Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children.

in Children (Basel, Switzerland) by Jiashu Chen, Wei Yang, Zesheng Ying, Ping Yang, Yuting Liang, Chen Liang, Baojin Shang, Hong Zhang, Yingjie Cai, Xiaojiao Peng, Hailang Sun, Wenping Ma, Ming Ge

TLDR

  • The study developed a radiomics-based prediction model for the prognosis of children with medulloblastoma and found that combining radiomics with clinical features improved predictive value.
  • The model discriminated different risk hierarchies and had potential clinical implications for treatment decision-making.
  • Radiomics has incremental value in predicting medulloblastoma prognosis, and clinical-radiomics models have a better predictive effect than clinical models alone.

Abstract

Medulloblastoma (MB) represents the predominant intracranial neoplasm observed in pediatric populations, characterized by a five-year survival rate ranging from 60% to 80%. Anticipating the prognostic outcome of medulloblastoma in children prior to surgical intervention holds paramount significance for informing treatment modalities effectively. Radiomics has emerged as a pervasive tool in both prognostic anticipation and therapeutic management across diverse tumor spectra. This study aims to develop a radiomics-based prediction model for the prognosis of children with MB and to validate the contribution of radiomic features in predicting the prognosis of MB when combined with clinical features. Patients diagnosed with medulloblastoma at our hospital from December 2012 to March 2022 were randomly divided into a training cohort (= 40) and a test cohort (= 41). Regions of interest (ROIs) were manually drawn on T1-weighted images (T1WI) along the boundary of the tumor, and radiomic features were extracted. Radiomic features related to survival prognosis were selected and used to construct a radiomics model. The patients were classified into two different risk stratifications according to the Risk-score calculated from the radiomics model. The log-rank test was used to test the difference in survival between the two stratifications to verify the classification value of the radiomics model. Clinical features related to the prognosis were used to construct a clinical model or clinical-radiomics model together with the radiomic features. Then, the clinical model, radiomics model, and clinical-radiomics model were compared to validate the improvement of radiomics in predicting the prognosis of medulloblastoma. The performance of the three models was evaluated with the C-index and the time-dependent AUC. Overall survival (OS) was defined as the time from receiving the operation to death or last follow-up. A total of 81 children were included in this study. A total of five prognostic radiomic features were selected. The radiomics model could discriminate different risk hierarchies with good performance power in the training and testing datasets (training set= 0.0009; test set= 0.0286). Six clinical features associated with prognosis (duration of disease, risk hierarchy, dissemination, radiology, chemotherapy, and last postoperative white blood cell (WBC) level in CSF) were selected. The radiomic-clinical molecular features had better predictive value for OS (C-index = 0.860; Brier score: 0.087) than the radiomic features (C-index = 0.762; Brier score: 0.073) or clinical molecular characteristics (C-index = 0.806; Brier score: 0.092). Radiomic features based on T1-weighted imaging have predictive value for pediatric medulloblastoma. Radiomics has incremental value in predicting the prognosis of MB, and clinical-radiomics models have a better predictive effect than clinical models.

Overview

  • The study aims to develop a radiomics-based prediction model for the prognosis of children with medulloblastoma and to validate the contribution of radiomic features in predicting the prognosis of MB when combined with clinical features.
  • The study includes 81 children diagnosed with medulloblastoma from December 2012 to March 2022 and divided into a training cohort (n=40) and a test cohort (n=41).
  • The primary objective of the study is to develop a predictive model for medulloblastoma prognosis prior to surgical intervention and to evaluate the improvement of radiomics in predicting the prognosis of medulloblastoma.

Comparative Analysis & Findings

  • Five prognostic radiomic features were selected, including features related to survival prognosis, which were used to construct a radiomics model.
  • The radiomics model could discriminate different risk hierarchies with good performance power in both the training and testing datasets.
  • The radiomic-clinical model had better predictive value for overall survival (OS) compared to the radiomic and clinical models alone.

Implications and Future Directions

  • The study highlights the potential of radiomics in predicting the prognosis of medulloblastoma, which could inform treatment modalities and improve patient outcomes.
  • Future studies could investigate the application of radiomics in other pediatric cancer types and explore the potential benefits of integrating radiomics with other imaging modalities.
  • Radiomics may also be useful in identifying high-risk patients who may benefit from more aggressive treatment strategies, as well as in monitoring treatment response and detecting early relapses.
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