预测结直肠癌的死亡率和复发:对预测模型的比较评估
Shayeste Alinia1, Mohammad Asghari-Jafarabadi2, Leila Mahmoudi1
1Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Heliyon
|March 22, 2024
概括
这项研究评估了结直肠癌 (CRC) 死亡率和复发的预测模型. 增强模型在预测死亡率方面表现出色,而渐变增强在CRC患者的复发预测方面表现出色.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 结肠直肠癌 (CRC) 是全球癌症死亡的主要原因.
- 准确预测CRC死亡率和复发对于患者管理至关重要.
研究的目的:
- 评估各种机器学习模型对结直肠癌死亡率的预测准确度.
- 评估这些模型在预测CRC患者疾病复发方面的表现.
主要方法:
- 在2001年至2017年期间诊断的284名CRC患者的回顾性分析.
- 评估决策树,随机森林,随机生存森林 (RSF),梯度提升, mboost,深度学习神经网络 (DLNN) 和Cox回归模型.
- 性能指标包括灵敏度,特异性,正预测值 (PPV),ROC区域和整体准确性.
主要成果:
- 对于死亡率预测, mboost 实现了最高准确度 (89%),灵敏度为 96.9%,ROC 面积为 0.88. 随机森林显示了100%的灵敏度,但0%的特异性.
- 渐变增强在复发预测方面表现出卓越的性能,具有100%的灵敏度,92.9%的特异性和96.4%的ROC面积.
- 在所有评估的指标中,DLNN模型在死亡率和复发预测方面表现不佳.
结论:
- 博斯特模型在预测结直肠癌患者的死亡率方面非常有效.
- 渐变增强显示出在CRC中预测复发的绝佳潜力.
- 这些发现凸显了特定机器学习模型在改善结直肠癌预后准确性的实用性.
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