Predicting Postoperative Prognosis in Pediatric Malignant Tumor With MRI Radiomics and Deep Learning Models: A
Yao Chen1,2, Xin Hu3, Ting Fan2
1Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University.
Objective:
The aim of this study is to develop a multimodal machine learning model that integrates magnetic resonance imaging (MRI) radiomics, deep learning features, and clinical indexes to predict the 3-year postoperative disease-free survival (DFS) in pediatric patients with malignant tumors.
Methods:
A cohort of 260 pediatric patients with brain tumors who underwent R0 resection (aged ≤ 14 y) was retrospectively included in the study. Preoperative T1-enhanced MRI images and clinical data were collected. Image preprocessing involved N4 bias field correction and Z-score standardization, with tumor areas manually delineated using 3D Slicer. A total of 1130 radiomics features (Pyradiomics) and 511 deep learning features (3D ResNet-18) were extracted. Six machine learning models (eg, SVM, RF, LightGBM) were developed after dimensionality reduction through Lasso regression analysis, based on selected clinical indexes such as tumor diameter, GCS score, and nutritional status. Bayesian optimization was applied to adjust model parameters. The evaluation metrics included AUC, sensitivity, and specificity.
Results:
The fusion model (LightGBM) achieved an AUC of 0.859 and an accuracy of 85.2% in the validation set. When combined with clinical indexes, the final model's AUC improved to 0.909. Radiomics features, such as texture heterogeneity, and clinical indexes, including tumor diameter ≥ 5 cm and preoperative low albumin, significantly contributed to prognosis prediction.
Conclusion:
The multimodal model demonstrated effective prediction of the 3-year postoperative DFS in pediatric brain tumors, offering a scientific foundation for personalized treatment.
