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Diagnosis of Acute Versus Chronic Thoracolumbar Vertebral Compression Fractures Using CT Radiomics Based on Machine Learning: a Preliminary Study.

The purpose of this study is to evaluate the performance of radiomic models in acute thoracolumbar vertebral compression fractures (VCFs) and their impact on radiologists. In this monocentre retrospective study, eligible for inclusion were adults who underwent emergent thoracic/lumbar CT between May...

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Published in:Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2183 - 2194
Main Authors: Zhuang, Xiangrong, Wang, Jinan, Kang, Jianghe, Lin, Ziying
Format: diagnostic images research tables/charts Journal Article
Published: Springer Nature Aug2025
Online Access:View this record in EBSCOhost
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      dt: Aug2025
      vid: 38
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-024-01359-5
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        atl: Diagnosis of Acute Versus Chronic Thoracolumbar Vertebral Compression Fractures Using CT Radiomics Based on Machine Learning: a Preliminary Study.
      aug:
        au:
          Zhuang, Xiangrong
          Wang, Jinan
          Kang, Jianghe
          Lin, Ziying
        affil: https://ror.org/00mcjh785 Department of Radiology, Zhongshan Hospital, School of Medicine, Xiamen University, No.201-209 Hubinnan Road, Siming District, 361004, Xiamen, Fujian Province, China
      sug:
        subj:
          Fractures, Compression Diagnosis
          Thoracic Vertebrae
          Lumbar Vertebrae
          Radiomics Evaluation
          Tomography, X-Ray Computed Methods
          Machine Learning
          Radiologists Psychosocial Factors
          Acute Disease
          Chronic Disease
          Human
          Male
          Middle Age
          Aged
          Aged, 80 and Over
          Retrospective Design
          Diagnostic Imaging
          Magnetic Resonance Imaging
          ROC Curve
          Random Sample
          Calibration
          Descriptive Statistics
          Confidence Intervals
          Decision Making
          Spinal Fractures
          Electronic Health Records
          Work Experiences
          Image Enhancement
          Mann-Whitney U Test
          Chi Square Test
          Univariate Statistics
          Correlation Coefficient
          Data Analysis Software
          Middle Aged: 45-64 years
          Aged: 65+ years
          Aged, 80 & over
          Male
      ab: The purpose of this study is to evaluate the performance of radiomic models in acute thoracolumbar vertebral compression fractures (VCFs) and their impact on radiologists. In this monocentre retrospective study, eligible for inclusion were adults who underwent emergent thoracic/lumbar CT between May 2022 and November 2023 in our hospital diagnosed with thoracolumbar VCFs. The lesions were randomly divided at a ratio of 7:3 into a training set and test set. For external validation, consecutive patients who underwent emergent thoracic/lumbar CT between January 2022 and April 2022 were included. MRI and previous imaging were used as reference standard. The vertebral body area was manually segmented. Logistic regression was used to construct a CT radiomic model and a combined model, including Relief-selected radiomic features and clinical information. The radiologists' diagnosis with and without the models was recorded. The performance was assessed using receiver operating characteristic curves (ROC), calibration curves (CC) and decision curve analysis (DCA). Of 235 VCFs in 147 patients (median age, 73 years, 66 male) included, the diagnosis of acute VCFs was confirmed in 126. The area under the ROC of the CT radiomics model and the combined model in the external validation set were 0.883 (95% CI 0.777, 0.998) and 0.875 (95% CI 0.768, 0.982), respectively. CC and DCA showed good clinical application of the models. The less experienced reader achieved a higher accuracy with the help of the models (p = 0.027). The radiomic models showed high accuracy for diagnosing acute VCFs and helped radiologists improve the accuracy of diagnosis.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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