Related Experiment Video
Updated: May 16, 2025
Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
Published on: April 18, 2015
Attention mechanism-based multi-parametric MRI ensemble model for predicting tumor budding grade in rectal cancer
Jianye Jia1, Yue Kang2, Jiahao Wang1
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Purpose:
To develop and validate a deep learning-based feature ensemble model using multiparametric magnetic resonance imaging (MRI) for predicting tumor budding (TB) grading in patients with rectal cancer (RC).
Methods:
A retrospective cohort of 458 patients with pathologically confirmed rectal cancer (RC) from three institutions was included. Among them, 355 patients from Center 1 were divided into two groups at a 7:3 ratio: the training cohort (n = 248) and the internal validation cohort (n = 107). An additional 103 patients from two other centers served as the external validation cohort. Deep learning models were constructed for T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) based on the CrossFormer architecture, and deep learning features were extracted. Subsequently, a feature ensemble module based on the attention mechanism of Transformer was used to capture spatial interactions between different imaging sequences, creating a multiparametric ensemble model. The predictive performance of each model was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
Results:
The deep learning model based on T2WI achieved AUC values of 0.789 (95% CI: 0.680-0.900) and 0.720 (95% CI: 0.591-0.849) in the internal and external validation cohorts, respectively. The deep learning model based on DWI had AUC values of 0.806 (95% CI: 0.705-0.908) and 0.772 (95% CI: 0.657-0.887) in the internal and external validation cohorts, respectively. The multiparametric ensemble model demonstrated superior performance, with AUC values of 0.868 (95% CI: 0.775-0.960) in the internal validation cohort and 0.839 (95% CI: 0.743-0.935) in the external validation cohort. DeLong test showed that the differences in AUC values among the models were not statistically significant in both the internal and external test sets (P > 0.05). The DCA curve demonstrated that within the 10-80% threshold range, the fusion model provided significantly higher clinical net benefit compared to other models.
Conclusion:
Compared to single-sequence deep learning models, the attention mechanism-based multiparametric MRI fusion model enables more effective individualized prediction of TB grading in RC patients. It offers valuable guidance for treatment selection and prognostic evaluation while providing imaging-based support for personalized postoperative follow-up adjustments.
