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<p>Artificial intelligence (AI) and radiomics show significant potential to augment bladder cancer (BC) MRI but face a critical translational gap. This scoping review of 79 studies maps a rapidly growing field dominated by retrospective, single-center designs lacking external validation. While diagnostic performance for staging is promising (AUC up to 0.99 in ideal settings), robust multi-center studies report more realistic AUCs of 0.85–0.95, positioning AI as a complement to VI-RADS, especially for equivocal cases. Evidence supports integration via simplified protocols (T2w-only) and workflow tools. However, systemic barriers impede progress: poor reporting transparency, a near-total absence of prospective utility trials, and a narrow focus on pre-treatment diagnosis. The current evidence suggests a high risk of translational stagnation. Realizing the transformative potential requires a fundamental reorientation toward rigorous, multi-center evaluation and transparency. Priorities must include mandatory adherence to reporting standards (TRIPOD + AI), the creation of public multi-center benchmarks, and a redesign of validation paradigms to prioritize prospective studies measuring clinical utility - such as impact on treatment decisions - over retrospective diagnostic accuracy alone. Only by prioritizing transparency, interdisciplinary collaboration, and patient-centered outcomes can AI evolve from a promising research instrument into a reliable clinical tool for BC management.</p>

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Artificial intelligence and radiomics in bladder cancer MRI: a scoping review of applications, performance, and barriers to clinical translation

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Giuseppe Salvaggio,
  • Albert Comelli,
  • Domenico Albano,
  • Massimo Galia,
  • Neeraj Lalwani

摘要

Artificial intelligence (AI) and radiomics show significant potential to augment bladder cancer (BC) MRI but face a critical translational gap. This scoping review of 79 studies maps a rapidly growing field dominated by retrospective, single-center designs lacking external validation. While diagnostic performance for staging is promising (AUC up to 0.99 in ideal settings), robust multi-center studies report more realistic AUCs of 0.85–0.95, positioning AI as a complement to VI-RADS, especially for equivocal cases. Evidence supports integration via simplified protocols (T2w-only) and workflow tools. However, systemic barriers impede progress: poor reporting transparency, a near-total absence of prospective utility trials, and a narrow focus on pre-treatment diagnosis. The current evidence suggests a high risk of translational stagnation. Realizing the transformative potential requires a fundamental reorientation toward rigorous, multi-center evaluation and transparency. Priorities must include mandatory adherence to reporting standards (TRIPOD + AI), the creation of public multi-center benchmarks, and a redesign of validation paradigms to prioritize prospective studies measuring clinical utility - such as impact on treatment decisions - over retrospective diagnostic accuracy alone. Only by prioritizing transparency, interdisciplinary collaboration, and patient-centered outcomes can AI evolve from a promising research instrument into a reliable clinical tool for BC management.