| Title: |
Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study |
| Authors: |
Nguyen, Olav Toai Duc; Fotopoulos, Ioannis; Markaki, Maria; Tsamardinos, Ioannis; Lagani, Vincenzo; Røe, Oluf Dimitri |
| Contributors: |
Saudi Data and Artificial Intelligence Authority (SDAIA)-KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal, Saudi Arabia; Biological, Environmental Sciences and Engineering; Biological and Environmental Science and Engineering (BESE) Division; Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Troendelag Hospital Trust, Cancer Clinic, Levanger, Norway; Department of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece; Institute of Applied and Computational Mathematics, Heraklion, Greece; JADBio Gnosis Data Analysis (DA) S.A., Science and Technology Park of Crete (STEP-C), Heraklion, Greece; Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia; Clinical Cancer Research Center and Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark |
| Publisher Information: |
Elsevier BV |
| Publication Year: |
2024 |
| Collection: |
King Abdullah University of Science and Technology: KAUST Repository |
| Description: |
Background: Improving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here, we compared the performance of the Helseundersøkelsen i Nord-Trøndelag (HUNT) Lung Cancer Model (HUNT LCM) versus the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON)) and 2021 United States Preventive Services Task Force (USPSTF) criteria regarding LC risk prediction and efficiency. Methods: We used linked data from 10 Norwegian prospective population-based cohorts, Cohort of Norway. The study included 44,831 ever-smokers, of which 686 (1.5%) patients developed LC; the median follow-up time was 11.6 years (0.01–20.8 years). Results: Within 6 years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased the LC prediction rate by 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity (p < 0.001 and p = 0.028), and reduced the number needed to predict one LC case (29 versus 40, p < 0.001 and 36 versus 40, p = 0.02), respectively. Applying the HUNT LCM 6-year 0.98% risk score as a cutoff (14.0% of ever-smokers) predicted 70.7% of all LC, increasing LC prediction rate with 89.2% and 18.9% versus the NELSON and 2021 USPSTF, respectively (both p < 0.001). Conclusions: The HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving the prediction of LC diagnosis, and may be used as a validated clinical tool for screening selection. ; This work was supported by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology. The funding sources had no role in the study conception, design, interpretation of the data, writing of the report, or decision to submit the article for publication. The authors thank all participants in the ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| ISSN: |
2666-3643 |
| Relation: |
https://linkinghub.elsevier.com/retrieve/pii/S2666364324000304; 2-s2.0-85189101614; JTO Clinical and Research Reports; 100660; http://hdl.handle.net/10754/697945 |
| DOI: |
10.1016/j.jtocrr.2024.100660 |
| Availability: |
http://hdl.handle.net/10754/697945; https://doi.org/10.1016/j.jtocrr.2024.100660 |
| Rights: |
Archived with thanks to JTO Clinical and Research Reports under a Creative Commons license, details at: http://creativecommons.org/licenses/by/4.0/ ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.D504694F |
| Database: |
BASE |