Title:Predicting Immune Checkpoint Inhibitor-Related Pneumonitis via Computed Tomography and Whole-Lung Analysis Deep Learning
Volume: 20
Author(s): Ning Wang, Zhifang Zhao, Zhimei Duan and Fei Xie*
Affiliation:
- Department of Respiratory and Critical Care Medicine, Air Force Medical Center, Beijing, China
Keywords:
Computed tomography, Deep learning, Artificial intelligence, Immune checkpoint inhibitor-related pneumonitis, Immunotherapy, Whole-lung analysis.
Abstract:
Background:
Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a fatal adverse event of immunotherapy. However, there is a lack of methods to
identify patients who have a high risk of developing ICI-P in immunotherapy.
Purpose:
We aim at predicting the individualized risk of developing ICI-P by computed tomography (CT) images and deep learning to assist in personalized
immunotherapy planning.
Methods:
We first explored the prognostic value of the commonly used clinical factors. Moreover, we proposed a novel whole-lung analysis deep learning
(DL) model, which is constructed using a combination of Densely Connected Convolutional Networks (DenseNet) and Feature Pyramid Networks
(FPN). This DL model mines global lung information from CT images for predicting the risk of developing ICI-P, and it is fully automated and
does not require manually annotating images. Finally, 157 patients were collected and randomly divided into training and testing sets for
performance evaluation.
Results:
In the testing set, the clinical model achieved an Area Under the Curve (AUC) of 0.710 and accuracy of 0.625. By mining global lung information,
the DL model achieved AUC=0.780 and accuracy=0.729 in the testing set, where the DL score revealed a significant difference between ICI-P and
non-ICI-P patients. Through deep learning visualization technique, we found that many areas outside of tumor (e.g., pleural retraction, pleural
effusion, and the abnormalities in vessels) are important for predicting the risk of developing ICI-P in immunotherapy.
Conclusions:
The whole-lung analysis DL model provides an easy-to-use method for identifying patients at high risk of developing ICI-P by CT images, which
is important for individualized treatment planning in immunotherapy. The performance improvement over the clinical model indicates that mining
whole-lung information in CT images is effective for prognostic prediction in immunotherapy.
