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Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features

Published:January 06, 2022DOI:https://doi.org/10.1016/j.acra.2021.12.008

      Rationale and Objectives

      To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT.

      Materials and Methods

      This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort.

      Results

      A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts.

      Conclusion

      Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.

      Key Words

      Abbreviations:

      : AUC (area under the receiver operating characteristic curve), ROI (region of interest), LASSO (least absolute shrinkage and selection operator), PPV (positive predictive value), NPV (negative predictive value), ICC (intraclass correlation coefficients), OR (odds ratio)
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