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Published in: European Radiology 7/2020

Open Access 01-07-2020 | Computed Tomography | Imaging Informatics and Artificial Intelligence

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening

Authors: Yaling Pan, Dejun Shi, Hanqi Wang, Tongtong Chen, Deqi Cui, Xiaoguang Cheng, Yong Lu

Published in: European Radiology | Issue 7/2020

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Abstract

Objective

Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using low-dose chest computed tomography (LDCT) scans obtained for lung cancer screening.

Methods

First, a deep learning model was trained and tested with 200 annotated LDCT scans to segment and label all vertebral bodies (VBs). Then, the mean CT numbers of the trabecular area of target VBs were obtained based on the segmentation mask through geometric operations. Finally, a linear function was built to map the trabecular CT numbers of target VBs to their BMDs collected from approved software used for osteoporosis diagnosis. The diagnostic performance of the developed system was evaluated using an independent dataset of 374 LDCT scans with standard BMDs and osteoporosis diagnosis.

Results

Our deep learning model achieved a mean Dice coefficient of 86.6% for VB segmentation and 97.5% accuracy for VB labeling. Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964–0.968 and mean errors of 2.2–4.0 mg/cm3. The area under the curve (AUC) was 0.927 for detecting osteoporosis and 0.942 for distinguishing low BMD.

Conclusion

The proposed deep learning-based system demonstrated the potential to automatically perform opportunistic osteoporosis screening using LDCT scans obtained for lung cancer screening.

Key Points

• Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fracture.
• A deep learning-based system was developed to fully automate bone mineral density measurement in low-dose chest computed tomography scans.
• The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.
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Metadata
Title
Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening
Authors
Yaling Pan
Dejun Shi
Hanqi Wang
Tongtong Chen
Deqi Cui
Xiaoguang Cheng
Yong Lu
Publication date
01-07-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 7/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06679-y

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