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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基于深度学习的肺癌生存分析框架和生物标志物解释。


肺癌是美国男性和女性癌症相关死亡的主要原因，其五年生存率比许多其他癌症低得多。为了更好的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，我们提出了一种利用最近新兴的深度学习技术的生存分析系统。所提出的系统由三个主要部分组成。 1）第一个组件是使用具有全局平均池化的深度神经网络的端到端细胞特征学习模块。学习到的细胞表示对高级生物相关信息进行编码，无需单个细胞分割，通过使用基于局部约束线性编码 (LLC) 的词袋 (BoW) 编码算法将其聚合为患者级特征向量。 2) 第二个组成部分是具有弹性净惩罚的 Cox 比例风险模型，用于稳健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，所提出的生存模型在两个常用指标方面对公共（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）和一致性指数（c 指数）。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，这可以作为支持生存模型决策的具体证据。