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Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures

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Abstract

Purpose

There is emerging evidence that radiomics analyses can improve detection of skeletal fragility. In this cross-sectional study, we evaluated radiomics features (RFs) on computed tomography (CT) images of the lumbar spine in subjects with or without fragility vertebral fractures (VFs).

Methods

Two-hundred-forty consecutive individuals (mean age 60.4 ± 15.4, 130 males) were evaluated by radiomics analyses on opportunistic lumbar spine CT. VFs were diagnosed in 58 subjects by morphometric approach on CT or XR-ray spine (D4-L4) images. DXA measurement of bone mineral density (BMD) was performed on 17 subjects with VFs.

Results

Twenty RFs were used to develop the machine learning model reaching 0.839 and 0.789 of AUROC in the train and test datasets, respectively. After correction for age, VFs were significantly associated with RFs obtained from non-fractured vertebrae indicating altered trabecular microarchitecture, such as low-gray level zone emphasis (LGLZE) [odds ratio (OR) 1.675, 95% confidence interval (CI) 1.215–2.310], gray level non-uniformity (GLN) (OR 1.403, 95% CI 1.023–1.924) and neighboring gray-tone difference matrix (NGTDM) contrast (OR 0.692, 95% CI 0.493–0.971). Noteworthy, no significant differences in LGLZE (p = 0.94), GLN (p = 0.40) and NGDTM contrast (p = 0.54) were found between fractured subjects with BMD T score < − 2.5 SD and those in whom VFs developed in absence of densitometric diagnosis of osteoporosis.

Conclusions

Artificial intelligence-based analyses on spine CT images identified RFs associated with fragility VFs. Future studies are needed to test the predictive value of RFs on opportunistic CT scans in identifying subjects with primary and secondary osteoporosis at high risk of fracture.

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Funding

No funds, grants, or other support was received.

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Author notes

  1. E. Biamonte and R. Levi contributed equally to the manuscript.

Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy

    R. Levi, M. Battaglia, F. Garoli, M. Riva, A. G. Lania, L. S. Politi & G. Mazziotti

  2. Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy

    E. Biamonte, F. Carrone, W. Vena, A. Brunetti, A. G. Lania & G. Mazziotti

  3. Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy

    R. Levi, M. Battaglia, F. Garoli, G. Savini, M. Grimaldi & L. S. Politi

  4. Neurosurgery Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy

    M. Riva, A. Ortolina, M. Tomei & M. Fornari

  5. Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy

    G. Angelotti, M. E. Laino & V. Savevski

  6. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    M. Mollura & R. Barbieri

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  1. E. Biamonte
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Correspondence to L. S. Politi.

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The authors did not receive support from any organization for the submitted work. The authors have no relevant financial or non-financial interests to disclose.

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The Institutional Ethics Committee approved this retrospective study.

Research Involving Human Participants

All procedures in the present study that involved human participants were performed in accordance with the ethical standards of the Ethics Committee of the IRCCS Humanitas Clinical and Research Hospital (Milan, Italy) and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Biamonte, E., Levi, R., Carrone, F. et al. Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures. J Endocrinol Invest 45, 2007–2017 (2022). https://doi.org/10.1007/s40618-022-01837-z

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