A0553
Title: Penalized deep partially linear cox models with application to CT scans of lung cancer patients
Authors: Yuming Sun - College of William and Mary (United States) [presenting]
Jian Kang - University of Michigan (United States)
Chinmay Haridas - Massachusetts General Hospital (United States)
Nicholas Mayne - Duke University (United States)
Alexandra Potter - Massachusetts General Hospital (United States)
Chi-Fu Jeffrey Yang - Massachusetts General Hospital (United States)
David Christiani - Harvard TH Chan School of Public Health (United States)
Yi Li - University of Michigan (United States)
Abstract: Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centred 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) to quantify the mortality risks of lung cancer patients. 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, a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC) is proposed, 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. The convergence and asymptotic properties of the estimator are proved, and it is compared 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.
