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Deep learning-based age estimation from chest CT scans

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Medical imaging can be used to estimate a patient’s biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient’s age based on their chest CT scan. Additionally, we investigated whether chest CT estimated age is a more accurate predictor of lung cancer risk compared to chronological age.

Methods

To develop our age prediction model, we utilized composite CT images and Inception-ResNet-v2. The model was trained, validated, and tested on 13,824 chest CT scans from the National Lung Screening Trial, with 91% for training, 5% for validation, and 4% for testing. Additionally, we independently tested the model on 1849 CT scans collected locally. To assess chest CT estimated age as a risk factor for lung cancer, we computed the relative lung cancer risk between two groups. Group 1 consisted of individuals assigned a CT age older than their chronological age, while Group 2 comprised those assigned a CT age younger than their chronological age.

Results

Our analysis revealed a mean absolute error of 1.84 years and a Pearson’s correlation coefficient of 0.97 for our local data when comparing chronological age with the estimated CT age. The model showed the most activation in the area associated with the lungs during age estimation. The relative risk for lung cancer was 1.82 (95% confidence interval, 1.65–2.02) for individuals assigned a CT age older than their chronological age compared to those assigned a CT age younger than their chronological age.

Conclusion

Findings suggest that chest CT age captures some aspects of biological aging and may be a more accurate predictor of lung cancer risk than chronological age. Future studies with larger and more diverse patients are required for the generalization of the interpretations.

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Acknowledgements

We gratefully acknowledge funding support for this research from the Saskatchewan Health Research Foundation.

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Authors and Affiliations

  1. Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada

    Ghazal Azarfar, Scott J. Adams & Paul S. Babyn

  2. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada

    Ghazal Azarfar & Seok-Bum Ko

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Correspondence to Ghazal Azarfar.

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The authors (G.A., S.K., S.J.A, and P.S.B) declare no conflict of interest.

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This study was approved by the University of Saskatchewan Research Ethics Board. A waiver of individual patient consent was granted.

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Azarfar, G., Ko, SB., Adams, S.J. et al. Deep learning-based age estimation from chest CT scans. Int J CARS 19, 119–127 (2024). https://doi.org/10.1007/s11548-023-02989-w

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