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Peer-reviewedCC BYCNS neuroscience & therapeutics

Radiomics-Assisted Preoperative Subtyping of Adult-Type Diffuse Gliomas: A Retrospective Study Aligned With the WHO 2021 Classification.

  • Yingzhun Liang1,2
  • Jiawei Shi1,2
  • Chongshun Zhao1,2
  • Jiajia Liu1,2
  • Ke Li1,2
  • Jun Yang1,2
  • Peiheng Ma1,2
  • Hui Qiao1,2
  • Wei Zhang1,2
  • Xing Fan1,2
  1. 1Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  2. 2Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
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Abstract

Background

Optimal surgical management of adult-type diffuse gliomas (ADGs) varies substantially by subtype, making accurate preoperative subtyping crucial for individualized surgical planning. This study aimed to develop a radiomics-based model for preoperative subtyping of ADG in accordance with the 2021 World Health Organization (WHO) Classification of Central Nervous System Tumors.Methods

A total of 382 patients with ADGs were enrolled and randomly assigned to a training set (n = 267) and an independent test set (n = 115). Tumor masks were automatically segmented and refined by experienced neurosurgeons. Radiomics features were extracted from MRI after standardized preprocessing and dimensionality reduction. Fourteen machine learning classifiers were evaluated, and the best-performing model was optimized and validated on the independent test set. Model performance was assessed using AUC, accuracy, recall, precision, F1 score, Kappa coefficient, and Matthews correlation coefficient.Results

After LASSO (Least Absolute Shrinkage and Selection Operator) regression, 50 key features were retained. Random Forest showed the best preliminary performance and was selected for optimization. On the independent test set, the optimized model achieved a macro-average AUC of 0.9084, with favorable results across other metrics. Texture features contributed most strongly to prediction.Conclusion

This radiomics model enables robust preoperative ADG subtyping under the 2021 WHO classification and may support individualized surgical planning using routine MRI features. Owing to its retrospective single-center design, multicenter prospective external validation is required prior to widespread clinical application.

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Cite

BibTeXRIS
BibTeX
@article{liang2026radiomicsassisted,
  title = {{Radiomics-Assisted Preoperative Subtyping of Adult-Type Diffuse Gliomas: A Retrospective Study Aligned With the WHO 2021 Classification.}},
  author = {Yingzhun Liang and Jiawei Shi and Chongshun Zhao and Jiajia Liu and Ke Li and Jun Yang and Peiheng Ma and Hui Qiao and Wei Zhang and Xing Fan},
  journal = {CNS neuroscience \& therapeutics},
  year = {2026},
  month = oct,
  doi = {10.1002/cns.71225},
  url = {https://europepmc.org/article/MED/42924459},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Radiomics-Assisted Preoperative Subtyping of Adult-Type Diffuse Gliomas: A Retrospective Study Aligned With the WHO 2021 Classification.
AU  - Yingzhun Liang
AU  - Jiawei Shi
AU  - Chongshun Zhao
AU  - Jiajia Liu
AU  - Ke Li
AU  - Jun Yang
AU  - Peiheng Ma
AU  - Hui Qiao
AU  - Wei Zhang
AU  - Xing Fan
PY  - 2026
DA  - 2026/10/01
JO  - CNS neuroscience & therapeutics
DO  - 10.1002/cns.71225
UR  - https://europepmc.org/article/MED/42924459
AB  - Background  Optimal surgical management of adult-type diffuse gliomas (ADGs) varies substantially by subtype, making accurate preoperative subtyping crucial for individualized surgical planning. This study aimed to develop a radiomics-based model for preoperative subtyping of ADG in accordance with the 2021 World Health Organization (WHO) Classification of Central Nervous System Tumors.Methods  A total of 382 patients with ADGs were enrolled and randomly assigned to a training set (n = 267) and an independent test set (n = 115). Tumor masks were automatically segmented and refined by experienced neurosurgeons. Radiomics features were extracted from MRI after standardized preprocessing and dimensionality reduction. Fourteen machine learning classifiers were evaluated, and the best-performing model was optimized and validated on the independent test set. Model performance was assessed using AUC, accuracy, recall, precision, F1 score, Kappa coefficient, and Matthews correlation coefficient.Results  After LASSO (Least Absolute Shrinkage and Selection Operator) regression, 50 key features were retained. Random Forest showed the best preliminary performance and was selected for optimization. On the independent test set, the optimized model achieved a macro-average AUC of 0.9084, with favorable results across other metrics. Texture features contributed most strongly to prediction.Conclusion  This radiomics model enables robust preoperative ADG subtyping under the 2021 WHO classification and may support individualized surgical planning using routine MRI features. Owing to its retrospective single-center design, multicenter prospective external validation is required prior to widespread clinical application.
SN  - 1755-5930
SN  - 1755-5949
ER  -