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Background <p>Medulloblastoma (MB), the most common malignant pediatric brain tumor, lacks prognostic tools integrating clinical, molecular, and treatment-related characteristics for individualized management.</p> Methods <p>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).</p> Results <p>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.</p> Conclusions <p>By integrating multidimensional data, our framework enables the tailored prognostication and clinical decision to meet the multidimensional requirements of research and medicine.</p>

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An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • 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

摘要

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.