Radiomics-based prediction of pathological subtypes in peripheral neuroblastic tumors using ¹²³I-MIBG SPECT/CT...

The purpose of the study is to employ a radiomics approach based on ¹²³I-MIBG SPECT/CT imaging to predict pathological subtypes in peripheral neuroblastic tumors (pNTs). A retrospective and exploratory study was conducted involving 67 pediatric patients with pNTs, who were randomly divided into training and validation cohorts at a ratio of 7:3. Clinical and radiomics features were selected using univariate feature selection and recursive feature elimination methods. By integrating clinical and radiomics features, a combined model based on logistic regression and a voting classifier incorporating four algorithms were constructed to optimize prediction accuracy. A total of 1702 features were extracted from SPECT and CT features. Ultimately, six clinical and nine radiomic features were included in our analysis. The combined model integrating clinical and radiomic features achieved a macro-average area under the curve (AUC) of 0.871 and an overall accuracy of 80.4% in the training set, and a macro-average AUC of 0.836 with an overall accuracy of 81.0% in the test set. The voting classifier significantly improved performance, achieving a macro-average AUC of 0.968 with an overall accuracy of 87.0% in the training set, and achieved a macro-average AUC of 0.879 in the test set, demonstrating robust stability and high accuracy.

The study demonstrates the potential of radiomics as a non-invasive diagnostic tool for differentiating pathological subtypes of pNTs, which could significantly influence treatment planning and surgical decisions.

• Peripheral neuroblastic tumors are the most common solid tumor in childhood. • Different pathological types exhibit distinctive cytomorphology and prognosis.

•The voting classifier based on clinical and radiomics features has been described in detail. •A non-intrusive diagnostic method for discriminating pathological types of peripheral neuroblastic tumors has been established.