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

Overview

Authors: Yu Su1, Kaiwen Deng2,3, Xuan Chen4,5, Zhaoyang Feng2,3, Dongyang Wang3, Craig Daniels6,7, Hyun Yong Koh6,8, Ricardo Daniel Gonzalez6,7, Hiromichi Suzuki9, Tsubasa Miyauchi9, Fei Liu2,3, Wei Wang10, Jiankang Li5, Shuaicheng Li11, Rui Chen1, Xiaoguang Qiu2,12, Chunde Li3, Tao Jiang3,12,13,14, Michael D Taylor6,7,15,16,17,18,19, Jiao Zhang6,7, Hailong Liu2,12,13,20, Yu Tian1,21,22,23
ORCID iDs: Hyun Yong Koh, Ricardo Daniel Gonzalez, Hiromichi Suzuki, Tsubasa Miyauchi, Shuaicheng Li, Michael D Taylor, Jiao Zhang, Hailong Liu, Yu Tian
23 affiliations
  1. School of Public Health, Capital Medical University, Beijing, China
  2. Department of Radiotherapy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  4. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
  5. BGI Research, Chongqing, China
  6. Texas Children’s Cancer and Hematology Center, Texas Children’s Hospital, Houston, TX USA
  7. Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas USA
  8. Department of Pediatrics—Neurology, Baylor College of Medicine, Houston, TX USA
  9. Division of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo, Japan
  10. Laboratory of Tumor Immunology, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
  11. Computer Science Department, City University of Hong Kong, Kowloon, Hong Kong
  12. Beijing Neurosurgical Institute, Beijing, China
  13. China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  14. Department of Neurosurgery, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
  15. Department of Neurosurgery, Baylor College of Medicine, Houston, TX USA
  16. Department of Neurosurgery, Texas Children’s Hospital, Houston, TX USA
  17. The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas USA
  18. Department of Surgery, Department of Laboratory Medicine and Pathobiology, and Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
  19. The Arthur and Sonia Labatt Brain Tumour Research Centre and the Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON Canada
  20. Chinese Institute for Medical Research, Beijing, China
  21. Beijing Key Laboratory of Environment and Aging, School of Public Health, Capital Medical University, Beijing, China
  22. Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
  23. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Institutions: Capital Medical University (China); Beijing Tian Tan Hospital (China); Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); BGI Research (China); Baylor College of Medicine (United States); Texas Children's Hospital (United States); National Cancer Centre Japan (Japan); Beijing Children’s Hospital (China); City University of Hong Kong (Hong Kong SAR China); University of Toronto (Canada); Hospital for Sick Children (Canada); Dan L Duncan Comprehensive Cancer Center; Beijing Institute of Neurosurgery (China); German Cancer Research Center (Germany); Chinese Academy of Medical Sciences & Peking Union Medical College (China)
Journal: Communications medicine, volume 6, issue 1, article 134
Dates: received 14 July 2025; accepted 10 February 2026; published online 10 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s43856-026-01454-4 · PMID 41807642 · PMCID PMC12976271 · OpenAlex W7134897526
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: CNS cancer
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (82204127, 82273343); Capital Medical University; National Key Research and Development Program of China (2022ZD0210100)
Citations: cited by 1 paper (Europe PMC); 40 references in the paper
Research resources: RRID:SCR_019818

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

Code and data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s43856-026-01454-4.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 1 keyword, 3 funders, 37 references, 1 RRID.

Cite

This paper

Su, Y., Deng, K., Chen, X., Feng, Z., Wang, D., Daniels, C., Koh, H. Y., Gonzalez, R. D., Suzuki, H., Miyauchi, T., Liu, F., Wang, W., Li, J., Li, S., Chen, R., Qiu, X., Li, C., Jiang, T., Taylor, M. D., . . . Tian, Y. (2026). An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features. Communications medicine, 6(1), 134. https://doi.org/10.1038/s43856-026-01454-4

BibTeX

@article{su2026interpretable,
author = {Su, Yu and Deng, Kaiwen and Chen, Xuan and Feng, Zhaoyang and Wang, Dongyang and Daniels, Craig and Koh, Hyun Yong and Gonzalez, Ricardo Daniel and Suzuki, Hiromichi and Miyauchi, Tsubasa and Liu, Fei and Wang, Wei and Li, Jiankang and Li, Shuaicheng and Chen, Rui and Qiu, Xiaoguang and Li, Chunde and Jiang, Tao and Taylor, Michael D and Zhang, Jiao and Liu, Hailong and Tian, Yu},
title = {{An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features}},
journal = {Communications medicine},
year = {2026},
month = mar,
volume = {6},
number = {1},
pages = {134},
publisher = {Nature Publishing Group},
issn = {2730-664X},
doi = {10.1038/s43856-026-01454-4},
url = {https://doi.org/10.1038/s43856-026-01454-4},
pmid = {41807642},
pmcid = {PMC12976271}
}

RIS

TY - JOUR
AU - Su, Yu
AU - Deng, Kaiwen
AU - Chen, Xuan
AU - Feng, Zhaoyang
AU - Wang, Dongyang
AU - Daniels, Craig
AU - Koh, Hyun Yong
AU - Gonzalez, Ricardo Daniel
AU - Suzuki, Hiromichi
AU - Miyauchi, Tsubasa
AU - Liu, Fei
AU - Wang, Wei
AU - Li, Jiankang
AU - Li, Shuaicheng
AU - Chen, Rui
AU - Qiu, Xiaoguang
AU - Li, Chunde
AU - Jiang, Tao
AU - Taylor, Michael D
AU - Zhang, Jiao
AU - Liu, Hailong
AU - Tian, Yu
TI - An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features
T2 - Communications medicine
J2 - Commun Med (Lond)
PY - 2026
DA - 2026/03/10
VL - 6
IS - 1
SP - 134
SN - 2730-664X
PB - Nature Publishing Group
DO - 10.1038/s43856-026-01454-4
UR - https://doi.org/10.1038/s43856-026-01454-4
LA - en
ER -

CSL-JSON

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