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October 23, 2025Scientific ReportsOpen Access

Integrating deep learning and radiomics for preoperative glioma grading using multi-center MRI data

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Authors

YSYin ShiHarbin Normal University JMJianguang MingHarbin Medical University HCHui ChenChangsha University

Discussion

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Implication

Retrospective multi-center analysis shows improved glioma grading accuracy using deep learning and radiomics, suggesting better preoperative risk stratification.

Key Points

The integrated model achieved superior AUC of 0.946 in distinguishing glioma grades.
Cross-validation confirmed robust performance across various MRI protocols and patient populations.
Analysis of 847 patients from 5 neurosurgical centers utilized advanced convolutional neural network techniques.
The model enhances surgical planning by accurately identifying aggressive glioma features non-invasively.

Cite This Study

Shi et al. (2025) studied this question.

synapsesocial.com/papers/68fa32a40df2e6cd2f7421a9 https://doi.org/10.1038/s41598-025-20711-5

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Implication

Retrospective multi-center analysis shows improved glioma grading accuracy using deep learning and radiomics, suggesting better preoperative risk stratification.