (ES)
- Arkhangel'skiy Klinicheskiy Onkologicheskiy Dispanser (RU)
- Federal State Budgetary Institution "Center for Expertise and Quality Control of Medical Care" (RU)
- Northern State Medical University (RU)
Publication Details
- Journal
- Ekologiya Cheloveka (Human Ecology)
- Published
- 2024-12-12
- DOI
- https://doi.org/10.17816/humeco635357
- Citations
- 1
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.40