Identification of high-risk hepatoblastoma in the CHIC risk stratification system based on enhanced CT radiomics features.

Abstract

Background: Survival of patients with high-risk hepatoblastoma remains low, and early identification of high-risk hepatoblastoma is critical.

Objective: To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting high-risk hepatoblastoma.

Methods: Clinical and CECT imaging data were retrospectively collected from 162 children who were treated at our hospital and pathologically diagnosed with hepatoblastoma. Patients were categorized into high-risk and non-high-risk groups according to the Children's Hepatic Tumors International Collaboration - Hepatoblastoma Study (CHIC-HS). Subsequently, these cases were randomized into training and test groups in a ratio of 7:3. The region of interest (ROI) was first outlined in the pre-treatment venous images, and subsequently the best features were extracted and filtered, and the radiomics model was built by three machine learning methods: namely, Bagging Decision Tree (BDT), Logistic Regression (LR), and Stochastic Gradient Descent (SGD). The AUC, 95 % CI, and accuracy of the model were calculated, and the model performance was evaluated by the DeLong test.

Results: The AUCs of the Bagging decision tree model were 0.966 (95 % CI: 0.938-0.994) and 0.875 (95 % CI: 0.77-0.98) for the training and test sets, respectively, with accuracies of 0.841 and 0.816,respectively. The logistic regression model has AUCs of 0.901 (95 % CI: 0.839-0.963) and 0.845 (95 % CI: 0.721-0.968) for the training and test sets, with accuracies of 0.788 and 0.735, respectively. The stochastic gradient descent model has AUCs of 0.788 (95 % CI: 0.712 -0.863) and 0.742 (95 % CI: 0.627-0.857) with accuracies of 0.735 and 0.653, respectively.

Conclusions: CECT-based imaging histology identifies high-risk hepatoblastomas and may provide additional imaging biomarkers for identifying high-risk hepatoblastomas.