  Vision Transformer (ViT) has achieved outstanding results in computer vision.
Although there are many Transformer-based architectures derived from the
original ViT, the dimension of patches are often the same with each other. This
disadvantage leads to a limited application range in the medical field because
in the medical field, datasets whose dimension is different from each other;
e.g. medical image, patients' personal information, laboratory test and so on.
To overcome this limitation, we develop a new derived type of ViT termed
variable Vision Transformer (vViT). The aim of this study is to introduce vViT
and to apply vViT to radiomics using T1 weighted magnetic resonance image (MRI)
of glioma. In the prediction of 365 days of survival among glioma patients
using radiomics,vViT achieved 0.83, 0.82, 0.81, and 0.76 in sensitivity,
specificity, accuracy, and AUC-ROC, respectively. vViT has the potential to
handle different types of medical information at once.
