August 6, 2025Frontiers in OncologyOpen Access

A multi-data fusion deep learning model for prognostic prediction in upper tract urothelial carcinoma

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The MICC model outperformed unimodal models with prognostic accuracy AUCs of 0.918 and 0.895 in training and testing sets.
Performance metrics revealed an accuracy of 0.854 and sensitivity of 0.889 for classifying UTUC patients.
Feature importance analysis using SHAP highlighted CT imaging features as the most significant for predictions.
This integrated approach can enhance personalized risk stratification and treatment planning for UTUC patients.

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Implication

Retrospective analysis shows a multi-modal deep learning model improves prognostic prediction in upper tract urothelial carcinoma, indicating potential for better risk stratification.

Cite This Study

Sun et al. (2025) studied this question.

synapsesocial.com/papers/689522189f4f1c896c429f61 https://doi.org/10.3389/fonc.2025.1644250

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Implication

Retrospective analysis shows a multi-modal deep learning model improves prognostic prediction in upper tract urothelial carcinoma, indicating potential for better risk stratification.

A multi-data fusion deep learning model for prognostic prediction in upper tract urothelial carcinoma

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Discussion

Implication

Cite This Study

A multi-data fusion deep learning model for prognostic prediction in upper tract urothelial carcinoma

Key Points

Discussion

Implication

Cite This Study

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