Radiomics models to predict bone marrow metastasis of neuroblastoma using CT

Background Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma. Methods We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast‐enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics‐clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots. Results The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95–0.99) on the training set and 0.90 (95% CI: 0.82–0.95) on the validation set. The radiomics‐clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89–0.96) on the training set and 0.91 (95% CI: 0.85–0.97) on the validation set. Conclusions MLP‐based radiomics and radiomics‐clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma. Information from 187 patients with neuroblastoma were retrospectively collected and randomly divided into the training and validation sets. Radiomics features were extracted from computed tomography to build noninvasive radiomics models. We attempted 13 machine learning algorithms and eventually chose three best‐performed models.