论文标题
使用CT图像和不完整的临床数据的特发性肺纤维化的生存分析
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
论文作者
论文摘要
特发性肺纤维化（IPF）是一种无情的进行性纤维化肺部疾病，其进展速度可变且不可预测。肺部的CT扫描为IPF患者的临床评估提供了信息，并包含与疾病进展有关的相关信息。在这项工作中，我们提出了一种多模式方法，该方法使用神经网络和记忆库使用临床和成像数据来预测IPF患者的存活。大多数临床IPF患者记录缺少数据（例如缺少肺功能测试）。为此，我们提出了一个概率模型，该模型捕获了观察到的临床变量与缺失的临床变量之间的依赖性。这种缺少数据归因的原则方法可以与深层生存分析模型自然结合在一起。我们表明，就一致性指数和综合的布里尔评分而言，所提出的框架比基线的生存分析结果明显更好。我们的工作还提供了有关与死亡率相关的新型基于图像的生物标志物的见解。
Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of progression. CT scans of the lungs inform clinical assessment of IPF patients and contain pertinent information related to disease progression. In this work, we propose a multi-modal method that uses neural networks and memory banks to predict the survival of IPF patients using clinical and imaging data. The majority of clinical IPF patient records have missing data (e.g. missing lung function tests). To this end, we propose a probabilistic model that captures the dependencies between the observed clinical variables and imputes missing ones. This principled approach to missing data imputation can be naturally combined with a deep survival analysis model. We show that the proposed framework yields significantly better survival analysis results than baselines in terms of concordance index and integrated Brier score. Our work also provides insights into novel image-based biomarkers that are linked to mortality.
