An Interpretable Machine Learning Model for Predicting Prognosis of Medulloblastoma Integrating Genetic and Clinical Features

Authors

• Yu Su
• Kaiwen Deng
• Xuan Chen
• Zhaoyang Feng
• Dongyang Wang
• Craig Daniels
• Hyun Yong Koh
• Ricardo Daniel Gonzalez
• Hiromichi Suzuki
• Tsubasa Miyauchi
• Fei Liu
• Wei Wang
• Jiankang Li
• Shuaicheng Li
• Rui Chen
• Xiaoguang Qiu
• Chunde Li
• Tao Jiang
• Michael D Taylor
• Jiao Zhang
• Hailong Liu
• Yu Tian

Language

English

Publication Date

3-10-2026

Journal

Communications Medicine

DOI

10.1038/s43856-026-01454-4

PMID

41807642

PMCID

PMC12976271

PubMedCentral® Posted Date

3-10-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Medulloblastoma (MB), the most common malignant pediatric brain tumor, lacks prognostic tools integrating clinical, molecular, and treatment-related characteristics for individualized management.

Methods: We developed machine learning models using multicenter data from 729 Chinese patients (2001-2023), of whom 509 were assigned to the training set and 220 to the testing set, and further validated the models on 201 patients from international MB consortia. To accommodate patients and researchers with varying datatypes, four application scenarios were established, including clinical-molecular-radiotherapy (CMR), clinical-molecular (CM), clinical-radiotherapy (CR), and clinical-only (CO).

Results: We construct four model scenarios and assess their predictive performance in the testing set: an XGBoost-based CMR model (incorporating 11 features, including molecular subgroup, radiotherapy dose, and key gene expression) with a C-index of 0.612; an XGBoost-based CM (C-index = 0.609); a GBM-based CR (C-index = 0.637); and a GBM-based CO (C-index = 0.635). External validation demonstrates robust performance, with radiotherapy and molecular data contributing significantly to enhanced efficacy. In addition, interactive web-based Shiny applications have been launched to facilitate dynamic risk assessment and treatment optimization.

Conclusions: By integrating multidimensional data, our framework enables the tailored prognostication and clinical decision to meet the multidimensional requirements of research and medicine.

Keywords

CNS cancer, CNS cancer

Published Open-Access

yes

Recommended Citation

Su, Yu; Deng, Kaiwen; Chen, Xuan; et al., "An Interpretable Machine Learning Model for Predicting Prognosis of Medulloblastoma Integrating Genetic and Clinical Features" (2026). Faculty, Staff and Students Publications. 6902.
https://digitalcommons.library.tmc.edu/baylor_docs/6902