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基于监测,流行病学和最终结果 (SEER) 数据库分析的深度学习模型来预测肝细胞癌患者的生存率

Shoucheng Wang¹, Mingyi Shao², Yu Fu³

¹Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China.

Scientific reports

|June 9, 2024

概括

一个新的深度学习模型,神经多任务后勤回归 (NMTLR),准确地预测肝细胞癌 (HCC) 患者的生存率. 这种先进的AI工具为原发性肝癌提供了改进的预后和个性化的治疗建议.

科学领域:

在瘤学瘤学.
人工智能的人工智能
生物统计学生物统计学

背景情况:

肝细胞癌 (HCC) 是一种流行癌症,存活率较低,需要有效的长期随访策略.
准确预测患者生存率对于管理HCC和指导治疗决策至关重要.

研究的目的:

开发和验证一种深度学习模型,用于预测原发性HCC患者的存活率.
将深度学习模型的性能与传统的机器学习和HCC预测的统计模型进行比较.

主要方法:

利用了美国SEER数据库 (2011-2015) 中的2197名HCC患者的数据.
开发和训练深度学习模型 (DeepSurv,NMTLR) 和机器学习模型 (RSF,CoxPH).
使用一致性指数 (C指数),Brier分数,ROC曲线和校准图进行评估模型性能.

主要成果:

深度学习模型,特别是NMTLR (C指数为0.735),表现优于RSF和CoxPH模型.
在预测1,3年和5年生存率方面,NMTLR取得了很高的准确性 (AUC分别为0.824,0.813,0.803).
该NMTLR模型显示出卓越的校准和临床预后的区分能力.

结论:

该NMTLR深度学习模型提供了一个强大的工具,用于预测HCC患者的生存率.
该模型为临床预后和治疗建议提供了更高的准确性和可靠性.
对于临床实践,NMTLR模型的Web应用程序是可用的,有助于个性化患者护理.

关键词:

深度学习是一种深度学习.机器学习是机器学习.预测模型是一个预测模型.原发性肝癌是主要的肝癌.塞尔·塞尔 (Seer) 是一个看得见的人.

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