A novel prediction model for immunotherapy induced pneumonitis prediction based on Chest CT and electronic health record
Year: 2024
Type: preprint
Study design: Observational Study
Abstract: Immune checkpoint inhibitors (ICIs) have been extensively used for the treatment of non-small cell lung cancer patients in recent years, providing a significant survival benefit. However, a major drawback of ICIs-related immunotherapy is the risk of developing post-surgical pneumonitis. In this study, we propose a deep learning-embedded, multi-modality prediction approach to assess whether patients will develop ICI-pneumonitis after receiving ICIs-based immunotherapy. This approach utilizes multi-modal data, including clinical data and pre-treatment lung screening computed tomography (CT) images. We extracted three types of features: 1) deep learning features from CT scans using a pretrained vision transformer, 2) radiomic features from CT scans using predefined radiomic algorithms, and 3) clinical features from patients’ clinical records. We then compared multiple machine learning algorithms for prediction based on these extracted features. Our results demonstrated a prediction accuracy of 0.823 and an area under the receiver operating characteristic curve of 0.895.
Source: medRxiv
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4403418951