Clinical Report: Utilizing Radiomics and Deep Learning for Enhanced Preoperative Risk Assessment in Upper Tract Urothelial Carcinoma

Overview

This narrative review discusses the role of radiomics and deep learning in preoperative risk assessment for upper tract urothelial carcinoma (UTUC).

Background

Upper tract urothelial carcinoma (UTUC) is a rare but aggressive malignancy, accounting for 5-10% of all urothelial carcinoma cases. Accurate preoperative assessment is crucial for optimizing treatment strategies, as many patients present with high-grade or muscle-invasive disease. Traditional diagnostic methods have limitations.

Data Highlights

No numerical data or trial data presented in the article.

Key Findings

• Radiomics and deep learning models have been reported to predict pathological grade and differentiate UTUC from renal cell carcinoma.
• These technologies may assist in assessing muscle invasion and stratifying survival or recurrence risk.
• Most studies reviewed are retrospective, single-center, and limited by small sample sizes.
• Challenges include heterogeneous imaging protocols and inconsistent segmentation methods.
• External validation and evidence of clinical utility are needed before routine implementation.

Clinical Implications

Further research is necessary to standardize methodologies and validate these approaches in larger, multicenter studies.

Conclusion

Further studies are required to establish the clinical utility of radiomics and deep learning in risk stratification for UTUC.

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9. Diagnostic performance of radiomics for detecting and characterising upper tract urothelial carcinoma (UTUC): a systematic review | World Journal of Urology | Springer Nature Link