ArticleBMC medical imaging2026
A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeThe aim of this study is to develop a deep learning model using preoperative multimodal MR data to predict the Ki-67 expression level of glioma and externally validate the predictive performance of the model.
methodsThis study retrospectively collected the clinical and imaging data from 421 patients with grade 2-4 gliomas who underwent surgical resection or biopsy and were pathologically diagnosed in two hospitals between January 2020 and December 2024. The 421 patients were divided into a training set (N = 217), an internal validation set (N = 94), and an external validation set (N = 110). Then, the tumor margins were delineated on contrast-enhanced T1- weighted imaging (CE-T1WI) and contrast-enhanced T2-fluid-attenuated inversion recovery (CE-T2FLAIR) to obtain the three-dimensional region of interest (3D ROI) of the tumor. Three vision transformer (ViT) models based on CE-T1WI, CE-T2FLAIR, and CE-T1WI + CE-T2FLAIR (CE-T1WI_ViT, CE-T2FLAIR_ViT and Combined_ViT) were constructed respectively. The predictive performance of the models was evaluated by the area under the receiver operating characteristic curve (AUC). Finally, to further assess the predictive performance of our transformer model, we trained and tested three convolutional neural network (CNN) models (ShuffleNet, ResNet50, and DenseNet121) on the same dataset and compared our trained Combined_ViT model with these three CNN models.
resultsThe three models, including CE-T1WI_ViT, CE-T2FLAIR_ViT, and Combined_ViT, demonstrated high predictive accuracy for Ki-67 level in grade 2-4 gliomas, with AUC values of 0.859 (95% confidence interval [CI], 0.792-0.926), 0.825 (95%CI, 0.745-0.906), and 0.922 (95%CI, 0.868-0.976), respectively. Among the three models, the Combined_ViT model achieved the highest predictive accuracy. Furthermore, the predictive performance of the Combined_ViT model exceeded that of the three CNN models (ShuffleNet, ResNet50, and DenseNet121), with AUC values of 0.897 (95%CI, 0.839-0.954), 0.905 (95%CI, 0.843-0.966), and 0.913 (95%CI, 0.861-0.965) respectively.
conclusionsThe deep learning models based on ViT can effectively predict the Ki-67 expression level of glioma, and are a feasible alternative to CNN models. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.