Penalized Deep Partially Linear Cox Models with Application to CT Scans
of Lung Cancer Patients
- URL: http://arxiv.org/abs/2303.05341v3
- Date: Fri, 29 Sep 2023 18:35:27 GMT
- Title: Penalized Deep Partially Linear Cox Models with Application to CT Scans
of Lung Cancer Patients
- Authors: Yuming Sun, Jian Kang, Chinmay Haridas, Nicholas R. Mayne, Alexandra
L. Potter, Chi-Fu Jeffrey Yang, David C. Christiani, Yi Li
- Abstract summary: Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies.
The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients.
We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
- Score: 42.09584755334577
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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) employed
computed tomography texture analysis, which provides objective measurements of
texture patterns on CT scans, to quantify the mortality risks of lung cancer
patients. Partially linear Cox models have gained popularity for survival
analysis by dissecting the hazard function into parametric and nonparametric
components, allowing for the effective incorporation of both well-established
risk factors (such as age and clinical variables) and emerging risk factors
(e.g., image features) within a unified framework. However, when the dimension
of parametric components exceeds the sample size, the task of model fitting
becomes formidable, while nonparametric modeling grapples with the curse of
dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model
(Penalized DPLC), which incorporates the SCAD penalty to select important
texture features and employs a deep neural network to estimate the
nonparametric component of the model. 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.
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