Objective: To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma. Methods: Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical–radiomics model incorporated both clinical and radiomics features. Results: The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563–0.845), 0.830 (95% CI: 0.704–0.959), and 0.874 (95% CI: 0.768–0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616–0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively. Conclusion: CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.

Background: Bacterial pathogens and Mycoplasma pneumoniae are the two main pathogens that cause community-acquired pneumonia complicated with pleural effusion (PE) in children, it is important to accurately differentiate between these two types of effusions. The aim of this study was to explore the feasibility and value of a radiomics approach based on non-contrast chest computed tomography (CT) scans in the differentiation of bacterial pneumonia PE (BPPE) and Mycoplasma pneumoniae parapneumonic effusion (MPPE) in children. Methods: The clinical and CT imaging data of hospitalized children with PE detected by chest CT scans from December 2020 to December 2023 were retrospectively collected. A total of 167 cases of BPPE and 368 cases of MPPE were included, and all cases were randomly divided into a training set and a test set in the ratio of 7:3. The region of interest (ROI) was manually segmented in images of non-contrast chest CT scans to extract radiomics features. The optimal radiomics features were screened using Select K Best, max-relevance and min-redundancy (mRMR), least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) was selected to construct the radiomics model. The receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), 95% confidence interval (CI), sensitivity, specificity, and accuracy were calculated to evaluate the model performance. Results: A total of 2,264 radiomics features were extracted from each ROI, seven optimal features were finally selected. The AUC in the training set was 0.942 (95% CI: 0.917–0.967), with sensitivity, specificity, accuracy and precision of 89.9%, 82.1%, 87.4% and 91.7%, respectively. The AUC in the test set was 0.917 (95% CI: 0.868–0.965), with sensitivity, specificity, accuracy and precision of 87.4%, 80.0%, 85.1% and 90.7%, respectively. Conclusions: The model based on CT radiomics demonstrates the potential to identify BPPE and MPPE in children and provides a new direction for future research.