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npj Digit. Med., Published: 05 March 2025 | https://doi.org/10.1038/s41746-025-01530-4
Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas
Yunsu Byeon1,7, Yae Won Park2,7, Soohyun Lee1, Doohyun Park1, HyungSeob Shin1, Kyunghwa Han2, Jong Hee Chang3, Se Hoon Kim4, Seung-Koo Lee2, Sung Soo Ahn2 & Dosik Hwang1,2,5,6
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
2Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
3Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea.
4Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea.
5Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea.
6Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
7These authors contributed equally: Yunsu Byeon, Yae Won Park.
Corresponding authors
Correspondence to Sung Soo Ahn or Dosik Hwang.
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
2Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
3Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea.
4Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea.
5Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea.
6Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
7These authors contributed equally: Yunsu Byeon, Yae Won Park.
Corresponding authors
Correspondence to Sung Soo Ahn or Dosik Hwang.
Abstract
Molecular subtyping and grading of adult-type diffuse gliomas are essential for treatment decisions and patient prognosis. We introduce GlioMT, an interpretable multimodal transformer that integrates imaging and clinical data to predict the molecular subtype and grade of adult-type diffuse gliomas according to the 2021 WHO classification. GlioMT is trained on multiparametric MRI data from an institutional set of 1053 patients with adult-type diffuse gliomas to predict the IDH mutation status, 1p/19q codeletion status, and tumor grade. External validation on the TCGA (200 patients) and UCSF (477 patients) shows that GlioMT outperforms conventional CNNs and visual transformers, achieving AUCs of 0.915 (TCGA) and 0.981 (UCSF) for IDH mutation, 0.854 (TCGA) and 0.806 (UCSF) for 1p/19q codeletion, and 0.862 (TCGA) and 0.960 (UCSF) for grade prediction. GlioMT enhances the reliability of clinical decision-making by offering interpretability through attention maps and contributions of imaging and clinical data.
논문정보
- Research article
- 2025년 03월 (BRIC 등록일 2025-03-06)
- 국내 연구진
- 바이오·의료융합 > 바이오·의료융합 기타
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