Family history of cancer and lung cancer

Utility of big data and artificial intelligence for exploring the role of genetic risk

verfasst von

Virginia Calvo, Emetis Niazmand, Enric Carcereny, Delvys Rodriguez-Abreu, Manuel Cobo, Rafael López-Castro, María Guirado, Carlos Camps, Ana Laura Ortega, Reyes Bernabé, Bartomeu Massutí, Rosario Garcia-Campelo, Edel del Barco, José Luis González-Larriba, Joaquim Bosch-Barrera, Marta Martínez, María Torrente, María Esther Vidal, Mariano Provencio

Abstract

Objectives: Lung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) to analyze differences between patients with LC based on family history of cancer (FHC). Materials and methods: From August 2016 to June 2020 clinical information was obtained from Thoracic Tumors Registry (TTR), a nationwide database sponsored by the Spanish Lung Cancer Group. In addition to descriptive statistical analysis, an AI-assisted analysis was performed. The German Technical Information Library supported the merging of data from the electronic medical records and database of the TTR. The results of the AI-assisted analysis were reported using Knowledge Graph, Unified Schema and descriptive and predictive analyses. Results: Analyses were performed in two phases: first, conventional statistical analysis including 11,684 patients of those 5,806 had FHC. Median overall survival (OS) for the global population was 23 months (CI 95 %: 21.39–24.61) in patients with FHC versus 21 months (CI 95 %: 19.53–22.48) in patients without FHC (NFHC), p < 0.001. The second AI-assisted analysis included 5,788 patients of those 939 had FHC. 58.48 % of women with FHC had LC. 9.53 % of patients had an EGFR or HER2 mutation or ALK translocation and at least one relative with cancer. A family history of LC was associated with an increased risk of smoking-related LC. Non-smokers with a family history of LC were more likely to have an EGFR mutation in NSCLC. In Bayesian network analysis, 55 % of patients with a family history of LC and never-smokers had an EGFR mutation. Conclusion: In our population, the incidence of LC in patients with a FHC is higher in women and younger patients. FHC is a risk factor and predictor of LC development, especially in people ≤ 50 years. These results were confirmed by conventional statistics and AI-assisted analysis.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Universidad Autónoma de Madrid (UAM)
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Institute Catala Oncologia
Insular University Hospital of Gran Canaria
International Business Information Management Association (IBIMA)
Hospital Universitario de Valladolid
Hospital General Universitario de Elche
Universitat de Valencia
Hospital Universitario de Jaén
Hospital Universitario Virgen del Rocio
Hospital General Universitario de Alicante
University of A Coruna
Hospital Universitario de Salamanca
Complutense Universität Madrid (UCM)
University of Girona

Typ

Artikel

Journal

LUNG CANCER

Band

195

ISSN

0169-5002

Publikationsdatum

09.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Onkologie, Lungen- und Bronchialmedizin, Krebsforschung

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1016/j.lungcan.2024.107920 (Zugang: Offen)