Paper Info
Title
A Quarter-split Domain-adaptive Network for EGFR Gene Mutation Prediction in Lung Cancer by Standardizing Heterogeneous CT image
Abstract
Epidermal growth factor receptor (EGFR) gene mutation status is crucial for the treatment planning of lung cancer. The gold standard for detecting EGFR mutation status relies on invasive tumor biopsy and expensive gene sequencing. Recently, computed tomography (CT) images and deep learning have shown promising results in non-invasively predicting EGFR mutation in lung cancer. However, CT scanning parameters such as slice thickness vary largely between different scanners and centers, making the deep learning models very sensitive to noise and therefore not robust in clinical practice. In this study, we propose a novel QuarterNet(adaptive) model to predict EGFR mutation in lung cancer, which is robust to CT images of different thicknesses. We propose two components: 1) a quarter-split network to sequentially learn local lung features from different lung lobes and global lung features; 2) a domain adaptive strategy to learn CT thickness-invariant features. Furthermore, we collected a large dataset including 1413 patients with both EGFR gene sequencing and CT images of various thicknesses to evaluate the performance of the proposed model. Finally, the QuarterNet(adaptive) model achieved AUC over 0.88 regarding CT images of different thicknesses, which improves largely than state-of-the-art methods.
Year
DOI
Venue
2021
10.1109/EMBC46164.2021.9630395
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
DocType
Volume
ISSN
Conference
2021
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Liusu Wang100.34
Shuo Wang200.34
He Yu300.34
Yongbei Zhu400.34
Weimin Li500.34
Jie Tian61475159.24