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Objective <p>To evaluate conventional CT features for differentiating Schmorl’s nodes from osteolytic bone metastases and to compare their diagnostic performance with that of CT-based models incorporating radiomics and CT features.</p> Materials and methods <p>In this retrospective study, 79 Schmorl’s nodes and 71 osteolytic bone metastases—well-defined, geographic, and purely osteolytic lesions located at the vertebral endplates on abdominal and chest CT images—were randomly divided into two groups: a training set (<i>n</i> = 100) and a test set (<i>n</i> = 50). Subjective image analysis was performed to identify significant discriminative CT features and to construct a CT feature-based model. Random forest models were constructed for the radiomics, CT feature–based, and combined radiomics–CT feature approaches, followed by tenfold cross-validation. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), and AUCs were compared.</p> Results <p>Multivariate analysis identified distribution (odds ratio [OR], 11.109; 95% confidence interval [CI], 2.417–51.065; <i>p</i> &lt; 0.01) and thickness (OR, 145.431; 95% CI, 14.761–1432.896; <i>p</i> &lt; 0.001) of the sclerotic margin as significant independent factors for differentiation. The CT feature-based model, combined model, and combination of significant CT features demonstrated high diagnostic performance, with AUCs of 0.912, 0.912, and 0.901, respectively (all <i>p</i> &lt; 0.001), without significant differences among them.</p> Conclusion <p>CT feature-based and combined models were effective in differentiating Schmorl’s nodes from osteolytic bone metastases on CT imaging. The combination of significant CT features—specifically, a completely sclerotic margin and a margin thickness equal to or greater than the adjacent cortex—showed comparable performance, offering a simple diagnostic and practical alternative.</p>

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Differentiating Schmorl’s nodes from osteolytic bone metastases: diagnostic performance of conventional CT features and CT-based models incorporating radiomics and CT features

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Sekyoung Park,
  • Seongyong Pak,
  • Jin Do Huh

摘要

Objective

To evaluate conventional CT features for differentiating Schmorl’s nodes from osteolytic bone metastases and to compare their diagnostic performance with that of CT-based models incorporating radiomics and CT features.

Materials and methods

In this retrospective study, 79 Schmorl’s nodes and 71 osteolytic bone metastases—well-defined, geographic, and purely osteolytic lesions located at the vertebral endplates on abdominal and chest CT images—were randomly divided into two groups: a training set (n = 100) and a test set (n = 50). Subjective image analysis was performed to identify significant discriminative CT features and to construct a CT feature-based model. Random forest models were constructed for the radiomics, CT feature–based, and combined radiomics–CT feature approaches, followed by tenfold cross-validation. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), and AUCs were compared.

Results

Multivariate analysis identified distribution (odds ratio [OR], 11.109; 95% confidence interval [CI], 2.417–51.065; p < 0.01) and thickness (OR, 145.431; 95% CI, 14.761–1432.896; p < 0.001) of the sclerotic margin as significant independent factors for differentiation. The CT feature-based model, combined model, and combination of significant CT features demonstrated high diagnostic performance, with AUCs of 0.912, 0.912, and 0.901, respectively (all p < 0.001), without significant differences among them.

Conclusion

CT feature-based and combined models were effective in differentiating Schmorl’s nodes from osteolytic bone metastases on CT imaging. The combination of significant CT features—specifically, a completely sclerotic margin and a margin thickness equal to or greater than the adjacent cortex—showed comparable performance, offering a simple diagnostic and practical alternative.