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基于DNN的半参数AFT模型,用于将基因组和病理成像数据整合到癌症预后中

Jingmao Li¹, Qingzhao Zhang^2,3, Shuangge Ma¹

¹Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, United States.

|March 16, 2026

概括

这项研究引入了一种用于癌症预后的新型半参数模型,使用深度学习集成基因组和成像数据. 该方法提高了预测准确度,并确定了肺癌的关键预后因素.

科学领域:

生物统计学生物统计学
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

预后建模在癌症研究和临床实践中至关重要.
基因组和病理成像数据为癌症预后提供了互补的见解.
整合不同的数据类型可以提高癌症结果预测的准确性.

研究的目的:

为癌症预后开发一个先进的半参数加速失效时间模型.
有效地整合高维基因组和病理成像数据.
提高癌症预后模型的解释性和灵活性.

主要方法:

开发了一种半参数加速失效时间模型.
用于可解释的基因组数据的参数效应和用于成像数据的非参数效应 (深度神经网络).
应用惩罚技术,包括对深度神经网络权重的群体惩罚,用于变量选择.

主要成果:

建立了理论性质,包括非对称选择和估计一致性和正常性.
通过模拟证明了竞争性表现.
在癌症基因组图谱 (The Cancer Genome Atlas) 肺癌数据分析中取得了令人满意和合理的发现,表现优于替代品.

结论:

拟议的半参数模型有效地整合了基因组和病理成像数据,以改善癌症预后.
深度神经网络增强复杂的成像特征的建模.
该方法为癌症结果预测提供了一个强大的框架,具有强大的理论依据.

关键词:

癌症预后癌症预后数据整合数据集成.深度神经网络是一个神经网络.受到惩罚的估计和选择.

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