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Purpose <p>Upper tract urothelial carcinoma (UTUC) is a rare but aggressive malignancy where accurate preoperative assessment of tumour grade and stage is essential to guide treatment. Conventional tools such as ureteroscopic biopsy have limited accuracy and procedural risks. This systematic review evaluated the diagnostic and prognostic performance of radiomics models for predicting grade, stage, histotype, muscle invasion, and outcomes in UTUC.</p> Methods <p>Following PRISMA guidelines (PROSPERO Registration ID: CRD420251141966), PubMed, EMBASE, CENTRAL, and grey literature were searched up to 6 September 2025. Eligible studies included radiomics-based analyses in adults with UTUC. Extracted performance metrics (AUC, sensitivity, specificity) were summarised as medians with ranges.</p> Results <p>Twelve studies (<i>n</i> = 1,685) were included, encompassing 45 distinct radiomics models: 20 for grade, 11 for stage, 10 prognostic, three histotype, and one for muscle invasion. Models predicting high-grade disease demonstrated excellent performance (median AUC 0.876, range 0.675–0.961; sensitivity 0.838; specificity 0.806). Prognostic models achieved a median AUC of 0.854 (range 0.750–0.933) and sensitivity of 0.909, effectively predicting recurrence-free and overall survival. Stage prediction models yielded a median AUC of 0.771 (range 0.711–0.860), while histotype models reported a median AUC of 0.84. The single study assessing muscle invasion achieved an AUC of 0.821. Most studies used CT-based radiomics with machine-learning classifiers, though validation methods and feature selection strategies were heterogeneous.</p> Conclusion <p>Radiomics demonstrates high diagnostic and prognostic accuracy in UTUC, particularly for tumour grade and survival prediction. These findings support its potential as a non-invasive adjunct for risk stratification and surgical planning, though standardised multicentre validation is required before clinical adoption.</p> Trial registration <p>The trial was prospectively registered on PROSPERO on 07/09/2025 under ID: CRD420251141966.</p>

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Diagnostic performance of radiomics for detecting and characterising upper tract urothelial carcinoma (UTUC): a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月17日

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  • Joshua Bruinsma,
  • Ninan Tharakan,
  • Hugo C. Temperley,
  • Benjamin M. Mac Curtain,
  • Matthew Chau,
  • Haider Bangash

摘要

Purpose

Upper tract urothelial carcinoma (UTUC) is a rare but aggressive malignancy where accurate preoperative assessment of tumour grade and stage is essential to guide treatment. Conventional tools such as ureteroscopic biopsy have limited accuracy and procedural risks. This systematic review evaluated the diagnostic and prognostic performance of radiomics models for predicting grade, stage, histotype, muscle invasion, and outcomes in UTUC.

Methods

Following PRISMA guidelines (PROSPERO Registration ID: CRD420251141966), PubMed, EMBASE, CENTRAL, and grey literature were searched up to 6 September 2025. Eligible studies included radiomics-based analyses in adults with UTUC. Extracted performance metrics (AUC, sensitivity, specificity) were summarised as medians with ranges.

Results

Twelve studies (n = 1,685) were included, encompassing 45 distinct radiomics models: 20 for grade, 11 for stage, 10 prognostic, three histotype, and one for muscle invasion. Models predicting high-grade disease demonstrated excellent performance (median AUC 0.876, range 0.675–0.961; sensitivity 0.838; specificity 0.806). Prognostic models achieved a median AUC of 0.854 (range 0.750–0.933) and sensitivity of 0.909, effectively predicting recurrence-free and overall survival. Stage prediction models yielded a median AUC of 0.771 (range 0.711–0.860), while histotype models reported a median AUC of 0.84. The single study assessing muscle invasion achieved an AUC of 0.821. Most studies used CT-based radiomics with machine-learning classifiers, though validation methods and feature selection strategies were heterogeneous.

Conclusion

Radiomics demonstrates high diagnostic and prognostic accuracy in UTUC, particularly for tumour grade and survival prediction. These findings support its potential as a non-invasive adjunct for risk stratification and surgical planning, though standardised multicentre validation is required before clinical adoption.

Trial registration

The trial was prospectively registered on PROSPERO on 07/09/2025 under ID: CRD420251141966.