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Related Experiment Video

Updated: Jul 14, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Multimodal deep learning for predicting WHO/ISUP grading in renal tumors on CT using a self-attention-based model:

Takuma Usuzaki1,2, Eriya Matsuno1, Takashi Shizukuishi1,2

  • 1Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan.

European Journal of Radiology Open
|July 12, 2026
PubMed
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Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine·2026

The variable vision transformer (vViT) model accurately predicts renal tumor World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade using multimodal data. Radiomic features were the most significant predictor for tumor grading.

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Renal tumors require accurate grading for prognosis and treatment.
  • The World Health Organization/International Society of Urological Pathology (WHO/ISUP) system is standard for renal tumor classification.
  • Integrating multimodal data offers potential for improved diagnostic accuracy.

Purpose of the Study:

  • To evaluate the variable vision transformer (vViT) model's performance in predicting WHO/ISUP grade for renal tumors.
  • To assess the contribution of clinical information, radiomic features, and CT images as model inputs.
  • To identify the most influential factor in WHO/ISUP grade prediction.

Main Methods:

  • Trained and validated the vViT model on a dataset of 111 patients (1398 images) and 15 patients (224 images).
Keywords:
Deep learningMachine learningRenal cell carcinoma (RCC)Vision transformerWorld Health Organization/International Society of Urological Pathology (WHO/ISUP)

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  • Classified renal tumors into low (WHO/ISUP grades 1-2) and high (grades 3-4) grades.
  • Utilized permutation feature importance to determine the contribution of each input modality (clinical data, radiomics, CT images).
  • Compared vViT performance against Vision Transformer (ViT), ConvNeXt, and ResNeXt models using the DeLong test.

    • Main Results:

    • The vViT model achieved an accuracy of 0.811 and an AUC-ROC of 0.856 on the test dataset.
    • vViT demonstrated superior performance compared to ViT and ResNeXt models (p < 0.05).
    • Radiomic features were identified as the most dominant predictor for WHO/ISUP grade (p < 0.05).

    Conclusions:

    • The vViT model shows comparable performance to other models in predicting WHO/ISUP pathological grade using multimodal data.
    • Radiomic analysis integrated with clinical data holds significant potential for enhancing renal tumor grading.
    • Multimodal data integration, particularly radiomics, is promising for improving renal tumor classification accuracy.