预测跨多种癌症类型的癌症诊断时间到时间
Kien Lau1, Gregory R Hart2, Jun Deng3
1Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT, 06510, USA.
Scientific reports
|July 9, 2025
概括
预测癌症诊断时间是复杂的. 新的模型准确地估计了肺癌,肝癌和膀癌的首次癌症诊断的时间,有助于早期检测和个性化风险评估.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 癌症是全球死亡的主要原因,早期检测显著提高了生存率.
- 预测第一个癌症诊断的时间是具有挑战性的,因为这种疾病的多因素性质.
- 准确的风险评估工具对于提高早期癌症检测策略至关重要.
研究的目的:
- 开发和评估预测模型来估计高发病率癌症首次癌症诊断的时间.
- 为了比较不同机器学习模型的性能,包括Cox比例危险模型,生存决策树和随机生存森林.
- 确定癌症诊断时间的关键人口,临床和行为预测因素.
主要方法:
- 利用了Cox比例危险模型与弹性网规范化,生存决策树和随机生存森林.
- 在前列腺,肺,结肠直肠和卵巢癌查试验 (PLCO) 的数据上训练模型,并在英国生物银行进行评估.
- 采用了46个性别不可知特征,包括人口,临床和行为变量.
主要成果:
- 考克斯模型实现了肺癌的C指数为0.813,超过了非参数式机器学习方法.
- 癌症特异型模型表现出高于非特异性癌症模型的性能,由时间依赖的AUC分析证实.
- 分析揭示了新的见解,例如身体质量指数 (BMI) 与肺癌风险之间的反向关联.
结论:
- 开发了可解释和准确的计算工具,用于个性化癌症风险评估.
- 证明了机器学习模型在预测癌症诊断的时间方面的有效性.
- 突出了改善早期癌症检测和将先进的计算方法集成到临床实践中的潜力.
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