Abstract
This study aimed to evaluate radiomic-based machine learning models for glioma grading on amide proton transfer weighted (APTw) images using explainability algorithms. A total of 102 patients who underwent preoperative MR examinations, including FLAIR, T1-weighted, T1-weighted contrast-enhanced, and APTw images, were included. Two groups of APTw images were analyzed: one corresponding to contrast-enhanced regions of gliomas and the other corresponding to both contrast-enhanced and peritumoral edematous regions of gliomas. Radiomic features were extracted from these regions. Random forest, support vector machine, naïve bayes classifier, and logistic regression models were trained to distinguish grade 4 from non-grade 4 gliomas. These models were analyzed by Shapley values, permutation importance, and the method of anchors. The results of model explainability analysis revealed that the grading performance of these models relied on radiomic features highlighting the heterogeneity of radiologic phenotypes.
Data availability
The data used in this study are available from the corresponding author on reasonable request and with permission of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.
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Acknowledgements
We gratefully thank Zhang Lan and Qin Qian for their help for the lesion delineations in this study.
Funding
This work was supported by the National Natural Science Foundation of China (Grant No.82371945).
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Xuan Gao conceived this study and participated in the literature search, study design, data analysis, data interpretation and wrote the manuscript. Jing Wang participated in data collection and provided critical revision. The authors read and approved the final manuscript.
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Gao, X., Wang, J. The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44963-x
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DOI: https://doi.org/10.1038/s41598-026-44963-x
