Magnetic resonance imaging-based deep-learning radiomics score for survival prediction and risk stratification in pediatric hepatoblastoma receiving surgical resection

BACKGROUND: Children with hepatoblastoma (HB) remain high heterogeneity with distinct survival outcomes among individuals after surgical resection. Therefore, it's essential to identify high-risk patients with poor outcomes before surgery in order to add appropriate neoadjuvant chemotherapy for improving prognosis. AIM: To evaluate the performance of a deep learning (DL)-based radiomics (DLBR) score at predicting event-free survival (EFS) in patients with HB at the early stage who underwent surgical resection. METHODS: = 32) from the other institution. The widely adopted clinicopathologic variables were collected, and the magnetic resonance imaging-derived DL-based features were extracted through automatic segmentation. We developed a DLBR score based on DL-based features and an integrated clinical-DL nomogram model, and validated them externally. RESULTS: The DLBR score was generated incorporating four DL-based features, including three TI-derived features and one T2-derived feature. The integrated clinical-DL nomogram was constructed based on the Pretreatment Extension of Disease stage, alpha-fetoprotein concentration, and the DLBR score. The integrated nomogram had relatively better prognostic and calibration abilities and less opportunity for prediction error compared with the clinicopathologic predictors alone and the DLBR score alone in both training and external validation. Additionally, the DLBR score could stratify the HB patients into two EFS-related risk subgroups accurately, and showed fine distinction abilities to identify patients with different survival outcomes within identical subgroups of clinical predictors. CONCLUSION: The DLBR score acted as a noninvasive and reliable tool for predicting EFS in early-stage HB patients receiving survival resection, and might instruct therapeutic plans for improving prognosis.