Skeletal radiology · 2026
Differentiating Schmorl's nodes from osteolytic bone metastases: diagnostic performance of conventional CT features and CT-based models incorporating radiomics and CT features.
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Park Sekyoung, Pak Seongyong, Huh Jin Do
Аннотация
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.
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.