Family history of cancer and lung cancer: Utility of big data and artificial intelligence for exploring the role of genetic risk

IF 4.5 2区医学 Q1 ONCOLOGY

Lung Cancer Pub Date : 2024-08-09 DOI:10.1016/j.lungcan.2024.107920

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

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引用次数: 0

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.

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癌症和肺癌家族史：大数据和人工智能在探索遗传风险作用方面的实用性。

目的：肺癌（LC）是一种多因素疾病，其遗传易感性的作用已变得越来越重要。我们的目的是利用人工智能（AI）分析基于癌症家族史（FHC）的肺癌患者之间的差异：从 2016 年 8 月到 2020 年 6 月，我们从胸部肿瘤登记处（TTR）获得了临床信息，该登记处是由西班牙肺癌组织发起的全国性数据库。除描述性统计分析外，还进行了人工智能辅助分析。德国技术信息图书馆为合并来自电子病历和 TTR 数据库的数据提供了支持。人工智能辅助分析的结果使用知识图谱、统一模式以及描述性和预测性分析进行报告：分析分两个阶段进行：第一阶段是常规统计分析，包括 11684 名患者，其中 5806 人患有 FHC。在全球人群中，FHC 患者的中位总生存期（OS）为 23 个月（CI 95 %：21.39-24.61），而无 FHC（NFHC）患者的中位总生存期（OS）为 21 个月（CI 95 %：19.53-22.48）：在我们的人群中，女性和年轻患者中患有 FHC 的 LC 发病率较高。FHC 是 LC 发生的危险因素和预测因子，尤其是在 50 岁以下的人群中。这些结果得到了常规统计和人工智能辅助分析的证实。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Lung Cancer 医学-呼吸系统

CiteScore

9.40

自引率

3.80%

发文量

407

审稿时长

25 days

期刊介绍： Lung Cancer is an international publication covering the clinical, translational and basic science of malignancies of the lung and chest region.Original research articles, early reports, review articles, editorials and correspondence covering the prevention, epidemiology and etiology, basic biology, pathology, clinical assessment, surgery, chemotherapy, radiotherapy, combined treatment modalities, other treatment modalities and outcomes of lung cancer are welcome.