Radiomics model distinguishes between acute and chronic vertebral compression fractures: Study
South Korea: Research in the journal Academic Radiology showed that a combination of radiomics with radiologic findings may enhance the performance of CT in identifying the acuity of vertebral compression fractures.
Min A Yoon and colleagues conducted the study with an objective to develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT.
For this purpose, the researchers included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. Three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists for each fractured vertebra. They built a radiomic score from 280 radiomic features extracted from non-contrast-enhanced CT images.
Avradiologic model was built based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Using an independent test cohort, the models were externally validated
A total of 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 researchers found the following:
· 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) and the test (AUC, 0.95) cohorts.
The authors concluded, combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.
Reference:
The study titled, "Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features," was published in the journal Academic Radiology.
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