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Updated: May 8, 2026
Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
Published on: March 29, 2019
Multi-DECT Image-based Interpretable Model Incorporating Habitat Radiomics and Vision Transformer Deep Learning for
Changyu Du1, Wei Wei1, Mengting Hu1
1Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China (C.D., W.W., M.H., J.H., J.S., Y.L.).
Rationale And Objectives:
The research aims to evaluate the effectiveness of a multi-dual-energy CT (DECT) image-based interpretable model that integrates habitat radiomics with a 3D Vision Transformer (ViT) deep learning (DL) for preoperatively predicting muscle invasion in bladder cancer (BCa).
Materials And Methods:
This retrospective study analyzed 200 BCa patients, who were divided into a training cohort (n=140) and a test cohort (n=60) in a 7:3 ratio. Univariate and multivariate analyses were performed on the DECT quantitative parameters to identify independent predictors, which were subsequently used to develop a DECT model. The K-means algorithm was employed to generate habitat sub-regions of BCa. Traditional radiomics (Rad) model, habitat model, ResNet 18 model, ViT model, and fusion models were constructed from the 40, 70, and 100 keV virtual monochromatic images (VMIs) in DECT. The evaluation of all models used the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). The SHAP method was employed to interpret the optimal model and visualize its decision-making process.
Results:
The Habitat-ViT model demonstrated superior performance compared to other single models, achieving an AUC of 0.997 (95% CI 0.992, 1.000) in the training cohort and 0.892 (95% CI 0.814, 0.971) in the test cohort. The incorporation of DECT quantitative parameters did not improve the performance. DCA and calibration curve assessments indicated that the Habitat-ViT model provided a favorable net benefit and demonstrated strong calibration. Furthermore, SHAP clarified the decision-making processes underlying the model's predicted outcomes.
Conclusion:
Multi-DECT image-based interpretable model that integrates habitat radiomics with a ViT DL holds promise for predicting muscle invasion status in BCa, providing valuable insights for personalized treatment planning and prognostic assessment.
