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使用癌胚抗原测量对结直肠癌复发的预后模型

Nastaran Mohammadian Rad¹, Odin Sosef², Jord Seegers²

¹The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology & Reproduction, Maastricht University, Maastricht, Netherlands.

Frontiers in oncology

|June 14, 2024

概括

机器学习模型可以使用最小数量的癌胚抗原 (CEA) 测试来预测结直肠癌复发. 这有助于癌症患者的早期干预和个性化治疗.

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:

大肠直肠癌 (CRC) 是全球领先的癌症.
在接受手术治疗的患者中,很大一部分患者经历了复发.
早期风险识别对于及时干预和治疗规划至关重要.

研究的目的:

为CRC复发开发预测机器学习模型.
为了确定癌胚抗原 (CEA) 测量的最小数量,以进行有效的预测.
确定CRC复发的关键预后因素.

主要方法:

使用了1927名I-III期CRC患者 (2008-2016) 的数据集.
训练有素的机器学习模型包含人口统计,临床病理因素和连续CEA测量.
评估模型的预测性能,并确定了关键的预后因素.

主要成果:

梯度增强分类器实现了AUC为0.81和平衡精度为0.73.
预测复发是可行的,AUC为0.71,仅使用五次手术后CEA测量.
确定了影响复发的关键预后因素.

结论:

机器学习有效地预测CRC复发,即使使用有限的CEA数据.
这种方法优化了临床工作流程,并使个性化治疗计划成为可能.
未来的工作包括在更大的队列中进行验证,以制定基于风险的后续策略.

关键词:

癌症的复发癌症的复发癌胚抗原是一种癌胚抗原.结肠直肠癌是什么意思机器学习是机器学习.预后预后预后

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