Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features.

PURPOSE

To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning.

METHODS

This cohort study retrospectively included a total of 345 neuroblastoma patients who underwent testing for bone marrow metastatic status. Tumor lesions on CECT images were delineated by two radiologists, and 1409 radiomics features were extracted. Correlation analysis, Least Absolute Shrinkage and Selection Operator regression, and one-way analysis of variance were used to identify radiomics features associated with bone marrow metastasis. A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. The performance of the radiomics model was evaluated using the area under the curve (AUC), 95% confidence interval (CI), accuracy, sensitivity, and specificity.

RESULTS

The radiomics model included 16 features, with a predominant focus on texture features (12/16, 75%). In the training set, the model demonstrated an AUC of 0.891 (95% CI: 0.848-0.933), an accuracy of 0.831 (95% CI: 0.829-0.832), a sensitivity of 0.893 (95% CI: 0.840-0.946), and a specificity of 0.757 (95% CI: 0.677-0.837). In the test set, the AUC, accuracy, sensitivity, and specificity were 0.807 (95% CI: 0.720-0.893), 0.767 (95% CI: 0.764-0.770), 0.696 (95% CI: 0.576-0.817), and 0.851 (95% CI: 0.749-0.953), respectively.

CONCLUSION

Radiomics features extracted from CECT images are associated with the presence of bone marrow metastasis in neuroblastoma, providing potential new imaging biomarkers for predicting bone marrow metastasis in this disease.