Lee Pin-Chieh, Lin Mong-Wei, Liao Hsien-Chi, Lin Chan-Yi, Liao Pei-Hung
Department of Nursing, National Taiwan University Cancer Center, Taipei, Taiwan.
Department of Surgery, Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Taiwan University, National Taiwan University Hospital, Taipei, Taiwan.
J Nurs Scholarsh. 2025 Jan;57(1):140-151. doi: 10.1111/jnu.12997. Epub 2024 Jun 4.
To utilize machine learning techniques to develop an association model linking lung cancer and environmental hormones to enhance the understanding of potential lung cancer risk factors and refine current nursing assessments for lung cancer.
This study is exploratory in nature. In Stage 1, data were sourced from a biological database, and machine learning methods, including logistic regression and neural-like networks, were employed to construct an association model. Results indicate significant associations between lung cancer and blood cadmium, urine cadmium, urine cadmium/creatinine, and di(2-ethylhexyl) phthalate. In Stage 2, 128 lung adenocarcinoma patients were recruited through convenience sampling, and the model was validated using a questionnaire assessing daily living habits and exposure to environmental hormones.
Analysis reveals correlations between the living habits of patients with lung adenocarcinoma and exposure to blood cadmium, urine cadmium, urine cadmium/creatinine, polyaromatic hydrocarbons, diethyl phthalate, and di(2-ethylhexyl) phthalate.
According to the World Health Organization's global statistics, lung cancer claims approximately 1.8 million lives annually, with more than 50% of patients having no history of smoking or non-traditional risk factors. Environmental hormones have garnered significant attention in recent years in pathogen exploration. However, current nursing assessments for lung cancer risk have not incorporated environmental hormone-related factors. This study proposes reconstructing existing lung cancer nursing assessments with a comprehensive evaluation of lung cancer risks.
The findings underscore the importance of future studies advocating for public screening of environmental hormone toxins to increase the sample size and validate the model externally. The developed association model lays the groundwork for advancing cancer risk nursing assessments.
利用机器学习技术开发一个将肺癌与环境激素联系起来的关联模型，以增进对潜在肺癌风险因素的理解，并完善当前肺癌护理评估。
本研究本质上是探索性的。在第一阶段，数据来源于一个生物数据库，并采用包括逻辑回归和类神经网络在内的机器学习方法构建关联模型。结果表明肺癌与血镉、尿镉、尿镉/肌酐以及邻苯二甲酸二（2-乙基己基）酯之间存在显著关联。在第二阶段，通过便利抽样招募了128例肺腺癌患者，并使用一份评估日常生活习惯和环境激素暴露情况的问卷对模型进行验证。
分析揭示了肺腺癌患者的生活习惯与血镉、尿镉、尿镉/肌酐、多环芳烃、邻苯二甲酸二乙酯和邻苯二甲酸二（2-乙基己基）酯暴露之间的相关性。
根据世界卫生组织的全球统计数据，肺癌每年导致约180万人死亡，超过50%的患者没有吸烟史或非传统风险因素。近年来，环境激素在病原体探索中受到了广泛关注。然而，目前肺癌风险护理评估尚未纳入与环境激素相关的因素。本研究建议通过对肺癌风险进行全面评估来重构现有的肺癌护理评估。
研究结果强调了未来研究倡导对环境激素毒素进行公众筛查以增加样本量并在外部验证模型的重要性。所开发的关联模型为推进癌症风险护理评估奠定了基础。
