Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) was a nationwide study aimed at investigating risk factors for lung cancer. The study employed computed tomography texture analysis (CTTA), which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models are becoming a popular tool for modeling survival outcomes, as they effectively handle both established risk factors (such as age and other clinical factors) and new risk factors (such as image features) in a single framework. The challenge in identifying the texture features that impact cancer survival is due to their sensitivity to factors such as scanner type, segmentation, and organ motion. To overcome this challenge, we propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select significant texture features and employs a deep neural network to estimate the nonparametric component of the model accurately. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

翻译：肺癌是全球癌症死亡的主要原因，凸显了理解其死亡风险以设计有效患者中心疗法的重要性。国家肺筛查试验（NLST）是一项旨在调查肺癌风险因素的全国性研究。该研究采用计算机断层扫描纹理分析（CTTA），通过客观测量CT扫描中的纹理模式，量化肺癌患者的死亡风险。部分线性Cox模型因其能够在统一框架下有效处理既有风险因素（如年龄及其他临床因素）与新型风险因素（如图像特征），已成为生存结局建模的常用工具。识别影响癌症生存的纹理特征面临的挑战在于其对扫描仪类型、分割及器官运动等因素的敏感性。为克服这一挑战，我们提出了一种新型惩罚性深度部分线性Cox模型（Penalized DPLC），该模型引入SCAD惩罚以选择显著纹理特征，并采用深度神经网络准确估计模型的非参数分量。我们证明了估计量的收敛性与渐近性质，并通过大量模拟研究将其与其他方法进行对比，评估其在风险预测和特征选择中的性能。所提方法被应用于NLST研究数据集，以揭示关键临床和影像风险因素对患者生存的影响。我们的发现为这些因素与生存结局之间的关系提供了宝贵见解。