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机器学习和深度学习以改善宫癌患者的整体生存预测

Nan Jiang¹, Xing Xiong¹, Xue Chen^1,2

¹Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Translational cancer research

|June 18, 2025

概括

这项研究确定了宫癌 (CC) 存活率的关键因素,并开发了机器学习模型. DeepSurv模型在预测CC患者的整体存活率方面表现出卓越的表现.

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:

宫癌 (CC) 是一种流行的妇科恶性瘤,其预后受到众多因素的影响.
确定关键的预后指标对于改善患者的治疗结果至关重要.

研究的目的:

确定子宫癌患者整体生存期 (OS) 的独立预后因素.
开发和评估机器学习和深度学习模型,用于预测CC中的操作系统.

主要方法:

利用了来自监测,流行病学和最终结果 (SEER) 数据库 (2007-2016) 的数据.
雇佣最低绝对收缩和选择运算符 (LASSO) 和考克斯回归用于因子识别.
构建并比较了五个生存模型 (CoxBoost,RandomForest,SuperPC XGBoost,DeepSurv) 对于1年,3年和5年的操作系统预测.

主要成果:

一些参数包括年龄,等级,瘤大小和AJCC阶段与生存有显著的关联.
在DeepSurv模型中,达到了最高的一致性指数 (C指数) 0.869.
接收器运行特征 (ROC) 曲线显示出出色的预测性能,曲线下的面积 (AUC) 值为1年的操作系统高达0.936.

结论:

使用DeepSurv算法开发的预后模型显示了个性化治疗计划的巨大潜力.
确定独立的预后因素可以帮助更准确地评估宫癌患者的预后.

关键词:

宫癌 (CC) 是一种癌症.监测,流行病学和最终结果计划 (SEER计划)总体存活率 (OS) 是指总体存活率.预后预后预后

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