Application Value of a nomogram integrating contrast-enhanced CT radiomics and clinical indicators in evaluating lymph node metastasis in pediatric peripheral neuroblastoma.
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OBJECTIVE: To construct a machine learning model using contrast-enhanced CT radiomics and clinical indicators, aiming to improve the diagnostic accuracy for lymph node metastasis in children with peripheral neuroblastoma and provide practical evidence for clinical diagnosis and treatment. METHODS: A total of children with pathologically confirmed neuroblastoma were retrospectively enrolled between February 2014 and December 2024, and then randomly divided into a training set and a test set via random sampling. Radiomics features were extracted separately from CT images of the arterial phase and venous phase. In the training set, four radiomics models, one clinical model, and one combined model incorporating radiomics and clinical features were constructed respectively using the filtered radiomics features and clinical features. All models were validated against the pathological reference standard in the test set, and the area under the receiver operating characteristic curve of each model was calculated. The clinical utility of each model was evaluated using the decision curve analysis curve. The optimal model was visualized with a nomogram, and the diagnostic gain of the nomogram for evaluating lymph node metastasis in neuroblastoma children was quantified via human-machine comparison. RESULTS: A total of 225 children with neuroblastoma were enrolled in this study, (with a mean age of 2.23 ± 2.34 years and an age range of 0-13 years). All subjects were randomly divided into a training set (n = 157) and a test set (n = 68) at a ratio of 7:3. Compared with four radiomics models (Arterial phase, Venous phase, Delta-Absolute, Delta-Relative) and one clinical model (Ki-67), the nomogram integrating radiomics and clinical features (Arterial phase + Delta-Relative + Ki-67) exhibited superior diagnostic performance in evaluating lymph node metastasis in pediatric peripheral neuroblastoma. The AUC values of the nomogram reached 0.937 and 0.829 in the training set and validation set, respectively. In the human-machine comparison experiment, the diagnostic accuracy of radiologists for lymph node metastasis in neuroblastoma children was improved by 21% when assisted by the nomogram. CONCLUSION: The nomogram combining contrast-enhanced CT radiomics and clinical indicators has significant diagnostic value in evaluating lymph node metastasis in pediatric patients with peripheral neuroblastoma. Moreover, it can substantially improve the diagnostic accuracy of radiologists with different levels of clinical experience.