使用全血细胞计数进行人工智能驱动的结直肠癌预先查:对更广泛的人口产生影响
Bruna Los1, Bruno Aragão Rocha2, Daniel Noce da Silva1
1, Huna, São Paulo, Brazil.
International journal of colorectal disease
|November 18, 2025
概括
使用常规全血计 (CBC) 数据的新AI模型可以帮助检测结直肠癌 (CRC). 这种具有成本效益的工具有助于风险分层,可能改善早期检测和查的可访问性.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 早期发现结直肠癌 (CRC) 显著改善治疗结果.
- 传统的查方法,如结肠镜检查的可用性有限,而便免疫化学测试 (FIT) 面临的坚持挑战.
研究的目的:
- 开发一个透明的人工智能 (AI) 模型,使用常规的全血细胞计数 (CBC) 数据,以经济高效地检测CRC.
- 探索可解释AI在增强现有的CRC查计划中的潜力.
主要方法:
- 对28450名接受了结肠镜检查和CBC检测的个体 (45-75岁) 进行了回顾性分析.
- 使用CBC标志物,CBC衍生比率和年龄开发了一个回归模型,对70%的数据进行训练,并对30%进行测试.
主要成果:
- 人工智能模型利用红细胞分布宽度 (RDW),全身炎症反应指数 (SIRI),血红蛋白和年龄,实现了0.77的CRC检测AUC.
- 在CRC病例和对照组之间观察到CBC标志物和年龄的显著差异 (P < 0.001).
- 该模型表现出与深度学习模型相似的性能,并确定了年龄较大,RDW/SIRI升高和低血红蛋白作为CRC指标.
结论:
- 通过可解释的AI模型分析的常规CBC数据,为CRC风险分层提供了一个可扩展的预选工具.
- 这种方法有可能优化资源分配,并提高CRC查计划的可访问性.
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