Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management. In this work, we propose a survival analysis system that takes advantage of recently emerging deep learning techniques. The proposed system consists of three major components. 1) The first component is an end-to-end cellular feature learning module using a deep neural network with global average pooling. The learned cellular representations encode high-level biologically relevant information without requiring individual cell segmentation, which is aggregated into patient-level feature vectors by using a locality-constrained linear coding (LLC)-based bag of words (BoW) encoding algorithm. 2) The second component is a Cox proportional hazards model with an elastic net penalty for robust feature selection and survival analysis. 3) The third commponent is a biomarker interpretation module that can help localize the image regions that contribute to the survival model’s decision. Extensive experiments show that the proposed survival model has excellent predictive power for a public (i.e., The Cancer Genome Atlas) lung cancer dataset in terms of two commonly used metrics: log-rank test (p-value) of the Kaplan-Meier estimate and concordance index (c-index). In this work, we have proposed a segmentation-free survival analysis system that takes advantage of the recently emerging deep learning framework and well-studied survival analysis methods such as the Cox proportional hazards model. In addition, we provide an approach to visualize the discovered biomarkers, which can serve as concrete evidence supporting the survival model’s decision.

中文翻译：

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一个基于深度学习的框架，可通过生物标志物解释进行肺癌生存分析。

在美国，无论是男性还是女性，肺癌都是与癌症相关的死亡的主要原因，与其他许多癌症相比，肺癌的五年生存率要低得多。为了更好地进行疾病诊断和治疗管理，迫切需要准确的生存分析。在这项工作中，我们提出了一种生存分析系统，该系统利用了最近出现的深度学习技术。拟议的系统包括三个主要部分。1）第一个组件是使用具有全局平均池的深度神经网络的端到端蜂窝特征学习模块。学到的细胞表征可编码高级生物学相关信息，而无需进行单个细胞细分，通过使用基于位置约束的线性编码（LLC）的单词袋（BoW）编码算法，将其汇总到患者级别的特征向量中。2）第二个组件是具有弹性净罚分的Cox比例风险模型，用于健壮的特征选择和生存分析。3）第三个组件是生物标志物解释模块，可以帮助定位有助于生存模型决策的图像区域。大量实验表明，根据两个常用指标，所提出的生存模型对于公共（即癌症基因组图谱）肺癌数据集具有出色的预测能力：Kaplan-Meier估计的对数秩检验（p值）和一致性指标（c-index）。在这项工作中 我们提出了一种无分割的生存分析系统，该系统利用了最近出现的深度学习框架和经过充分研究的生存分析方法，例如Cox比例风险模型。此外，我们提供了一种可视化发现的生物标志物的方法，可以作为支持生存模型决策的具体证据。