肺癌的时间趋势和患者分层:罗马尼亚蒂米斯县的综合集群分析
Versavia Maria Ancusa1, Ana Adriana Trusculescu2,3, Amalia Constantinescu2,4,5
1Department of Computer and Information Technology, Automation and Computers Faculty, "Politehnica" University of Timisoara, Vasile Pârvan Blvd, no. 2, 300223 Timisoara, Romania.
Cancers
|July 29, 2025
概括
罗马尼亚的肺癌病例显著增加,特别是在流行病后. 机器学习识别了五个不同的患者群体,帮助有针对性的查和个性化的肺癌治疗策略.
科学领域:
- 在瘤学瘤学.
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 肺癌是癌症死亡的主要原因,区域差异很大.
- 在罗马尼亚,肺癌病例的增加引起了这项调查.
- 了解患者的特征和促成因素对于公共卫生至关重要.
研究的目的:
- 核实和量化罗马尼亚肺癌入院病例的增加.
- 使用无监督机器学习识别不同的肺癌患者表型.
- 描述影响肺癌发病率和患者概况的因素.
主要方法:
- 对4206名肺癌患者 (2013-2024) 的回顾性分析.
- 无监督的k-means对761种临床特征进行聚类,以确定患者的表型.
- 分析时间趋势,地理分布,吸烟模式,并发症和人口统计数据.
主要成果:
- 确认肺癌病例在流行病后 (2022-2024) 与流行病前 (2013-2020) 相比增加了80.5%.
- 确定了五个不同的患者群,在年龄,吸烟,地理和分子形状方面存在显著差异.
- 报告指出,COPD患病率高 (44.8-78.9%);COVID-19病史低 (3.4-8.3%),表明疫情影响有限.
结论:
- 罗马尼亚首次基于机器学习的肺癌患者分层,证实了流行病学上的增加.
- 确定了五种具有临床意义的表型,对区域医疗保健规划和资源配置有价值.
- 这些发现支持针对高风险人群的有针对性的查,个性化医疗和精确瘤学策略.
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