Work

The predictive value of pre-treatment MRI-based radiomics and clinical characteristics for medulloblastoma recurrence in pediatric patients

Year: 2025

Type: article

Abstract: Objective: The prognosis of medulloblastoma (MB) is extremely poor. This study aimed to develop a nomogram model for predicting the recurrence of MB in children by integrating pre-treatment magnetic resonance imaging radiomics and clinical characteristics. Methods: A retrospective analysis was conducted on 95 children with MB who were pathologically diagnosed with MB and underwent radical resection surgery. On the basis of recurrence status observed within the two-year post-treatment follow-up period, patients were categorized into recurrent and non-recurrent groups. The entire cohort was subsequently randomized into a training dataset and a test dataset using a 7:3 allocation ratio. Radiomic feature extraction was carried out utilizing the Feature Explorer Pro platform, with features derived from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (T1WI_CE) sequences. The most significant features were selected using the Pearson correlation coefficient, analysis of variance (ANOVA), recursive feature elimination, and the Kruskal-Wallis test. A radiomics prediction model was developed using a support vector machine classifier. Logistic regression analysis was employed to identify the most valuable clinical characteristics, and they were used to develop a clinical model. The clinical and radiomics features were combined to develop a clinical-radiomics hybrid model, followed by establishing a nomogram. The predictive performance of each model was assessed using receiver operating characteristic curve analysis. The clinical utility of the model was evaluated via decision curve analysis (DCA) and calibration curves. Results: Two clinical characteristics and six radiomics features exhibiting the strongest associations with MB recurrence were selected to independently develop a hybrid model. The results showed that the hybrid model exhibited good predictive performance for MB recurrence in children. The AUC of the hybrid model reached 0.833 (95% confidence interval [CI], 0.730-0.937) in the training dataset and 0. 802 (95% CI, 0.635-0.970) in the test dataset, both of which exceeded the performance of the clinical model and the radiomics model. The calibration curve and DCA indicated that the nomogram possessed favorable clinical utility for predicting MB recurrence. Conclusion: The hybrid model, integrating pre-treatment MRI-based radiomics features and clinical characteristics, could effectively predict MB recurrence in pediatric patients.

Source: Frontiers in Neurology

DOI: 10.3389/fneur.2025.1624819

Authors: Huiwen Lu, Danzhu Li, Lixuan Huang, Zisan Zeng

Institution: First Affiliated Hospital of GuangXi Medical University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801