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基于深度学习的低肢黑色素瘤患者的生存预测

Jinrong Zhang¹, Hai Yu¹, Xinkai Zheng¹

¹Department of Dermatology, The First Affiliated Hospital of Jinan University and Jinan University Institute of Dermatology, Guangzhou, 510630, China.

Discover oncology

|November 29, 2023

概括

与传统方法相比,深度学习模型DeepSurv显著改善了下肢黑色素瘤生存预测. 这一进展为患有这种罕见癌症的患者提供了更好的预后估计.

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:

下肢黑色素瘤 (LLM) 的生存率需要改进的预后工具.
监测,流行病学和最终结果 (SEER) 数据库为癌症研究提供了有价值的数据.

研究的目的:

评估DeepSurv模型在预测下肢黑色素瘤患者的长期存活率方面的有效性.
将DeepSurv的预测性能与Cox的比例危险 (CoxPH) 模型进行比较.

主要方法:

利用2000-2019年26243名下肢黑色素瘤患者的SEER数据.
开发并验证了DeepSurv模型与CoxPH模型相比,使用培训 (70%) 和测试 (30%) 队列.
使用对应指数 (C指数) 和受收机操作特征曲线 (AUC) 下的时间依赖区域来评估模型准确性.

主要成果:

DeepSurv的C指数比CoxPH (0.766) 高 (0.852),这表明其预测准确度更高.
与CoxPH (0.795,0.767,0.847) 相比,DeepSurv在3,5和8年内显示出更好的AUC值 (0.872,0.858,0.847) 与CoxPH (0.795,0.767,0.847) 相比.
用CoxPH模型确定了患者因素 (年龄,阶段,治疗) 和生存率之间的显著相关性.

结论:

在预测下肢黑色素瘤患者存活率方面,DeepSurv模型明显优于CoxPH模型.
DeepSurv提供了一个更准确,更有效的工具,用于估计下肢黑色素瘤的预后.
这种深度学习方法提高了LLM的生存预测准确度.

关键词:

在DeepSurv上进行深度调查.下肢黑色素瘤是什么意思神经网络的神经网络的神经网络塞尔 (Seer) 是一个明星.预测生存的预测.

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