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Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT

  • Musculoskeletal
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Abstract

Objectives

To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT.

Methods

One hundred sixty-five patients with vertebral compression fractures were allocated to training (n = 110 [62 acute benign and 48 malignant fractures]) and validation (n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features (n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set.

Results

The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort.

Conclusions

The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability.

Key Points

• A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features.

• The model showed good calibration and discrimination in both training and validation cohorts.

• The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.

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Abbreviations

CT:

Computed tomography

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

ROI:

Region of interest

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Acknowledgements

The authors thank Sehee Kim, Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, for her advice on statistical analysis.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1G1A1097626).

Author information

Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea

    Choong Guen Chee, Min A Yoon, Kyung Won Kim, Su Jung Ham, Young Chul Cho & Hye Won Chung

  2. Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea

    Yusun Ko

  3. Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea

    Bumwoo Park

Authors
  1. Choong Guen Chee
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  2. Min A Yoon
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Corresponding author

Correspondence to Min A Yoon.

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Guarantor

The scientific guarantor of this publication is Min A Yoon.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Sehee Kim, Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Approving body: Asan Medical Center Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

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• performed at one institution

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Supplementary information

ESM 1.

Supplementary Data S1. CT examination protocol. Supplementary Data S2. List of the 144 radiomics features extracted from each vertebra. Supplementary Data S3. List of the 26 radiomics features selected by the concordance correlation coefficient screening (DOCX 27 kb)

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Chee, C.G., Yoon, M., Kim, K.W. et al. Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. Eur Radiol 31, 6825–6834 (2021). https://doi.org/10.1007/s00330-021-07832-x

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