Predicting isocitrate dehydrogenase status among adult patients with diffuse glioma using patient characteristics, radiomic features, and magnetic resonance imaging: Multi-modal analysis by variable vision transformer

To evaluate the performance of the multimodal model, termed variable Vision Transformer (vViT), in the task of predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma. vViT was designed to predict IDH status using patient characteristics (sex and age), radiomic feat...

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Bibliographic Details
Published in:Magnetic resonance imaging 2024-09, Vol.111, p.266-276
Main Authors: Usuzaki, Takuma, Inamori, Ryusei, Shizukuishi, Takashi, Morishita, Yohei, Takagi, Hidenobu, Ishikuro, Mami, Obara, Taku, Takase, Kei
Format: Article
Language:English
Subjects:
Artificial intelligence
Brain tumor
Deep learning
Genetic mutation
Neural networks
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Summary:To evaluate the performance of the multimodal model, termed variable Vision Transformer (vViT), in the task of predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma. vViT was designed to predict IDH status using patient characteristics (sex and age), radiomic features, and contrast-enhanced T1-weighted images (CE-T1WI). Radiomic features were extracted from each enhancing tumor (ET), necrotic tumor core (NCR), and peritumoral edematous/infiltrated tissue (ED). CE-T1WI were split into four images and input to vViT. In the training, internal test, and external test, 271 patients with 1070 images (535 IDH wildtype, 535 IDH mutant), 35 patients with 194 images (97 IDH wildtype, 97 IDH mutant), and 291 patients with 872 images (436 IDH wildtype, 436 IDH mutant) were analyzed, respectively. Metrics including accuracy and AUC-ROC were calculated for the internal and external test datasets. Permutation importance analysis combined with the Mann–Whitney U test was performed to compare inputs. For the internal test dataset, vViT correctly predicted IDH status for all patients. For the external test dataset, an accuracy of 0.935 (95% confidence interval; 0.913–0.945) and AUC-ROC of 0.887 (0.798–0.956) were obtained. For both internal and external test datasets, CE-T1WI ET radiomic features and patient characteristics had higher importance than other inputs (p 
ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2024.05.012