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CT-based radiomics can identify physiological modifications of bone structure related to subjects’ age and sex

  • Computed Tomography
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects’ sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners.

Materials and methods

We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects’ sex and age respectively, and we computed a voting model which combined predictions.

Results

The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects’ sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects’ age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years).

Conclusion

Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects’ sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.

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Abbreviations

AUROC:

Area Under the Receiver Operating Characteristic curve

BMD:

Bone Mineral Density

CT:

Computed Tomography

DXA:

Dual-Energy X-ray Absorptiometry

FRAX:

Fracture Risk Assessment Tool

ICC:

Intraclass Correlation Coefficient

MAD:

Median Absolute Deviation

VF:

Vertebral Fracture

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Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Author notes

  1. Riccardo Barbieri, Marco Grimaldi and Letterio S. Politi have equally contributed to the manuscript and share senior authorship.

Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy

    Riccardo Levi, Federico Garoli, Massimiliano Battaglia, Dario A. A. Rizzo, Marco Riva, Gherardo Mazziotti & Letterio S. Politi

  2. Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy

    Riccardo Levi, Federico Garoli, Massimiliano Battaglia, Dario A. A. Rizzo, Giovanni Savini, Marco Grimaldi & Letterio S. Politi

  3. Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milan, Italy

    Maximilliano Mollura & Riccardo Barbieri

  4. Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy

    Marco Riva, Massimo Tomei, Alessandro Ortolina & Maurizio Fornari

  5. Department of Neuroradiology, Massachusetts General Hospital, Boston, MA, 02114, USA

    Saurabh Rohatgi

  6. Artificial Intelligence Center, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy

    Giovanni Angelotti & Victor Savevski

  7. Metabolic Bone Diseases and Osteoporosis Section, Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, 20090, Rozzano, Italy

    Gherardo Mazziotti

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  1. Riccardo Levi
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by RL, FG, MB, DR, MM, and GA. The first draft of the manuscript was written by RL, LSP and MG, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Letterio S. Politi.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of IRCCS-Humanitas Research Hospital (March 22, 2022 / 2410).

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The collection of written informed consent was waived by Ethics Committee due to the retrospective nature of the study.

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Levi, R., Garoli, F., Battaglia, M. et al. CT-based radiomics can identify physiological modifications of bone structure related to subjects’ age and sex. Radiol med 128, 744–754 (2023). https://doi.org/10.1007/s11547-023-01641-6

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