Multiparametric MRI radiomics nomogram improves preoperative differentiation of primary from metastatic lumbar spine tumors.

ObjectiveTo develop and externally validate a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for preoperative differentiation of primary from metastatic lumbar spine tumors.Methodology200 patients were divided into training (n=100) and independent external validation (n=100) cohorts. Radiomics features from T1WI, T2WI, FS-T2WI were filtered and reduced via LASSO to construct Radscore; a combined nomogram integrating Radscore and clinical variables was evaluated.ResultsThe combined nomogram demonstrated excellent discriminatory ability in the independent external validation cohort, with an area under the curve (AUC) of 0.921 (95% confidence interval [CI]: 0.838–0.970). Its performance was significantly superior to that of the clinical variables-only model (AUC: 0.732, P < 0.001) and the Radscore-only model (AUC: 0.880, P = 0.028), achieving a sensitivity of 85% and a specificity of 87%. Univariate and multivariate logistic regression analyses identified the Radscore, age > 60 years, and serum alkaline phosphatase (ALP) > 120 U/L as independent predictors for differentiating primary from metastatic lumbar spine tumors. The nomogram exhibited good calibration (Hosmer-Lemeshow test, P = 0.62). Decision curve analysis (DCA) confirmed its clinical utility by showing a higher net benefit across a wide range of threshold probabilities compared to default strategies.ConclusionA radiomics nomogram integrating multiparametric MRI features and key clinical factors was successfully developed and externally validated. It serves as an effective, non-invasive auxiliary tool for preoperative differentiation of primary from metastatic lumbar spine tumors, with potential for clinical translation.