使用机器学习方法预测形甲状腺癌的整体存活率
Arnavaz Hajizadeh Barfejani1, Mohammadreza Rostami2, Mohammad Rahimi3
1Royal College of Surgeons in Ireland, Dublin, Ireland.
概括
机器学习模型准确地预测了形甲状腺癌 (ATC) 患者的短期生存率. 这些预测工具可以帮助临床决策和针对这种侵袭性癌症的个性化治疗计划.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 无塑性甲状腺癌 (ATC) 是一种具有不良预后的侵袭性癌症.
- 机器学习 (ML) 提供了改善ATC生存预测的潜力.
研究的目的:
- 开发和验证ML模型,以预测ATC患者的3个月,6个月和12个月整体存活期 (OS).
- 使用SEER数据库进行模型开发和验证.
主要方法:
- 采用了五种ML算法:AdaBoost,支持向量机 (SVC),梯度提升,随机森林和天真贝叶斯.
- 来自SEER数据库 (2004-2015) 的数据分为培训 (70%) 和测试 (30%) 集.
- 模型性能使用一致性指数 (C指数) 和Brier分数进行评估,对调整进行了五倍交叉验证.
主要成果:
- 梯度增强模型在3个月生存预测方面表现出色 (C指数:0.8197).
- AdaBoost模型在6个月生存期表现出优异的表现 (C指数:0.8473).
- SVC模型显示了12个月生存期的最佳结果 (C指数:0.8347).
- 6个月后的关键预测因素包括手术,IVC阶段,辐射,化疗和瘤大小,治疗改善,较高阶段减少生存率.
结论:
- ML算法可以准确预测形甲状腺癌的短期存活率.
- 这些模型有可能指导临床决策和为ATC患者量身定制治疗策略.
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