Miguel Areia1, Yuichi Mori2, Loredana Correale3, Alessandro Repici4, Michael Bretthauer5, Prateek Sharma6, Filipe Taveira7, Marco Spadaccini4, Giulio Antonelli8, Alanna Ebigbo9, Shin-Ei Kudo10, Julia Arribas11, Ishita Barua12, Michal F Kaminski13, Helmut Messmann9, Douglas K Rex14, Mário Dinis-Ribeiro15, Cesare Hassan4. 1. RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto, Porto, Portugal; Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal. 2. Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway. Electronic address: yuichi.mori@medisin.uio.no. 3. Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy. 4. Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy. 5. Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway. 6. VA Medical Center and University of Kansas, Kansas City, MO, USA. 7. Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal. 8. Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy; Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy. 9. Department of Gastroenterology and Infectious Diseases, University Hospital, Augsburg, Germany. 10. Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan. 11. RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto, Porto, Portugal. 12. Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway. 13. Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland; Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland. 14. Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, IN, USA. 15. RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto, Porto, Portugal; MEDCIDS-Department of Community Medicine, Information and Decision in Health, Faculty of Porto, University of Medicine, Porto, Portugal; Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal.
Abstract
BACKGROUND: Artificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools. METHODS: We conducted Markov model microsimulation of using colonoscopy with and without AI for colorectal cancer screening for individuals at average risk (no personal or family history of colorectal cancer, adenomas, inflammatory bowel disease, or hereditary colorectal cancer syndrome). We ran the microsimulation in a hypothetical cohort of 100 000 individuals in the USA aged 50-100 years. The primary analysis investigated screening colonoscopy with versus without AI every 10 years starting at age 50 years and finishing at age 80 years, with follow-up until age 100 years, assuming 60% screening population uptake. In secondary analyses, we modelled once-in-life screening colonoscopy at age 65 years in adults aged 50-79 years at average risk for colorectal cancer. Post-polypectomy surveillance followed the simplified current guideline. Costs of AI tools and cost for downstream treatment of screening detected disease were estimated with 3% annual discount rates. The main outcome measures included the incremental effect of AI-assisted colonoscopy versus standard (no-AI) colonoscopy on colorectal cancer incidence and mortality, and cost-effectiveness of screening projected for the average risk screening US population. FINDINGS: In the primary analyses, compared with no screening, the relative reduction of colorectal cancer incidence with screening colonoscopy without AI tools was 44·2% and with screening colonoscopy with AI tools was 48·9% (4·8% incremental gain). Compared with no screening, the relative reduction in colorectal cancer mortality with screening colonoscopy with no AI was 48·7% and with screening colonoscopy with AI was 52·3% (3·6% incremental gain). AI detection tools decreased the discounted costs per screened individual from $3400 to $3343 (a saving of $57 per individual). Results were similar in the secondary analyses modelling once-in-life colonoscopy. At the US population level, the implementation of AI detection during screening colonoscopy resulted in yearly additional prevention of 7194 colorectal cancer cases and 2089 related deaths, and a yearly saving of US$290 million. INTERPRETATION: Our findings suggest that implementation of AI detection tools in screening colonoscopy is a cost-saving strategy to further prevent colorectal cancer incidence and mortality. FUNDING: European Commission and Japan Society of Promotion of Science.
BACKGROUND: Artificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools. METHODS: We conducted Markov model microsimulation of using colonoscopy with and without AI for colorectal cancer screening for individuals at average risk (no personal or family history of colorectal cancer, adenomas, inflammatory bowel disease, or hereditary colorectal cancer syndrome). We ran the microsimulation in a hypothetical cohort of 100 000 individuals in the USA aged 50-100 years. The primary analysis investigated screening colonoscopy with versus without AI every 10 years starting at age 50 years and finishing at age 80 years, with follow-up until age 100 years, assuming 60% screening population uptake. In secondary analyses, we modelled once-in-life screening colonoscopy at age 65 years in adults aged 50-79 years at average risk for colorectal cancer. Post-polypectomy surveillance followed the simplified current guideline. Costs of AI tools and cost for downstream treatment of screening detected disease were estimated with 3% annual discount rates. The main outcome measures included the incremental effect of AI-assisted colonoscopy versus standard (no-AI) colonoscopy on colorectal cancer incidence and mortality, and cost-effectiveness of screening projected for the average risk screening US population. FINDINGS: In the primary analyses, compared with no screening, the relative reduction of colorectal cancer incidence with screening colonoscopy without AI tools was 44·2% and with screening colonoscopy with AI tools was 48·9% (4·8% incremental gain). Compared with no screening, the relative reduction in colorectal cancer mortality with screening colonoscopy with no AI was 48·7% and with screening colonoscopy with AI was 52·3% (3·6% incremental gain). AI detection tools decreased the discounted costs per screened individual from $3400 to $3343 (a saving of $57 per individual). Results were similar in the secondary analyses modelling once-in-life colonoscopy. At the US population level, the implementation of AI detection during screening colonoscopy resulted in yearly additional prevention of 7194 colorectal cancer cases and 2089 related deaths, and a yearly saving of US$290 million. INTERPRETATION: Our findings suggest that implementation of AI detection tools in screening colonoscopy is a cost-saving strategy to further prevent colorectal cancer incidence and mortality. FUNDING: European Commission and Japan Society of Promotion of Science.
Authors: Jasjit S Suri; Mahesh A Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M Singh; Paramjit S Chadha; Monika Turk; Amer Johri; Narendra N Khanna; Klaudija Viskovic; Sofia Mavrogeni; John R Laird; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanase D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Meyypan Sockalingam; Ajit Saxena; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Padukode R Krishnan; Tomaz Omerzu; Subbaram Naidu; Andrew Nicolaides; Kosmas I Paraskevas; Mannudeep Kalra; Zoltán Ruzsa; Mostafa M Fouda Journal: Diagnostics (Basel) Date: 2022-06-24