Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation

Accurate prediction of IDH mutation status in gliomas is critical for guiding diagnosis, prognosis, and treatment planning. We enrolled 2,537 preoperative MRI of glioma patients (mean age 55.91 ± 14.79, 1,063 females) from 11 different datasets, consisting of 1,382 patients (mean age 58.26 ± 14.38, 548 females) in training set, 346 patients (mean age 57.43 ± 14.04, 141 females) in internal validation set, and 809 patients (mean age 53.92 ± 14.04, 374 females) in external test set, including 242 patients from The Cancer Genome Archive (TCGA) dataset. A fully automated Res3DNet model was established for isocitrate dehydrogenase (IDH) gene prediction. Four radiologists also read images from TCGA test dataset as a comparison with the deep learning model. Our Res3DNet model achieved AUCs of 0.946 (internal validation), 0.872 (external test), and 0.912 (TCGA test), with corresponding accuracies of 0.925, 0.806, and 0.840, respectively, outperforming ResNet model, I3D model, transformer model, and four radiologists.

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Publication Details

Journal
iScience
Published
2025-12-09
DOI
https://doi.org/10.1016/j.isci.2025.114373
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation

Wujun Li, Zhiqiang Zhang, Sixuan Chen, Linqing Fu et al.
iScience
Glioma Diagnosis and Treatment
article

Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation

Wujun Li, Zhiqiang Zhang, Sixuan Chen, Linqing Fu, Yajing Zhu, Zhengyang Zhu, Huiquan Yang, Meiping Ye, Bing Zhang, Xinru Xu, Yang Song, Yijun Bai, Xin Zhang, Xin Li, Cong Long, Yuying Liu, Jianan Zhou, Weiping Li, Jiaming Lu, Shunshun Du, Yang Xu
article en

Abstract

Accurate prediction of IDH mutation status in gliomas is critical for guiding diagnosis, prognosis, and treatment planning. We enrolled 2,537 preoperative MRI of glioma patients (mean age 55.91 ± 14.79, 1,063 females) from 11 different datasets, consisting of 1,382 patients (mean age 58.26 ± 14.38, 548 females) in training set, 346 patients (mean age 57.43 ± 14.04, 141 females) in internal validation set, and 809 patients (mean age 53.92 ± 14.04, 374 females) in external test set, including 242 patients from The Cancer Genome Archive (TCGA) dataset. A fully automated Res3DNet model was established for isocitrate dehydrogenase (IDH) gene prediction. Four radiologists also read images from TCGA test dataset as a comparison with the deep learning model. Our Res3DNet model achieved AUCs of 0.946 (internal validation), 0.872 (external test), and 0.912 (TCGA test), with corresponding accuracies of 0.925, 0.806, and 0.840, respectively, outperforming ResNet model, I3D model, transformer model, and four radiologists.

iScienceVol. 29(1)
Nanjing General Hospital of Nanjing Military Command (CN), Nanjing Drum Tower Hospital (CN), Nanjing Medical University (CN), Nanjing University (CN)
Openalex Percentile: Top 40%
Glioma Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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